Share Data Through the Art of Visualization Complete Course | Data Analytics By Google Coursera

Share Data Through the Art of Visualization Complete Course | Data Analytics By Google Coursera


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hey there it's great to see how far you've come in this google data analytics certificate so first off i want to say congrats on your achievement and second welcome to your next course it's all about the art of data storytelling through visualization as a data analyst you can do all the necessary work of planning collecting cleaning and analysis but you also need to show stakeholders what your data means in a compelling way using visuals we're
here to show you how that's done and with my experience as director of analytics at google i hope i'll be a pretty good guide my name's kevin and i'll be your instructor for this course this part of your training is particularly meaningful to me because i love data storytelling i can't think of anything in today's business world that's more vibrant more exciting and more rewarding with the amount of data we have around us data analytics plays a key role in pretty much every part of business
in my opinion there isn't a skill that's more important to you as an analyst than being able to effectively communicate the stories you uncover to stakeholders stakeholders usually lack the time access to data or expertise needed to find those stories by themselves that's why we'll focus on visualizing data to help you better share data stories which means we're now at the share phase of the data analysis process we'll start with the basic concepts of visualization and why visualizing your analysis is such an
important part of data analytics from there we'll discuss how to plan for and start building effective visualizations that are inclusive accessible and consider the audience first afterwards we'll explore one possible tool you can use for data visualization tableau tableau helps us create visualizations from our analysis so that we can share our findings more effectively we'll show you how data visualizations including visual dashboards can help bring your data to life we'll
also explore how you can use visualizations in your presentations and slideshows to continue telling a story with data we'll discuss the art and science behind effective presentations finally you'll learn how to anticipate and answer questions from stakeholders and respond to their feedback throughout the course i'll guide you through what i think is the most exciting part of the data analysis process by the end you'll have everything you need to plan create and present effective and
compelling data visualizations now that we've gotten to know each other a little it's time to get down to business coming up we'll talk a bit about the history of data visualization and why visualizations matter so much today we'll also discuss the methods for using imagery effectively and what you can do to make the most out of your visuals see you there data analytics is the collection and analysis
and then use of data to tell stories using charts and visualizations so that businesses can make better decisions so i've always loved numbers and have always enjoyed math and calculus and those sorts of things so data came really easy to me i was working at a consultancy before and using lots of numbers and i enjoyed it for sure but it wasn't until i worked at an advertising agency where i saw
the creative expression of of numbers and how data could drive that creativity that it really all fell into place for me and i realized that doing analytics in a marketing in an advertising setting was exactly what i wanted to do and i found that i just really enjoyed when we put those two things together i think the visuals that come out of data analysis and data analytics are really beautiful but what is even more interesting for me are the stories behind them if you look at a big block of text or a
big block of numbers those stories are in there but they really have to be found and so it takes certain skill to pull those things out and i find that skill that analysis really exciting and interesting but it ultimately then ends in a beautiful visualization which i find very gratifying you know there are great data visualization thinkers and you see these visuals all over the place you can find inspiration from you looking at news outlets today
and and seeing the visuals that they present and how they tell stories that way visualizations have become so important that you can find them everywhere and you can take great inspiration from that but i also take inspiration from unlikely sources like photography and art and others and seeing how composition is created how color is used i think that's really important and i try hard to bring those sorts of elements and those sorts of influences
into the visualizations i create so i know this course is pretty intense we are throwing a lot at you your brain is probably overloaded you're probably fried just stick with it it's all going to start to piece together and make sense the biggest thing that i think you can think of is just how important these skills really are this is the new way of doing business involving data using the analytics tools and techniques that we're talking about to make decisions so there's a great big reward waiting
for you at the end of this course i'm kevin i am director of analytics here at google welcome back future data analyst as a budding analyst you'll be exposed to a lot of data people learn and absorb data in so many different ways and one of the most effective ways that this can happen is through visualization data visualization is the graphic representation and presentation of data in reality it's just putting information
into an image to make it easier for other people to understand if you've ever looked at any kind of map whether it's paper or online then you know exactly how helpful visuals can be data visualizations are definitely having a moment right now online we are surrounded by images that show information in all kinds of ways but the history of data visualization goes back way further than the web visualizing data began long ago with maps which are the visual representation of geographic data
this map of the known world is from 1502 map makers continued to improve their visualizations as new lands were charted new data was collected about those locations and new methods for visualizing the data were created scientists and mathematicians began to truly embrace the idea of arranging data visually in the 1700s and 1800s this bar graph is from 1821 and it doesn't look too different from bar graphs that we see today but since the beginning of the digital
age of data analytics in the 1990s the scope and reach of visualizations have grown along with the data they graphically represent as we keep learning how to more efficiently communicate with visuals the quality of our insights continue to grow too today we can quantify human behavior through data and we've learned to use computers to collect analyze and visualize that data as an analyst in today's world you'll probably split your time with data visuals in two ways looking at visuals in order to understand and draw conclusions about
data or creating visuals from raw data to tell a story either way it's always good to keep in mind that data visualizations will be your key to success this is especially true once you reach the point where you're ready to present the results of your data analysis to an audience getting people to understand your vision and thought process can feel challenging but a well-made data visualization has the power to change people's minds plus it can help someone who doesn't have the same technical background or
experience as you form their own opinions so here's a quick rule for creating a visualization your audience should know exactly what they're looking at within the first five seconds of seeing it basically this means the visual should be clear and easy to follow in the five seconds after that your audience should understand the conclusion your visualization is making even if they aren't totally familiar with the research you've been doing they might not agree with your conclusion and that's okay you can always use their feedback to adjust your
visualization and go back to the data to do further analysis so now let's talk about what we have to do to create a visualization that's understandable effective and most importantly convincing let's start from the beginning data visualizations are a helpful tool for fitting a lot of information into a small space to do this you first need to structure and organize your thoughts think about your objectives and the conclusions you've reached after sorting through data then think about the patterns you've noticed in the data the things that surprised you and of
course how all of this fits together into your analysis identifying the key elements of your findings helps set the stage for how you should organize your presentation check out this data visualization made by david mccandless a well-known data journalist this graphic includes four key elements the information or data the story the goal and the visual form it's arranged in a four-part venn diagram which tells us that all four elements are needed for a successful
visualization so far you've learned a lot about the data used in visualizations that's important because it's a key building block for your visualization the story or concept adds meaning to the data and makes it interesting we'll talk more about the importance of data storytelling later but for now just remember that the story and the data combined provide an outline of what you're trying to show the goal or function makes the data both useful and usable and the visual form creates both beauty and structure
with just two elements you can create a rough sketch of a visual this could work if you're at an early stage but won't give you a complete visualization because you'd be missing other key elements even using three elements gets you closer but you're not quite finished for example if you combined information goal and visual form without any story your visual will probably look fine but it won't be interesting on their own each element has value but visualizations only become truly powerful and effective
when you combine all four elements in a way that makes sense and when you think about all these elements together you can create something meaningful for your audience at google i make sure to develop visualizations to tell stories about data that include all four of these elements and i can tell you that each element is a key to a visualization success that's why it's so important for you as the analysts to pay close attention to each element as we move forward other people might not know or
understand the exact steps you took to come to the conclusions you've made but that shouldn't stop them from understanding your reasoning basically an effective data visualization should lead viewers to reach the same conclusion you did but much more quickly because of the age we live in we're constantly being shown different ways to view and absorb information this means that you've already seen lots of visuals you can reference as you design your own visualizations you have the power to tell convincing stories that can change opinions and
shift mindsets that's pretty cool but you also have the responsibility to pay attention to the perspectives of others as you create these stories so it's important to always keep that in mind coming up we'll start drawing connections between data and images to create a strong foundation for your visual masterpieces i can't wait to get started hello again earlier we talked about why data visualizations are so important to both analysts and stakeholders
now we'll discuss the connections you can make between data and images in your visualizations visual communication of data is important to those using the data to help make decisions to better understand the connection between data and images let's talk about some examples of data visualizations and how they can communicate data effectively you've maybe come across lots of these in your daily life and we'll explore them a little bit more here a good place to start is a bar graph bar graphs use size contrast to compare
two or more values the horizontal line of a bar graph usually placed at the bottom is called the x-axis in bar graphs with vertical bars the x-axis is used to represent categories time periods or other variables the vertical line of a bar graph usually placed to the left is called the y-axis the y-axis usually has a scale of values for the variables in this example the time of day is compared to someone's level of motivation throughout the whole
work day bar graphs are a great way to clarify trends here it's clear this person's motivation is low at the beginning of the day and gets higher and higher by the end of the work day this type of visualization makes it very easy to identify patterns another example is a line graph line graphs are a type of visualization that can help your audience understand shifts or changes in your data they're usually used to track changes through a period of time but they can be paired with other
factors too in this line graph we're using two lines to compare the popularity of cats and dogs over a period of time with two different line colors we can immediately tell that dogs are more popular than cats we'll talk more about using colors and patterns to make visualizations more accessible to audiences later too even as the line moves up and down there's a general trend upwards and the line for dogs always stays higher than the line for cats now let's check out another
visualization you'll probably recognize say hello to the pie chart pie charts show how much each part of something makes up the whole this pie chart shows us all the activities that make up someone's day half of it spent working which is shown by the amount of space that the blue section takes up from a quick scan you can easily tell which activities make up a good chunk of the day in this pie chart and which ones take up less time earlier we learned how maps help
organize data geographically the great thing about maps is they can hold a lot of location-based information and they're easy for your audience to interpret this example shows survey data about people's happiness in europe the border lines are well defined and the colors added make it even easier to tell the countries apart understanding the data represented here which we'll come back to again later can happen pretty quickly so data visualization is an excellent tool for making the connection between an image
and the information it represents but it can sometimes be misleading one way visualizations can be manipulated is with scaling and proportions think of a pie chart pie charts show proportions and percentages between categories each part of the circle or pie should reflect its percentage to the whole which is equal to 100 so if you want to visualize your sales analysis to show the percentage of your company's sales that come from online transactions you could use a pie chart the size of each slice would be the
percentage of total sales that it represents so if your online sales account for sixty percent the slice would be 60 of the whole pie now here's a misleading pie chart it's supposed to show opinions about pizza toppings but each slice or segment represents more than one option and they all add up to well over 100 percent there's lots of ingredients listed below the image that are not even included in the visual data and all of the segments are the same size even though they're supposed to be
showing different values if a visualization looks confusing then it probably is confusing okay let's explore another example where the size of the graphic components comes into play this time with a bar chart in a truncated bar chart like this one the values on the y-axis don't start at zero the data points start at nine thousand one hundred and are at intervals of one hundred this makes it seem like the data let's say it's for novel clicks per day on different website links
is fairly wide ranging in this view website e seems to clearly receive way more clicks than website d which receives more clicks than website c and so on while the graph is clear and the elements are easy to understand the way the data is presented is misleading let's try to fix this by changing the graph's y-axis so that it starts at zero instead now the difference between the website clicks per day don't look nearly as drastic by making the y-axis
start at zero we're changing the visual proportions to be more accurate and more honest some platforms always start their y-axis at zero but other programs like spreadsheets might not fix the y-axis so it's important to keep this in mind when creating visualizations by following the conventions of data analysis you'll be able to avoid misleading visualizations you always want your visualizations to be clear and easy to understand but never at the expense of
communicating ideas that are true to the data so we've talked about some effective data driven visualizations like bar graphs line graphs and pie charts and when to use them on top of that we've discussed some things to avoid in your visualizations to keep them from being misleading coming up we'll check out how to make those visualizations reach your target audience see you then hey there you're back and ready to learn how to create powerful data visualizations
coming up we'll explore how to take our findings and turn them into compelling visuals earlier we discussed the relationship between data and images now we'll build on that to explore what visualizations can reveal to your audience and how to make your graphics as effective as possible one of your biggest considerations when creating a data visualization is where you'd like your audience to focus showing too much can be distracting and leave your audience confused in some cases restricting data can be a good thing on
the other hand showing too little can make your visualization unclear and less meaningful as a general rule as long as it's not misleading you should visually represent only the data that your audience needs in order to understand your findings okay now let's talk about what you can show with visualizations change over time is a big one if your analysis involves how the data has changed over a certain period could be days weeks months or years you can set your visualization to show only the time period
relevant to your objective this visualization shows the search entrance and news story topics like environment and science and social issues the viz is set up to show how the search interest changes day to day the bubbles represent the most popular topic on each day in a given part of the u.s as news stories come up the data changes to reflect the topic of those stories if we wanted the data for weekly or monthly news cycles we changed the interactive feature to
show changes by week or month another situation is when you need to show how your data is distributed a histogram resembles a bar graph but it's a chart that shows how often data values fall into certain ranges this histogram shows a lot of data and how it's distributed on a narrow range from negative one to positive one each bin or bucket as the bar is called contains a certain number of values that fall into one
small part of the range if you don't need to show that much data other histograms would be more effective like this one about the length of dinosaurs here the bends or buckets of data values are segmented so you can show each value that falls into each part of the range if your data needs to be ranked like when ordering the number of responses to survey questions you should first think about what you want to highlight in your visualization bar charts with horizontal bars
effectively show data that's ranked with bars arranged in ascending or descending order a bar chart should always be ranked by value unless there's a natural order to the data like age or time for example this simple bar chart shows metals like gold and platinum ranked by density an audience would be able to clearly see the ranking and quickly determine which metals had the highest density even if this data vis included a lot more metals
correlation charts can show relationships among data but they should be used with caution because they might lead viewers to think that the data shows causation causation or a cause effect relationship occurs when an action directly leads to an outcome correlation and causation are often mixed up because humans like to find patterns even when they don't exist so if two variables look like they're associated in some way we might assume that one's dependent on the other
that implies causation even if the variables are completely independent and if we put that data into a visualization then it would be misleading but correlation charts that do show causation can be effective for example this correlation chart has one line of data showing the average traffic for google searches on tuesdays in brazil the other lines for a specific date of search traffic june 15th so the data is automatically correlated because
both lines are representing the same basic information but the chart also shows one big difference when a football match or soccer match for americans began on june 15th the search traffic showed a significant drop this implies causation football is a very popular and important sport for brazilians and the data in this chart verifies that we've now talked about time series charts histograms ranked bar charts and correlation charts each of these charts can visualize a
different type of analysis your business objective and audience will help figure out which of these common visualizations to choose or you may want to check out some other kinds of visualizations out there there's also a glossary of visualizations that you'll be able to reference later so that wraps up our lesson on creating visualizations coming up next we'll add some more layers to your planning and execution of visuals so hang on tight hey great to see you again so far we've shown that there's lots of choices
you'll make as a data analyst when creating visualizations each of your choices should help make sure that your visuals are meaningful and effective another choice you'll need to make is whether you want your visualizations to be static or dynamic static visualizations do not change over time unless they're edited they can be useful when you want to control your data and your data story any visualization printed on paper is automatically static
charts and graphs created in spreadsheets are often static too for example the owner of this spreadsheet might have to change the data in order for the visualization to update now dynamic visualizations are interactive or change over time the interactive nature of these graphics means that users have some control over what they see this can be helpful if stakeholders want to adjust what they're able to view let's check out a visualization about happiness that we've created in tableau tableau is
a business intelligence and analytics platform that helps people see understand and make decisions with data visualizations in tableau are automatically interactive we'll go into the dashboard to see how the happiness score has changed from 2015 to 2017. we can check this out in our 12th slide yearly happiness changes on the left are the country level changes in happiness score the countries are sorted by largest
increase to largest decrease on the right there's a map with overall happiness scores the color scale moves from blue for the countries with the highest happiness score to red for those with the lowest if you look below the map you'll notice a year to view slider where people can choose which year's happiness scores to display on the map it's currently set for 2016 but if someone wants to know the scores for 2015 or 2017
they can adjust the slider they could then make note of how the color coding and score labels change from year to year other dynamic visualizations upload new data automatically these bar graphs continually update data by the minute and second other data visuals can do the same by day week or month so if you need to you can show trends in real time having an interactive visualization can be useful for both you and the audience you share it with
but it's good to remember that the more power you give the user the less control you have over the story you want the data to tell it's something to keep in mind as you learn how to create your own visualizations you want to find the right balance between interactivity and control something else to consider is a choice between using a static or dynamic visualization this will usually depend on the data you're visualizing the audience you're presenting to and how you're giving your presentation now
that we've made some decisions about what kind of data vis we want to create we can start thinking about the design which is exactly where we're going to start talking about next time see you there hello and welcome back you probably didn't think you'd be learning about art in a data analytics course but that's exactly what we're going to do both data analysts and artists use elements of art in their work we'll introduce those elements to you here and we'll show you how to apply them to visualizations later
the elements we'll check out are line shape color space and movement now these aren't the only elements to consider but these particular ones can add value to your data vis by making them more visually effective and compelling lines and visualizations can be curved or straight thick or thin vertical horizontal or diagonal they can add visual form to your data and help build the structure for your visualization these charts
show some of the variety that lines can bring to your data vis the combo chart shows two different types of lines both providing a graphic for the data the line chart does the same but uses curved lines instead shapes are also known for their variety shapes and visualizations should always be two-dimensional this is because three-dimensional objects in a visualization can complicate the visual and confuse the audience shapes are also a great way to add eye-catching contrast
especially size contrast to your data story this circle used for a pie chart lets someone quickly understand the data in a familiar format shapes with symmetry are usually more familiar to people so there is less work for the audience to do when viewing symmetrical data vis but the asymmetrical shapes in this map are still instantly recognizable as countries it's good to note that the data you're sharing with your audience will usually inform the types of shapes
you want to use in your database next we have colors and colors are well colors of course in the eyes of artists and analysts colors can be much more complex colors can be described by their hue intensity and value the hue of a color is basically its name red green blue and so on intensity is how bright or dull a color is and finally there's value the value is how light or dark the colors are
in a visualization in more scientific terms value indicates how much light is being reflected dark values with some black added are called shades of color like these shades of green light values with white added are called tents like these tents of blue in this map there are shades and tents of gray the value of these colors help us understand the population data in the map and varying the color's value can be a very effective way to draw our
audience's attention to specific areas space is the area between around and in the objects there should always be space and data visualizations just not too much or too little for example the space between the bars of a bar graph like this one should be smaller than the width of the bars themselves this will draw the viewer's attention to the bar and the data it represents instead of the empty space finally there's movement movement is used to create a sense of
flow or action in a visualization one of my favorite examples is the data vis the wealth and health of nations this vis showcases a correlation between the financial health and physical health of nations it traces these elements over time so you can see how the two correlated effects play out the movement pulls in data from the 1800s all the way up until recently the interactivity allows for a greater
volume of data to be displayed and will reveal multiple stories from the same data visualization remember this is something that should be used sparingly there's a fine line between attracting attention and distracting the audience a static image lets you control all elements of the story you want to tell when you start incorporating movement and interactivity the story is controlled by whoever is controlling the interactivity whether that's you or possibly your audience if you've turned control
over to them we'll discuss this delicate bounce later on in the course when you bring many of these art elements together in a visualization like this one about sea levels it can be beautiful and provoking it proves that there's a place for creative expression in data analytics coming up we'll continue exploring ways to add meaningful creative expression to your database bye for now welcome back let's jump in hopefully by now we've developed a clear picture of
dataviz we've explored everything from design principles to the types of charts you can use in your visualizations choosing the right visualization for your data findings can often come down to one question which one will make it easiest for the user to understand the point you're trying to make no matter how complex your analysis is your audience will only care about what's in front of them and how easy they can understand it as you complete your analyses
you'll have to decide which visualizations serve your needs and your audience's needs for each task for example if you want to show a comparison of the different age groups of visitors to a website a line graph with a line for each age group plus one for total users would work let's say you want to highlight the differences among the age groups to compare them more directly for that you might use a positive negative bar chart like this
touched on this before but let's make some more connections between the data you'll have after analysis and the visualizations you'll want to use for different cases we'll start with some charts worked with some of these before and we'll cover more about charts with more examples later you'll also discover that the best charts to suit your purposes might depend on the needs of your industry and company and the stakeholders who will be in your audience for comparing data over time we
showed you how line graphs can be effective like in this one bar graphs and stacked bar graphs along with area charts can also be good ways to visualize how data changes over time by the way there's a lot of charts out there we'll give you as much information as possible about as many as we can but doing your own research or practicing using them in visualizations will also be helpful okay when you're comparing distinct objects like in our example about
mobile versus computer usage ordered bar and grouped bar graphs and ordered column charts are useful then there's charts that show parts of a whole this is known as data composition and is achieved by combining the individual parts of a visualization and displaying them together as a whole stacked bars donuts stacked areas pie charts and tree maps can do all this now to show relationships in your data you might want to use
scatter plot and bubble charts column line charts and heat maps let's revisit the happiness data vis to show you an example of this each of these scatter plots show the relationship between a country's happiness score and one of the factors that contributes to that score so the health versus happiness scatter plot shows a strong relationship between the life expectancy of people living in a country and how happy those people are basically as life expectancy increases so does their
happiness score speaking of happiness a successful data visualization results in a happy audience so it's important to understand how your audience is viewing your data visualizations since they should always be top of mind and it all starts in the brain when processing information our brains try to find patterns and rely on visual context as data analysts we can use our understanding of the human visual system to produce better visuals when we create
visualizations we can do so in a way that helps the audience process the information and helps them remember what they're seeing visual journalist donna wong proposes that effective visuals like the data vids we've been discussing here have three essential elements the first is clear meaning good visualizations clearly communicate their intended insight the second is a sophisticated use of contrast which helps separate the most important data from the rest
using visual context that our brains naturally look for the third essential element for effective visuals is refined execution visuals with refined execution include deep attention to detail using visual elements like lines shapes colors value space and movement in other words the elements of art that we talked about earlier the first rule in most businesses is to satisfy the customer it's no different with data analytics while your customers will probably be managers and other
stakeholders you should always think of them first when creating data visualizations think about the 5 second rule we called out earlier if you make your data vis easy to look at and understand quickly then you have done your job and then you'll be satisfied just like your customers coming up we'll talk about design thinking and data visualizations see you soon hey welcome back we've covered a lot of ground in our exploration of data visualizations we've talked a lot about how your
audience should be the focus when you are making decisions about charts colors space labels and everything else that goes into a dataviz now let's talk about design thinking design thinking is a process used to solve complex problems in a user-centric way when you bring design thinking into your work you're trying to identify alternative strategies for your visualizations that might not be clear right away you have to challenge your own thinking and explore different ways of
approaching the problems and finding solutions airbnb is one example of a company that used a design thinking approach to help their business grow when the company a vacation rental online marketplace wasn't generating as much revenue as they wanted they decided to start experimenting even though the data they collected and analyzed was valuable they needed to look at their product through the eyes of the customer they realized the photos of the places that customers were seeing
just weren't very good so they decided to help their customers replace the not so great photos with more professional looking ones so they hired a photographer and went door to door to take professional photos of their new york city listings in a week the listings with these photos saw two to three times more bookings and their revenue nearly doubled thanks to their new design thinking user-based mindset if design thinking can work for companies like airbnb it can help data analysts too and data
visualization is the perfect stage of your analysis to apply a user-based mindset if you use design thinking when planning and creating your data biz you'll be making decisions based on the needs of the people who will be viewing them this way your audience will be engaged and enlightened by how you visualize your findings while the design thinking process comes in lots of different forms they all have stages or phases we'll talk about five phases that you can use when creating data visualizations
empathize define ideate prototype and test and the spirit of design thinking these phases don't have to follow a set order instead think of them as an overview of actions that can help you produce a user-centered design in your visualizations in the empathize phase you think about the emotions and needs of the target audience of your database whether it's stakeholders team members or the general public here you should avoid areas where people
might face obstacles interacting with your visualizations for example let's say you've been working on an analysis for a pharmaceutical company about how patients have been responding to a new treatment you're getting ready to visualize the data so you should think about the audience which will include stakeholders like pharmacists doctors and other medical professionals maybe you're thinking of using a color scheme that you like but you realize that these colors might be a challenge to some people the colors might be too bright or
dramatic which might not be right for the seriousness of the data or the colors might not have enough contrast for people who have color vision deficiencies by adjusting the colors you'll be empathizing with the needs of your audience if there's someone on your team who is vision impaired you'll want to find a way to explain the data verbally as well the define phase helps you define your audience's needs their problems and your insights this goes hand in hand with the empathize phase as you'll use what you
learned in that phase to help you spell out exactly what your audience needs from your visualization you could use this phase to think about which data to show in your visualization maybe this data vis will also be presented to patients who are part of your company's study while you'll need to meet your objectives there might be data that could make these people uncomfortable you can think of ways to position that data to make it more digestible or if you're presenting to different audiences you can adjust your
visualizations to meet each group's needs by seeking input from members of the group or colleagues who've worked with that group before in the id8 phase you start to generate your data vis ideas you'll use all of your findings from the empathize and define phases to brainstorm potential dataviz solutions this might involve creating drafts of your visualization with different color combinations or maybe experimenting with different shapes creating as many examples as possible
will help you refine your ideas the key here is to always remember your audience when coming up with ideas and strategies you want to think about how you can position your visualizations to meet the needs and expectations of your audience the final two phases are prototype and test here you'll start putting your charts dashboards or other visualizations together if you've kept your audience in mind through all the phases to this point then your data vis will be informative and approachable you might want to create
lots of visualizations to choose which one best meets your objective you can test your visualizations by showing them to team members before presenting them to stakeholders if you've created more than one for the same data or for different audiences like the medical professionals and the patients for our earlier example you can share all of your options as always listen to any feedback you get critiques both your own and others are key to the design thinking process
they help you keep your focus on the audience by integrating new ideas in your final product the phrase thinking outside the box is used a lot but it definitely applies here the box in this case is your own usual way of approaching data and its visualization if you embrace design thinking you'll be able to create super effective data vis for any audience up next we'll cover more things you need to consider within your data vis see you there
hello again so we've learned data visualizations are designed to help an audience process information quickly and memorably you might remember the five second rule we covered earlier within the first five seconds of seeing a data visualization your audience should understand exactly what you're trying to convey five seconds might seem like a flash but adding in descriptive wording can really help your audience interpret and understand the data in the right way your audience will be less likely to have questions about what you're sharing
if you add headlines subtitles and labels one of the easiest ways to highlight key data in your data vis is through headlines a headline is a line of words printed in large letters at the top of the visualization to communicate what data is being presented it's the attention grabber that makes your audience want to read more take charts for example a chart without a headline is like a report without a title you want to make it easy to understand what your chart's
about be sure to use clear concise language explaining all information as plainly as possible try to avoid using abbreviations or acronyms even if you think they're common knowledge the typography and placement of the headline is important too it's best to keep it simple make it bold or a few sizes larger than the rest of the text and place it directly above the chart aligned to the left
then explain your data vis even further with a subtitle a subtitle supports the headline by adding more context and description use a font style that matches the rest of the chart's elements and place the subtitle directly underneath the headline now let's talk about labels earlier we mentioned dona wong a visual journalist who's well known for sharing guidelines on making dataviz more effective she makes a very strong case for using labels directly on the data
instead of relying on legends this is because lots of charts use different visual properties like colors or shapes to represent different values of data a legend or key identifies the meaning of various elements in a data visualization and can be used as an alternative to labeling data directly direct labeling like this keeps your audience's attention fixed on your graphic and helps them identify data quickly while legends force the audience to do more work because the legend is positioned away
from the charts data the truth is the more support we provide our audience the less work they have to do trying to understand what the data is trying to say and the faster our story will make an impact now that we've covered how to make a data vis as effective as possible next up we'll figure out how to make it accessible to all see you in a bit hey great to have you back let's dive back in over 1 billion people in the world have a disability that's more than the populations of the united states
canada france italy japan mexico and brazil combined before you design a data vis it's important to keep that fact in mind not everyone has the same abilities and people take in information in lots of different ways you might have a viewer who's deaf or hard of hearing and relies on captions or someone who's color blind might look to specific labeling for more description we've covered a lot of ways to make a data visualization beautiful and informative and now it's time to take that knowledge
and make it accessible to everyone including those with disabilities accessibility can be defined a number of different ways right from the start there's a few ways you can incorporate accessibility in your data visualization you just have to think a little differently it helps to label data directly instead of relying exclusively on legends which require color interpretation and more effort by the viewer to understand this can also just make it a faster read for those with or without disabilities check out
this database the colors make it challenging to read and the legend is confusing now if we just remove the legend and add in data labels bam you've got a clearer presentation another way to make your visualizations more accessible is to provide text alternatives so that it can be changed into other forms people need such as large print braille or speech alternative text provides a textual alternative to non-text content it allows the content and function of
the image to be accessible to those with visual or certain cognitive disabilities here's an example that shows additional text describing the chart and speaking of text you can make data from charts and diagrams available in a text based format through an export to sheets or excel you can also make it easier for people to see and hear content by separating foreground from background using bright colors that contrast against the background can help those with poor visibility whether permanently or temporarily
clearly see the information conveyed another option is to avoid relying solely on color to convey information and instead distinguish with different textures and shapes another general rule is to avoid over complicating data visualizations overly complicated data visualizations turn off most audiences because they can't figure out where and what to focus on that's why breaking down data into simple visualizations is key
a common mistake is including too much information in a single piece or including long chunks of text or too much information in graphs and charts this can defeat the whole purpose of your visualization making it impossible to understand at first glance ultimately designing with an accessibility mindset means thinking about your audience ahead of time focusing on simple easy to understand visuals and most importantly creating alternative ways for your audience to access and
interact with your data and when you pay attention to these details we can find solutions that make data visualizations more effective for everyone so now you've completed your first close-up exploration of data visualization you've discovered the importance of creating data vis that cater to your audience while keeping focus on the objective you learn different ways to brainstorm and plan your visualizations and how to choose the best charts to meet that objective and you also learned how to incorporate
elements of science art and even philosophy into your visualizations coming up we'll check out how to take all of these learnings and apply them in tableau you'll get to see how this data visualization tool makes your data vis work more efficient and effective see you soon i'm andrew and i'm a data insights manager on the ads research and insights team what does that actually mean i help my company google
make better decisions through data and i also work with our data to tell stories for marketers you know basically data storytelling at scale accessibility should be built into everything we do accessibility is really about making sure that you are creating data visualization graphs charts tables that anyone can interact with whether they have a long term or even as a temporary form of impairment it could be auditory it could be visual
it could be sensory in some way typically the ones we talk about in data visualization have to deal with a color and contrast or maybe they can't see so there's a number of things you can do in your visualizations as you're getting you know ready to show people and bring it out into the world to make it easier for them to understand your graph uh to understand the points you're trying to make and just to make yourself more inclusive you're going to create this stuff and you're not going to be the one presenting it anymore it's going to show up in a place where you won't be the one being able to navigate the data for people and frame it for people and that's a
good thing but as it moves further away from you you also won't be there standing by it being able to explain it to people and be like hey here's the point or hey maybe you can't read or see this or maybe these colors are confusing let me make sure that you got the point clearly it's just a way of making sure that everyone in the room is able to experience the thing you work so hard on and able to take away the point in a clear way that they too can basically take action on the data that you spent all this time working with and making you know presentable for folks so all these things
they're great for accessibility and they're more inclusive but they're also just making you a better data analyst and a better storyteller because they force you to become more empathetic of your audience and who's receiving your data you're making this to move the hearts and minds of other people to convince someone else that this data is meaningful to them and they should take an action on it or they should know this and they should use this for their organization or for their lives or whatever the thing may be and so my focus on accessibility by focusing on
the audience by making it more inclusive you're making your data clearer and more impactful for everyone hello again we've already discussed how helpful data visualizations can be when we want to fit a lot of knowledge into a small space now it's time to explore a powerful tool that can help you create these visualizations and bring your data to life it's called tableau a visual analytics platform that makes it a lot easier to explore and manage data you might remember hearing a bit about tableau in some of
our earlier discussions but you're about to discover even more plus when you get comfortable with tableau you'll find it even easier to use similar tools giving you another skill that will help you stand out in the job hunt coming up we'll cover some of the features that make tableau effective for visualizations and why it's used across industries after that the fun really starts we'll jump right in and explore the tableau interface identifying and applying the various
tools it has to offer i'll show you how to add data sources control visual elements and work with a variety of features that'll make your visualization really powerful like any software platform there's some best practices to keep in mind so i'll show you some examples of the good and the bad when it comes to visualizations we'll also get creative using color vision deficiency palettes to make our visualizations more accessible and we'll show you how multiple data sources can be combined to tell a more
comprehensive story by the time we wrap up here you'll be able to publish your own visualizations on tableau i am so excited to lead you on this tableau tour it's another useful tool that you'll be able to turn to as a future data analyst so you can visualize and publish data you care about after all data has a story and this is your chance to share it with others alright let's discover what it's all about welcome back mastering online tools like tableau will make it easier for your audience to
understand difficult concepts or identify new patterns in your data need to help a news outlet showcase changing real estate prices in regional markets check want to help a non-profit use their data in better ways to streamline operations check need to explore what video game sales look like over the past few decades double check many different kinds of companies are using tableau right now to do all these things and more this means there's a good chance you'll end up using it at some point in your career
but i'm getting ahead of myself first let's talk about what tableau actually is you might remember learning that tableau is a business intelligence and analytics platform that you can use online to help people see understand and make decisions with data but it's not all business all the time take this data viz for example created by tableau enthusiast eve thomas to record bigfoot sightings across the u.s it's available on tableau public which we'll be using together in our activities in this course
tableau can help you make and easily share interactive dashboards maps and graphs with your data without any coding you can connect to data in lots of formats like excel csv and google sheets you might also find yourself working with a company that uses another option like looker or google data studio for example like tableau looker and google data studio help you take raw data and bring it to life visually but each does this in different ways for example
while tableau is offered in a variety of formats like browser and desktop looker and google data studio are completely browser-based but here's the great news once you learn the fundamentals of tableau you'll find they easily transfer to other visualization tools ready to get started using it then without further ado meet tableau up next hello and welcome to the intersection of analytics and art the place where data analysts like me go to unleash the true potential of data with meaningful visuals and a place where
future data analysts like you can go to learn about how to do this welcome to tableau one of the many visualization platforms that helps you do more with your data when you turn data into a visualization you get to watch it transform before your eyes into a meaningful story that people can connect to and care about visualizations in tableau are dynamic not static as a quick refresher dynamic visualizations are interactive or change with time the interactive nature of these graphics
means your audience has some control over what they see and you have incredible flexibility with how you can create them so let's create your own visualization using a pre-loaded table on tableau public it's important to note that there's different ways to create visualizations in tableau tableau has a few different offerings but for this course we'll be using tableau public in browser which is free one cool thing about tableau public is the public gallery with dataviz examples from across the web
for now you'll be working with one of these examples from the gallery you'll be copying over data workbooks to your own profile to start creating and publishing visualizations to get started sign in to your tableau public account you can check out an earlier reading for more details then to access the workbook open the google career certificates page on tableau public by clicking the link included in this video and the reading from earlier this opens a new tab that is still linked to your account
there are a few workbooks loaded up with different data sets that you can save to your own profile these are a great starting point for creating your own visualizations there will also be a resource following this video that goes through how to download tableau and load your own data but for now let's use this gallery as a starting point now click to view the workbook titled just the data world happiness this brings up the data table we used to help create the world
happiness data vis that's in the gallery next click the button in the top right corner at this point tableau will save a copy of this workbook to your own profile so you can create your own visualizations now that you're working in your own copy create a new worksheet so that you can build a data vids from scratch you'll click on worksheet in the top of the menu and then new worksheet to start building your data vis add country as a detail in the marks shelf
you can do this by dragging the country table over to the detail icon this sets up your viz as a world map to represent the data in the table next add happiness score to color on the marks shelf this applies a color scheme to the viz in this case shades of blue this range of colors doesn't offer a lot of contrast especially for people with color vision deficiencies so to adjust the colors click the color menu and click edit colors then
change the color change the color scheme to green blue diverging and check the box for stepped color which shows a clear difference between the highest and lowest happiness scores darker blue means a higher happiness score whereas darker green relates to a lower happiness score you can see this broken down in the scale in the top right corner so with just a couple of steps you have an interesting visualization that shows happiness data in a way that's easy to digest the countries and colors on the map are
readable and offer some great insight but let's keep going so you can explore more tableau features to refine your data vis since there are three years of data in the table we're using you can filter the data to only include 2016. using multiple years can also be useful depending on your objective regardless you have lots of options for filtering so we'll add year to the filter shelf then we'll choose to filter by year and we'll select 2016. let's focus our visualization on
one region the european region to do this move your cursor to the left part of the map to find the view toolbar use the tools in this toolbar to pan to and zoom in on the european region this takes some time and practice once you have a pretty good view of europe and its surrounding areas use the shape tool in the same toolbar to select as much of europe as you can since we're practicing make your best guess if you're not sure which countries to include if you
are working on a visualization that you were going to share with others you'd want to double check that it was accurate hover your cursor over one of the countries and it shows you data about that specific country as well as all the countries you've selected in the region then use the lasso select tool to select just a few countries like this keep only this applies another filter this time to the country you've included in your database you'll notice that the color scheme of these countries is updated
this reflects that the range of colors is now only being applied to these countries countries in this region might have been in the same part of the range when compared to the rest of the world but now they're in different parts because the data being measured is specific to this region to make your vis even better add the happiness score as a label in the map you can now see a happiness score for each country on the map this adds an extra layer of detail to
the viz to help make a connection to the actual data there's an option to change the data type of this happiness score from decimals to whole numbers but when you do this you lose the contrast that the values with the decimals offer so change it back to show the happiness score as a decimal now to make it more interactive let's add a filter with a slider this will allow your audience to filter by happiness score so they can focus on fewer countries but first
let's bring in more of the map we started with to do this you will need to remove the country filter we added earlier up in the filter shelf add happiness score to the filter shelf and choose all values then open the menu on the happiness score filter and choose show filter this adds a slider to the right of the map now try filtering to show happiness score of 6.0 or below
you can see how the filter changes which countries are now highlighted in your viz and there you have it our first visualization based on data we brought in from an external source pretty powerful right we'll save our viz so now we can admire it anytime we want to and maybe even practice using the tableau tools with it it's always important to save your work but make sure not to include any personal information in your file name all the data vis created in tableau public is visible to
well the public you can also keep your visualizations hidden by going to your profile page and checking out the eye icon in the upper right corner of the viz if the icon is selected you'll see the eye with a slash through it on your viz and the vis will remain hidden it's up to you but lots of data vids created by users like you are viewable in fact you can easily check them by searching on tableau public then you can search for any kind of
database including world happiness visualizations you'll come across all types of data vis with many advanced settings some of these examples you'll find in the gallery are stronger than others coming up we'll talk about effective data visualizations and some ways you can make your database work even stronger see you soon hey there in this video we'll take a closer look at effective and ineffective data visualizations using tableau
that's right even though this platform can help you create some really beautiful visuals all those features and functions can lead to something that's just not very useful too you might remember the five second rule we spoke about earlier a sign of a good data visualization is that once you show it to an audience they should understand what you're trying to convey within five seconds this means it's clear effective and most importantly convincing if you keep that rule of thumb in mind before you begin any tableau
is you'll be on the right path to creating good visuals let's take a look at an example of a good use of a diverging color palette a diverging color palette displays two ranges of values using color intensity to show the magnitude of the number and the actual color to show which range the number is from it's a good way to show the difference between numbers here green is associated with higher numbers and red with lower numbers you may come
across tables like this as it relates to business metrics and kpis the colors you choose should fit within the scope of the audience's expectations while this might not always be true globally a lot of people associate green with positive and red with negative this makes things nice and clear here's an ineffective dataviz example there's a lot about this viz that isn't working well but these colors are hard to read this graph uses green
and orange and the data points are really close together these colors don't clearly show the difference between low and high data points data visualization allows us to share meaningful stories about data but we can't do it if it's too hard for the audience to understand the data vids we're sharing using color pairings that don't fit your audience's expectations could add another layer of unnecessary complexity brace yourself because there's another way to make an ineffective data
visualization even worse if you add a few too many labels you end up with a data visualization that's really hard to take in doing this makes the visualization too busy it takes up too much space and it prevents the labels from being clearly shown all of this is made worse by using different fonts across the labels what we have here is good data turned bad because of the visuals having an interactive visualization can be useful for both your audience and for you as the analyst but just like
anything else the more power you have the more responsibility you have lose sight on the qualities of a good viz and you could lose control over the story you want the data to tell now that you've learned how to use visual enhancements to your advantage next up we'll check out ways you can get even more creative with them stay tuned hey good to see you again one of my favorite aspects of data visualizations is how creative it lets me be you might remember me saying earlier
that data vis is the intersection of art and analytics and it really is i'm here to help you find your inner kid with a crayon from turning messy charts into effective ones to making your visuals more accessible to people who have color vision deficiencies it's time to explore how your creativity can help you get the most out of your data jump right in by signing into your tableau account we'll return to our world happiness data in the google career certificates page
click the link included in this video to open the gallery now click to view the workbook titled just the data world happiness we use the same workbook in the earlier video about getting started with tableau let's click up here to get started let's say we want to use this data in this table to figure out what the biggest contributors to a country's happiness score are we'll start by digging into the relationship between happiness scores and the other country measures to see what we can find
to start create a new worksheet to build your own data vis from scratch you'll click on worksheet and then new worksheet since our data has three years of values let's filter to 2016. to do this add year to the filter shelf and choose 2016. add happiness score to the rose shelf then add economy gdp per capita to the column shelf
next drag and drop country to the detail section this creates a separate circle or data point for each country you might notice that where the economy score is higher the happiness score is also higher to make this trend easier to see let's add a trendline now duplicate this sheet so that all of your formatting stays the same in the duplicate sheet replace economy gdp per capita with another measure such as family drag
the family table into the column shelf to replace economy we'll rename our original tab economy and this new tab family so that we can easily recognize what they show and find them when we need to try doing this for more of the measures now let's show what multiple visualizations on a single dashboard look like so you can more easily see the relationship between them drag your visualizations which are now listed in the left tables column one at a time to populate the dashboard
they can be arranged in different ways take note of which trend lines have the steepest incline those are the ones with the strongest relationship to happiness score each chart should have an easily understood purpose that's immediately clear to your audience and that's just what this does you can also add a companion table which shows the same data in a different way in case people in your audience prefer tables in my experience some stakeholders prefer to see their data in spreadsheets we've been spending a lot of time using one data source but you'll likely end up
juggling more than one data set at a time as an analyst coming up you'll have the option to learn more about combining multiple data sources in tableau bye for now storytelling is the oldest form of teaching humans have been sharing knowledge through stories for tens of thousands of years back when tvs and computer screens were the walls in a cave in fact scientists have confirmed that cave paintings were created by early humans
who used their art to convey the stories of their imaginations today storytelling is still the most natural form of education that's because stories make learning easier by helping us process and remember information everyone tells stories even if we're just sharing how our day went with a friend many experts believe that human brains automatically organize events with a beginning a middle and an end thinking about things in this way like stories helps us make sense of the
past the present and the future on top of that stories also help us relate to other people and create important human connections it's no wonder that people are so captivated by stories over the course of history many important inventions have changed how stories are told for example the invention of the printing press led to newspapers magazines and books the invention of motion picture camera made movies possible and soon we had tv
videos on demand and streaming services that let us enjoy all kinds of stories anywhere anytime the invention of data visualization tools changed the way people tell stories once again as you've learned data visualization is the representation and presentation of data to help with understanding coming up you'll discover how to use data visualization to transform data into a meaningful story that people connect to and care about you also start working with dashboards
and dashboard filters a dashboard is a tool that organizes information typically from multiple data sets into one central location for tracking analysis and simple visualization through charts graphs and maps and just like filters in spreadsheets and queries a dashboard filter is a tool for showing only the data that meets a specific criteria while hiding the rest soon you'll know how to use these tools to tell stories that motivate and
persuade people to take action based on the data you present finally you'll understand the key attributes of data-driven stories and effective ways to communicate them in all sorts of business situations ready to become an expert storyteller then let's turn to the next chapter of your data analytics story stephen few an innovator author teacher and data visualization expert once said numbers have an important story to tell they rely on you to give them a clear and convincing
voice facts and figures are very important in the business world but they rarely make a lasting impression to create strong communications that make people think and convince them to take action you need data storytelling data storytelling is communicating the meaning of a data set with visuals and a narrative that are customized for each particular audience a narrative is another word for a story in this video you'll learn about data storytelling steps
these are engage your audience create compelling visuals and tell the story in an interesting way here's an example from the music streaming industry some companies send their customers a year in review email it highlights the songs the users have listened to most and sometimes congratulates them for being a top fan of a particular artist this is a much more exciting way to share data than just a printout of the customer's activity it also reminds the listener about how much time they spend enjoying the service
a great way to build customer loyalty here's another example some ride sharing companies are using data storytelling to show their customers how many miles they've traveled and how that equals spending less money on gas reducing carbon emissions and saving time they might otherwise have spent fighting traffic it makes it really easy for the rider to clearly see the value of the service in this simple and fun visual data stories like these keep the customer engaged and make them
feel like their choices matter because the companies are taking the time to create something just for them and importantly the stories are interesting knowing how to reach people in this way is an essential part of data storytelling images can draw us in at a subconscious level this is the concept of engaging people through data visualizations so far you've been learning about the importance of focusing on your audience coming up you'll keep building on that knowledge you'll discover that there are three
data storytelling steps and the first is knowing how to engage your audience engagement is capturing and holding someone's interest and attention when your audience is engaged you're much more likely to connect with them and convince them to see the same story you see every data story should start with audience engagement all successful storytellers consider who's listening first for instance when a kindergarten teacher is choosing books for their class they'll pick ones that are appropriate
for five-year-olds if they were to choose high school level novels the complex subject matter would probably confuse the kids and they'd get bored and tune out the second step is to create compelling visuals in other words you want to show the story of your data not just tell it visuals should take your audience on a journey of how the data changed over time or highlight the meaning behind the numbers here's an example let's say a cosmetic company keeps track
of stores that buy its product and how much they buy you could communicate the data to others in a spreadsheet like this or you could create a colorful visual such as this pie chart which makes it easy to see which stores are most and least profitable as business partners that's a much clearer and more visually interesting approach now the third and final step is to tell the story in an interesting narrative a narrative has a beginning a middle and an end it should connect the data you've
collected to the project objective and clearly explain important insights from your analysis to do this it's important that your data storytelling is organized and concise soon you'll learn how to do that using slides for a discussion during a meeting and a formal presentation we'll discuss how the content visuals and tone of your message changes depending on the way you're communicating it and speaking of business communications one of the many ways that companies use visualization to tell data stories
is with word clouds word clouds are a pretty simple visualization of data these words are presented in different sizes based on how often they appear in your data set it's a great way to get someone's attention and to unlock stories from big blocks of text where each word alone could never be seen word clouds can be used in all sorts of ways on social media they can show you which topics show up in posts most often or you can use them in blogs to highlight the ideas that interest readers the most
this word cloud was created using text from the syllabus of this course it tells a pretty engaging story where data analytics analysis sql and spreadsheets are unsurprisingly some of the lead characters all right let's continue turning the pages of your data analytics story there's lots of action and adventure to come welcome back when you want to communicate something to others a great story can help you reach people's hearts and minds and make them more open to what you have to say in other words
stories make people care as you learned before the first of the three data storytelling steps teach us that for a story to be successful you need to focus on who's listening data analysts do this by making sure that they're engaging their audience that's what we'll explore together now first you need to know your audience think back to the example of telling someone a joke they've heard many times before and expecting them to laugh at the punchline not likely to get the response you're seeking you've got to understand your audience's
point of view that means thinking about how your data project might affect them it helps to ask yourself a few questions what role does this audience play what is their stake in the project and what do they hope to get from the data insights i deliver let's say you're analyzing readership data from customers to help a magazine publisher decide if they should switch from quarterly to monthly issues if your stakeholder audience includes people from the printing
company they're going to care because the change means they have to order paper and ink more frequently they also might need to assign more staff members to the project or if your stakeholders include the magazine authors and editors you'll want to keep in mind that your recommendations might change the way they work for instance they might need to write and edit stories at a faster pace than they're used to once you've considered the answers to those questions it's time to choose your
primary message every single part of your story flows from this one key point so it's got to be clear and direct with that in mind let's think about the key message for the data project about our pretend magazine maybe the readership data from customers shows that print magazine subscriptions have been going down recently you discover in survey data that this is mainly because readers feel the information is outdated so this finding suggests that readers
would probably appreciate a publication cycle that gets the information into their hands more often but that's not all your reader survey data also shows that readers prefer shorter articles with quick takeaways the data is generating a lot of possible decision points the volume and variety of information in front of you may feel challenging so to get the key message you'll need to take a few steps back and pinpoint only the most useful pieces not every piece of data is relevant to
the questions you're trying to answer a big part of being a data analyst is knowing how to eliminate the less important details one way to do this is with something called spotlighting spotlighting is scanning through the data to quickly identify the most important insights there are many ways to spotlight but lots of data analysts like to use sticky notes on a whiteboard kind of like how archaeologists make sense of the artifacts they discover in a dig to do this you write each insight from
your analysis on a piece of paper spread them out and display them on a whiteboard then you examine it it's important not to get bogged down in every tiny detail instead look for broad universal ideas and messages try to find ideas or concepts that keep popping up again and again or numbers and words that are repeated often maybe you're finding things that look like they're connecting or forming patterns highlight these items or group them
together on your whiteboard next explore your discoveries find the meaning behind the numbers the idea is to identify which insights are most likely to help solve your business problem or give you the answers you've been seeking this is how spotlighting can lead you to your key message remember to keep your key message clear and concise as an overly long message like this one shown on screen has less chance of conveying the most important conclusion here's a clear concise message that's
likely to engage your audience because it's short and to the point of course no matter how much time and effort you put into studying your audience you can't predict exactly how they'll react to your recommendations but if you follow the steps we're discussing you'll be much more likely to have good results in an upcoming video you'll learn how to deal with situations that don't go quite according to plan and that's okay it happens to all of us [Music] my name is carolyn and i'm a measurement
lead at google and that means that i measure a client's advertising investment and figure out ways that it can perform better for them in the future over the course of my career i've worked in four very different fields but the thing that links all of them together is my ability to understand data to get the information that i need out of it and to convey it in a simple and compelling way a really early example of this is from a man named jon snow he was a doctor in london in the 1850s
and he was living during a cholera outbreak and the theory at the time was cholera was spread through the air or you know ill humors people didn't really know what was causing it but he thought that it was caused by drinking really contaminated in gross water from the thames so he went out and interviewed people who were sick and asked them where they got their water and he found out through mapping the data that they got it all at the same pump so he went to the authorities and asked them to
dismantle the pump and they took the handle off and the outbreak ended this started a really robust field of epidemiology but it also is a great example of data journalism most recently before google my job was as a data journalist and that was to use data to tell stories i worked at the chicago tribune for three years i worked a lot around the election season and olympics big graphic moments where
there's a lot of interesting data and a need to understand it the field of data journalism has changed a lot over time but not as much as you might think we have a lot more access to data and data that we've never really tracked before but it's still the job of the data journalists to really understand what that data means as we have more and more data it's not really helpful to a reader to just say here's a link to a database you still have to uncover the meaning beneath it and really understand
what's the insight within the data the tools that a journalist would use to understand something which is consulting an expert really diving into a story are very similar to the tools i need at google to really understand a media investment and make a really clear recommendation of what someone should do in the future my advice is to understand the tools that are available to you and know how they work but never to let those tools overwhelm your story
so i never want to look at a piece and know oh this was created using data studio or using microsoft excel i want to know what the data says and what a data journalist's point of view is behind that story don't let the way you create something influence what it's actually saying people just want to know what you've distilled and what new information you've found in all the hard work you've done i love the field of data journalism because it's just how my brain works i'm
always the type of person who will doodle in the margins or make a quick chart to really understand the data beneath it and that forces me to find a point of view and to share that with other people i love that through data i can communicate with a large audience and really help them understand the world around them have you ever been driving a car when one of the warning lights on the dashboard suddenly comes on maybe the gas gauge starts blinking because you're getting low on fuel it's handy when you have that alert right in front of you clearly showing
you that you need to pay attention to your gas level can you imagine if cars didn't have dashboards we'd never know if we were about to run out of gas we'd have no idea if our tire pressure was low or if it was time for an oil change without dashboards if our cars started acting differently we'd have to pull out the user manual sift through all that information inside and try to figure out the problem ourselves car dashboards make it easy for drivers to understand and respond to any issues with their
vehicles because they're constantly tracking and analyzing the car status but as you've been learning dashboards aren't just for cars companies also use them to share information get people engaged with business plans and goals and uncover potential problems and just like a cars dashboard data analytics dashboards take tons of information and bring it to life in a clear visually interesting way this is extremely important when telling a story with data
which is why it's a big part of number two in our three data storytelling steps you've learned that a dashboard is a tool that organizes information from multiple data sets into one central location for tracking analysis and simple visualization through tables charts and graphs dashboards do this by constantly monitoring live incoming data as we've been discussing you can make dashboards that are specifically designed to speak to your
stakeholders you can think about who will be looking at the data and what they need from it and how often they'll use it then you can make a dashboard with the perfect information just for them this is helpful because people can get confused and distracted when they're presented with too much data a dashboard keeps things neat and tidy and easy to understand when designing a dashboard it's best to start simple with just the most important data points and if later on you discover something's missing you can
always go back and tweak your dashboard or create a new one an important part of dashboard design is the placement or layout of your charts graphs and other visuals these elements need to be cohesive which means they're balanced and make good use of the space on the dashboard after you decide what information should be on your dashboard you might need to resize and reorganize it so it works better for your users one option in tableau is choosing between a vertical or horizontal layout
a vertical layout adjusts the height a horizontal layout resizes the width of the views and objects it contains also as you can see here evenly distributing the items within your layout helps create a clear and organized data visual you can select either tiled or floating layouts tiled items are part of a single layer grid that automatically resizes based on the overall dashboard size
floating items can be layered over other objects in this example the map and scatter plots are tiled they don't overlap this really helps make clear what the data is all about which is valuable because the majority of people in the world are visual learners they process information based on what they see that's why sharing your dashboards with stakeholders is such a valuable practice now there's something important to keep in mind about that sharing dashboards with others likely means that you'll lose
control of the narrative in other words you won't be there to tell the story of your data and share your key messages dashboards put storytelling power in the hands of the viewer that means they'll craft their own narrative and draw their own conclusions but don't let that scare you away from being collaborative and open just understand the risks that come with sharing your dashboards after all sharing information and resources means that you'll have more people working on the solution to a big problem
or coming up with that next big idea this leads to more connections which can result in really exciting new practices and innovations so far we've focused a lot on understanding our audience whether you're trying to engage people with data storytelling or creating dashboards designed for a certain person or group understanding your audience is key as you've learned you can make dashboards that are tailored to meet different stakeholder requirements
to do this it's important to think about who will be looking at the data and what they need from it in this video we'll continue exploring how to create compelling visuals to tell an interesting and persuasive data story one great tool for doing this is a filter you've learned about filters in spreadsheets and queries but as a refresher filtering means showing only the data that meets a specific criteria while hiding the rest filtering works
the same way with dashboards you can apply different filters for different users based on their needs tableau lets you limit the data you see based on the criteria you specify maybe you want to filter data in a data set to show only the last six months or maybe you want to see information from one particular customer you can even limit the number of rows or columns in a view to explore these options let's return to our world happiness example
say your stakeholders were interested in only a few of the topics that affect overall happiness filtering for just gross domestic product family generosity freedom trust and health and then creating individual scatter plots for each would make this possible you can also use filters to highlight or hide individual data points for instance if you have a scatter plot with outliers you may want to explore what your plot would look like without them
however note that this is just an example to show you how filters work it's not okay to drop a data point just because it's an outlier outliers can be important observations sometimes even the most interesting ones so be sure to put on your data detective hat and investigate that outlier before deciding to remove it from your dashboard here's how to do it to filter data points from the view we can choose a single data point or click and drag in the view to select several points let's choose just one
then on the tooltip that appears we'll select exclude to hide it or we could have chosen to do it the other way by keeping only selected data points here's another example if your data is in a table you can filter entire rows or columns from your view to do this we'll select the rows we want in the view then on the tooltip that appears we'll choose to keep only those countries again we could have also selected the data points we wanted to exclude and picked
that option instead or if you like we can even pre-filter a tableau dashboard this means that your stakeholders don't have to filter the data themselves basically by doing the filtering for them you can save them time and effort and direct them to the important data you want them to focus on personally i think the best thing about filters is they let you zero in on what's important sometimes i'm working with a huge data set and i want to concentrate only on a specific area
so i'll add a filter to limit the data displayed on my dashboard this cuts the clutter and gives me a simple clear visual i use filters a lot when working with data about advertising campaign performance filters help me isolate specific tactics such as search or youtube ads to see which ones are working best and which ones could be improved by limiting and customizing the information i'm looking at it's much easier for me to see the story behind the numbers and as i'm sure you've noticed i love a good data story
as a data analyst you'll often be relying on spreadsheets to create quick visualizations of your data to tell your story let's practice building a chart in a spreadsheet to follow along use the spreadsheet link in the previous reading also included in the video we'll be using google sheets so this might look a little different in other spreadsheet platforms like excel we'll begin by filtering just the data on how many customers purchase basic plus or premium software packages
to start select the column for the software package and insert a chart the spreadsheet suggests what it thinks is the best type of chart for our data but we can choose any type of chart you'd like spreadsheet charts also let you assign different styles access titles a legend and many other options feel free to explore the different functionality later on we'll also cover this more in reading there's lots of different options to choose from let's say we also have data on which
countries our customers are from and their overall satisfaction score for the software they purchased first highlight columns a and b then click on insert and then chart again under chart type you want to select the first map option voila now we have a map that summarizes the customer survey scores by country we can also customize this chart by clicking customize in the top right corner let's say we wanted to change our colors from red and green
to a gradient so it's more accessible we can do that by clicking geo and then change the men color to the lightest shade of blue the mid color to the middle shade of blue and the max color to the darkest shade of blue to show the spectrum of scores from low to high now we have a map chart that shows where respondents are most satisfied with their software in dark blue and least satisfied with their software in light blue and this will be easier for anyone in our audience with color
vision deficiencies to understand tableau and spreadsheets are common tools for creating data visualizations by using their built-in functionalities like filters and charts you can zero in on what information is most important and create compelling visuals for your audience and now that we've explored some ways to create visuals it's time to start preparing our data narrative coming up we're going to talk more about telling stories with data and organizing presentations i'll see you soon
businesses everywhere know the power of using data to solve problems and achieve goals but all the data in the world won't get you anywhere if your stakeholders can't understand it or if they can't stay focused on what you're telling them so you want to create presentations that are logically organized interesting and communicate your key messages clearly an effective presentation supports your narrative by making it more interesting than words alone it starts with how you want to organize your data insights
the narrative you share with your stakeholders needs characters a setting a plot a big reveal and an aha moment just like any other story the characters are the people affected by your story this could be your stakeholders customers clients and others when adding information about your characters to your story you have a great opportunity to include a personal account and bring more human context to the facts that the data has revealed think about why they care next up is a
setting which describes what's going on how often it's happening what tasks are involved and other background information about the data project that describes the current situation the plot sometimes called the conflict is what creates tension in the current situation this could be a challenge from a competitor an inefficient process that needs to be fixed or a new opportunity that the company just can't pass up this complication of the current situation should reveal the problem your
analysis is solving and compel the characters to act the big reveal or resolution is how the data has shown that you can solve the problem the characters are facing by becoming more competitive improving a process inventing a new system or whatever the ultimate goal of your data project may be finally your aha moment is when you share your recommendations and explain why you think they'll help your company be successful when i'm working on a presentation this
is where i like to start too using these basic elements to outline your presentation can be a great place to start and they can help you organize your findings into a clear story and once you've decided on these five key parts of your story it's time to think about how to pair your narrative with interesting visuals because as you're learning an interesting and persuasive data story needs interesting and persuasive visuals coming up you'll learn even more about how to be an expert data storyteller
welcome back now that you know how to prepare the key parts of your data story the character setting plot big reveal and the aha moment it's time to think about the visuals and how your slideshow should look it's always good to remember that your presentation reflects on you if it's messy disorganized or full of images that don't support your story your audience could easily lose confidence in your results and recommendations on the other hand if your slideshow looks professional and appealing
you've got a better chance to capture your audience's attention and keep them focused on your main points themes are a great tool for this they control the color font types and sizes formatting and positioning of text and visuals some themes are fun or creative while others have a more professional look by choosing a theme that matches the tone and information you're communicating your presentation will have a consistent look and support the argument you are trying to make next comes the title it's good to include a title and subtitle that
describe what you're about to present you should include the date of your presentation too especially if you're including data that's likely to change over time specifying a date such as a date created or date last updated gives anyone viewing your presentation important context a good slideshow guides the audience through your main communication points but it doesn't repeat every word you say or give a lot of written information part of your job is to choose what information to include
this might be a description of what's being shown in a visual the first step in a process a set of directions or an important message that you want to be sure your audience understands and remembers also be sure to adjust the font size so your audience can easily read what you've written a good rule is to keep text to less than 5 lines and 25 words per slide basically you want your audience focused on what you're saying not busy reading the slides also choose your words carefully it's always
smart to avoid slang terms abbreviations that people might not know and words or phrases that are specific to one particular region now let's discuss visuals visuals help the audience quickly understand the content of each slide they can help you make a point in a way that words might not be able to alone great visuals don't leave room for interpretation because the meaning is instantly understood when you include visuals on a slide try not to share too many details all at
once choose just the data points that support your points especially your key message i like to ask myself what's the single most important thing i want my audience to learn from my analysis that helps me decide which visuals will be most likely to get the point across if you have several important things you need to include don't cram them all on one slide instead create a new visual for each point then add an arrow a call out or another
clearly labeled element to direct your audience's attention toward what you want them to look at and finally when you get to your big reveal and aha moment your visuals must communicate these messages with clarity and excitement these are the most powerful discoveries from your analysis make it feel that way before you go there's one last thing i'd like to share it's a quick tip for knowing when to copy and paste link or embed a visual into a slideshow this can be challenging for new data
analysts but there are some simple points to keep in mind when you copy and paste a visual into your presentation you can edit it directly within your slideshow if your visual or its data points exist in other places such as a tableau dashboard any changes you make will not affect them there now this also means your visuals won't be updated if the original data set changes this means your visual might not be reflecting the latest information but if you link your visual within your
presentation the visual lives within its original file and the slideshow connects to it with the visuals url because the two files are now linked when you make changes to the original file say a spreadsheet the changes will automatically appear in your presentation this can be useful if the data is likely to change over time your slideshow will always be up to date finally an embedded object also lives in the original source file but the difference is that it doesn't
get automatically updated if the source file changes the embedded copy is completely independent similarly you can make changes to it in your presentation without affecting the visual or data points from the original source file so the main difference between pasted linked and embedded objects has to do with where you store them and how you update them after you place them in your slideshow now that you're beginning to understand how to make great slideshows take a few minutes to practice what you've learned
create a new slideshow and select an appropriate theme add your text visuals and an exciting reveal at the end try pasting linking and embedding visuals from different sources to see how they behave differently you can design a presentation about any data set that interests you it doesn't need to be long or have a ton of information just take the first steps and have fun telling your own data story
hi my name is sandes and i am analytical lead at google my role is turning data into powerful stories that influences business decisions i have a untraditional background where i have a six-year gap between my high school and my college career so for me when i was trying to start all over again so i started at a community college that was my first exposure to online learning and it was perfect because like i was managing kids at home
so at google we talk a lot about imposter syndrome and i personally relate to it quite a bit being the first female in my family to graduate university and also being an immigrant a lot of times i'm surrounded by people who do not look like me for example there was one time where i was presenting to senior leaders in my in my org and i was so nervous presenting to them i was like this i'm gonna totally blow this up and they're going to figure out that i'm just totally fraud and fake one of the things
that i changed is that even though i was the only female in my team i started networking i started expanding my network and i met a lot of women who are from the country where i am from um and they were also immigrant they also struggled with english and they also looked like me and they were doing very well in their career they were being successful so when i looked at them i was like okay if they can do it then so can i so that was for me was a very a big confidence boost to kind of like get over that imposter syndrome feeling but like i
struggle from it day to day like i'm struggling with it right now standing in front of you like do i even deserve to be like talking about my journey and my skills so it's completely normal there are a few things that i like to do one is that i like to give myself a pep talk like pep talk definitely works like just saying like you're totally worth it you you deserve it it like does wonders for me personally the second thing i like to do is um i like to keep a log of my success and failures so when i
am at a down point when i'm feeling down or feeling like uh feeling like i do not belong here i look at all the things that i have achieved from that log and that kind of helps me that's a good reminder of the hard work that i put in to kind of get here so i did not get here to because of luck i got here because i worked hard and um i earned it my family is actually really really proud of me um after seeing me go to school and graduate with two kids my younger brother he
actually went to school with two kids as well and he graduated he finished his master's program and my sister-in-law who also had two kids then she was managing and after seeing me that i could do it they had somebody to look up to and so my sister-in-law went back to school and she finished her degree as well so i think just being the first in my family was really hard because i didn't have anybody to look up to but now i am that person that people in my family can look up to specifically girls and they can pursue
whatever they put their minds to welcome back now that we're in the share phase of the data analysis process it's time to show other people what we've found you've already learned about creating data visualizations and how to use data driven storytelling now it's time to talk about actually presenting the data maybe the idea of presenting your findings to stakeholders makes you nervous or maybe you're getting excited just thinking about it either way these upcoming videos will get you ready to present like a pro
coming up we'll learn about the art and science of presentations some best practices you can use for future presentations and how to bring multiple data sources together to tell the whole story as a data analyst it's important to find answers and make new discoveries during your data analysis but it's just as important to share those findings with other people so if you're ready let's get started hi again earlier in this program you learned how to keep your audience in mind when communicating your data
findings by making sure that you're thinking about who your audience is and what they need to know you'll be able to tell your story more effectively in this video we'll learn how to use a strategic framework to help your audience understand the most important takeaways from your presentation to make your data findings accessible to your audience you'll need a framework to guide your presentation this helps to create logical connections that tie back to the business tasks and
metrics as a quick reminder the business task is the question or problem your data analysis answers the framework you choose gives your audience context to better understand your data on top of that it helps keep you focused on the most important information during your presentation the framework for your presentation starts with your understanding of the business task raw data doesn't mean much to most
people but if you present your data in the context of the business task your audience will have a much easier time connecting with it this makes your presentation more informative and helps you empower your audience and knowledge that's why understanding the business task early on is key here's an example let's say we're working with a grocery store chain they've asked us to identify trends in online searches for avocados to help them make seasonal
stocking decisions during our presentation we want to make sure that we continue focusing on this task and framing our information with it let's check out this example slide presentation we can begin our presentation by framing it with the business task here in this second slide i've added goals for the discussion it starts with share an overview of historical online avocado searches
under that a more detailed explanation we'll cover how avocado searches have grown year over year and what that means for your business then we'll examine seasonal trends in online avocado searches using historical data this is important because understanding seasonal trends can help forecast stocking needs and inform planning and finally discuss any potential areas for further exploration this is where we'll address
next steps in the presentation this clearly outlines the presentation so our audience knows what to expect it also lets them know how the information we share is going to be connected to the business task you might remember we talked about telling a story with data before you can think of this like outlining the narrative we can do the same thing with our data vis examples if we're showing this visual graph of annual searches for avocados
we might want to frame it by saying this graph shows the months with the most online searches for avocados last year so we can expect that this interest in avocados will fall on the same months this year that can even be used in our speaker notes for the slide this is a great place to add important points you want to remember during the presentation ahead of time these notes aren't visible to your audience in presentation mode so they're great reminders you can refer to as you present
plus you can even share your presentation with speaker notes ahead of time to make the content more accessible for your audience using this data the grocery store can anticipate demand and make a plan to stock enough avocados to match their customers interests that's just one way we can use the business task to frame our data and make it easier to understand you also want to make sure you're outlining and connecting with your business metrics by showcasing what business metrics you
use you can help your audience understand the impact your findings will have think about the metrics we use for our avocado presentation we track the number of online searches for avocados from different months over several years to anticipate trends and demand by explaining this in our presentation it's easy for our audience to understand how we used our data these data points alone the dates or number of searches aren't useful for our audience
but when we explain how they are combined as metrics the data we're sharing makes so much more sense here's another potential data vis that we want to use we can frame it for our audience by including some of our metrics there's an explanation of what time period this data covers our data shows google search queries from 2004 to 2018. where we gathered this data from search queries are limited to the united states only
and a quick explanation of how the trends are being measured google trend scores are normalized at one hundred so now that our audience understands the metrics we use to organize this data they'll be able to understand the graph more clearly using a strategic framework to guide your presentation can help your audience understand your findings which is what the sharing phase of the data analysis process is all about coming up we'll learn even more about how to weave data into your
presentations hey great to have you back so we know how to use our business tasks and metrics to frame our data findings during a presentation now let's talk about how you'll work data into your presentations to help your audience better understand and interpret your findings first it's helpful for your audience to understand what data was available during data collection you can also tell them if any new relevant data has come up or if you discovered that you need different data
for our analysis we use data about online searches for avocados over several years the data we collected includes all searches with the word avocado so it includes a lot of different kinds of searches this helps our audience understand what data they're actually looking at and what questions they can expect it to answer with the data we collected on searches containing the word avocado we can answer questions about the general interest in avocados but if we wanted to know more about something specific like guacamole
we'd probably need to collect different data to better understand that part of our search data next you'll want to establish the initial hypothesis your initial hypothesis is the theory you're trying to prove or disprove with data in this example our business task was to compile average monthly prices our hypothesis is that this will show clear trends that can help the grocery store chain plan for avocado demand in the coming year you want to establish your hypothesis
early in the presentation that way when you present your data your audience has the right context to put it in next you'll want to explain the solution to your business task using examples and visualizations a good example is the graph we used last time that clearly visualize the search trend score for the word avocado from year to year raw data can take time to sink in but a good example or visualization can make it much easier for your audience to understand you during a presentation keep in mind
presenting your visualizations effectively is just as important as the content if not more and that's where the mccandless method we learned about earlier can help so let's talk through the steps of this method and then apply them to our own data visualizations the mccandless method moves from the general to the specific like it's building a pyramid you start with the most basic information introduce the graphic you're presenting by name this directs your audience's attention
let's open the slide deck we were working on earlier we've got the framework we explored last time in our two data vis examples according to the mccandless method we want to introduce our graphic by name the name of this graph yearly avocado search trends is clearly written here when we present it we'll be sure to share that title with our audience so they know where to focus and what the graphic is all about next you'll want to answer the obvious questions your audience might have
before they're asked start with the high level information and work your way into the lowest level of detail that's useful to your audience this way your audience won't get distracted trying to understand something that could have easily been answered when the graphic was introduced we added in the information about when where and how this data was gathered to frame this data vis but it also answers the first question many stakeholders will ask where is this data from and what does it cover so going back to the second graph
in our presentation let's think about some obvious questions our audience might have when they see this graph at first this data vids is really interesting but can be hard to understand at a glance so our audience might have questions about how to read it knowing that we can add an explanation to our speaker notes to answer these questions as soon as this graph's introduced this shows time running in a circle with winter months on top and some are on bottom the farther
elements are away from the center the more queries happen around that time for avocado now some of the answers to these questions are built into our presentation once you've answered any potential questions your audience might have you'll want to state the insight your data vis provides it's important to get everyone on the same page before you move into the supporting details we can write in some key takeaways to this slide to help our audience understand the most important insights from the graphic here we let the audience know that this
data shows us the consistent seasonal trends year over year we can also see that there's low online interest in avocados from october through december this is an important insight that we definitely want to share even though avocados are a seasonal summer fruit searches peak in january and february for a lot of people in the united states watching the super bowl and eating chips with guacamole is popular this time of year now our audience knows what takeaways we want them to
have before moving on the fourth step in the mccandless method is calling out data to support that insight this is your chance to really wow your audience so give as many examples as you can with our avocado graphs it might be worth pointing to specific examples in our monthly trends graph we can point to specific weeks recorded here during the week of november 25th 2018 the search score was around 49 but the week of february
4th the search score was 90. this shows the rise and fall of online search interest with the help of some of the very cool data in our graphs finally it's time to tell your audience why it matters this is the so what moment why is this insight interesting or important to them this is a good time to present the possible business impact of the solution and clear action stakeholders can take you might remember that we outlined this in our framework at the beginning of our presentation
so let's explain what this data helps our grocery store stakeholder do first they can account for lower interest in avocados between the months of october and december they can also prepare for the super bowl surge in avocado interest in late january early february and they'll be able to consider how to optimize stocking practices during summer and spring there's a little more detail under each of these points but this is a basic breakdown of the
impact and that's how we use the mccandless method to introduce data visualizations during our presentations i have one more piece of advice take a second to self-check and ask yourself does this data point or chart support the point i want people to walk away with it's a good reminder to think about your audience every time you add data to a presentation so now you know how to present data using a framework and weave data into your presentation for your audience and you got to learn the mccandless
method for data presentation coming up we'll learn some best practices for actually creating presentations see you soon my name is brittany and i'm an analytical lead at google one of the tips that i have is to try to keep things kindergarten simple and what that means is keep the concepts that you're presenting as simple and as straightforward as possible whenever you enter a room there are going to be
people within that room of varying interest levels varying knowledge levels they have different levels of subject matter expertise nobody wants to present to a room whose eyes are glazing over my pet peeve about seeing certain presentations with data is that they often will include what i like to call eyesore charts and what an eyesore chart is it has way too much data has way too many colors it just looks busy and you just
really can't figure out what the presenter is actually trying to say another tip that i have is to make your presentation fun so nobody wants to be in a room where you are talking for a full hour and the only voice that you're hearing is your own one of the things that i try to do to break it up is i try to think of little fun games or quizzes or i'll play a video or ask questions to the audience just to make sure that they're fully engaged
and that they are talking back to me another tip that i try to incorporate into my presentations is storytelling everybody loves a good story and when you do it right you are able to connect and make your audience engage in a way that they probably wouldn't if you weren't telling that story the last tip that i have is make sure that you have an ally in the room oftentimes before i'm giving a really big data presentation
i will find one or two people that i know are going to be in the room and present my content to them ahead of time and what that does is it allows me to not only get feedback but it also allows me to make sure that someone else is nodding their head and aligned to the numbers that i'm about to present and i can't even tell you how many times that i've been in presentations where those allies have really come to my rescue when the room asks a lot of questions or
are potentially trying to poke holes in the analysis those allies are there to speak up and they really are going to have your back and lend credibility to what it is that you're presenting the most challenging part of my job would be the fact that i am there to convince people to do something that they might not be fully confident that they should be doing and a lot of times it takes multiple conversations multiple rounds of convincing for
someone to actually come around to what i was trying to articulate or get them to do when you have spent maybe six months or a year building an analysis and building a story and building a narrative for someone to apply to their strategies and they actually come around and they actually do it that makes the challenges worth it [Music] so we're going to dive into specific examples that we have so we've built a what we call a messy example of
a data presentation and we'll walk through each slide and the presentation as a whole to understand why it doesn't actually work well for explaining a specific analysis we have a title slide the relationship between health and happiness around the world now right off the bat there is a very generic picture about the world it is a very lengthy title and we know what we're going to be talking about but there's nothing here that's really compelling about the presentation
so the first slide when we are looking at a data presentation we have immediately put a lot of data in front of them and a lot of text in front of them so right now they don't know what they're looking at there was no statement of purpose we don't have an introduction slide they don't know who i am they don't know why they're there so what are we talking about why are we talking about it what should they walk away with there's none of that we've just
immediately gone into the specific data visuals that we are showing them now an important aspect of every slide is also to have a title now title subtitle these things help people understand exactly what this slide is going to be discussing so that they know what they're trying to understand as you are talking so immediately getting here the audience is going to be lost they're going to be trying to read the slide they're going to be trying to decipher what the visuals mean it's important for you to make sure there's not too much going on
now if we move on to the next slide what we're looking at here is the visual is better it's easier to understand there's not more than one of them we have a map we have visual colors to represent the numeric values within them but again there's nothing for them to really understand now this is where you can explain within the speaker notes but you also have again a lot of words no title what is it that they are really trying to get from this slide part of a good presentation as well is
the theme that you have or a consistent theme so you now switch sides of the specific visual you have the text on the other side doesn't mean you can't do it but what you're really trying to do throughout a presentation is build some familiarity especially with data analytics you're building familiarity with the visuals that you're showing them the data by the end of the presentation they should understand the data or the concept as much as you do and finally we have the conclusion slide this one does have a title has laughter
is the best medicine we understand again what it is that we're looking at but there was no logical flow on how to get here was this overall presentation compelling we put two slides on there we had too much text we didn't really explain anything about it and again there's awkward placement on where all of these things are within the slide itself when you're thinking about building a presentation you should think about it from the audience's point of view the only thought that's going through
their head is where should my focus be as i'm trying to listen as i'm trying to comprehend where should i be looking if you have slides like we just showed you they don't know where they should be looking or they're going to spend their time reading and trying to comprehend while you're also talking so it's very important that you are directing their gaze and directing the audience so that they know exactly what they should be listening to what they should be trying to understand and you are guiding them through to the overall conclusion so to sum up in terms of what is wrong with this overall presentation and
not just what you're going to be talking about or what you are trying to conclude but just the overall placement of the data visuals and the visuals that you chose so the main thing is there was no story no logical flow you started with a bunch of scatter plots and a lot of text and you moved on to the heat map of the happiness scores but without somebody presenting something without any idea of or concept behind what they are trying
to conclude you didn't have titles there's too much text it was very difficult to understand and it was uneven and inconsistent so even if you had a really good explanation on each slide you might have lost the audience because what they were trying to do what they were trying to understand is what was the slide trying to tell them and finally the most important part of any data analytics presentation is the recommendation or
conclusion slide you had that but there was no title they didn't know that this was the end of the presentation that this is where they should be trying to put all the pieces together coming up we're going to discuss how we can improve this presentation as well as dive into what the presentation will actually look like when we're trying to explain how health and happiness are correlated [Music] so now that you know what not to do i'm going to walk you through how i would tackle a presentation so to start you can see the title slide
is a lot simpler we have a title we have who is presenting and we have when it occurred now i do want to talk a little bit about the date at the bottom which is an important factor that you shouldn't forget to include you may come back to this presentation a few months later even a year later or this may be disseminated across your company it's important to know that when this analysis took place and why and what were the circumstances of it and a big part of that is what were the circumstances of the
company at the time that this was actually presented so the next slide is giving an idea of what you're going to be presenting to everyone and when so you start with your purpose statement right you're going to be discussing what are we talking about the next aspect is where you actually tell your story and that's an important concept is this overall presentation is a story with data and finally you have your conclusion slide you're going to be very clear that
this is the conclusion this is where you're going to add recommendations if this is in a business context and then you'll have your appendix where you can have an additional information on data data visuals as well as overall context for the presentation that may not work within the overall flow itself so our transition slide what are we talking about so this is where you let the audience know what we're talking about what are we trying to tell them what are
all the slides that are following this going to be driving towards so when we look at this slide i'm trying to identify if there are geographic demographic and or economic factors that contribute to a happier life that is the purpose of the overall presentation so everybody now in the room knows this and that is what they're going to be thinking about as you present all of the data to them next section on our table of contents present the data it is important to mention these will probably have different titles as you
build them out but this is the topic that we're moving into so you'll recognize this visual from the messy slide but it has a different color context that's not as important but what is important is when you get here you have a title on the slide you have a visual but there's no text and this is an important aspect to it is what we're trying to do is walk and introduce the audience to the overall data that you're going to be using
now this is the first slide that has any form of content on it so it's important that you introduce them to the underlying data and seeing as the data is all about geographic demographic and economic data points per each country it's important that the visual represents that if i were to be the presenter on a slide like this i would start by getting to this slide and explaining the process and data that we're looking at so we analyzed data set
consisting of data collected from residents of european countries between 2015 and 2017. the data contained demographic and economic data for individuals within each country including population gdp or gross domestic product and a happiness score per person so i've now introduced them to the data set there's still no text so they know that they should be looking at the visual and listening to me
now the next aspect which is can be over utilized but i've also seen underutilized is using animations in your presentation animations can be used as a way to direct your audience's attention as you speak a way to say look over here at this area of the slide as i'm talking it also allows them not to get too bogged down or distracted as you're introducing new concepts to them because remember as you're introducing data that technical components may be new to a lot of people and finally another way to do this is through
annotations on top of visuals that can be used as another form of directing their gaze and their overall attention so putting these together we can have something like an annotation appear as you're discussing it so if we were trying to explain what the visual is showing we have an annotation that pops up that says happiness score and points to the score within the specific country and we can explain exactly what the visual is showing so in this way we could say something like we began by creating a heat map of the happiness score for each country
where the number within each country represents the overall score and the colors represent how high or how low the score is on a scale so the darker blue the country is the higher the numeric happiness score for that country the deeper red that the country is the lower the happiness score and overall numeric value so what we've done before any text has appeared on the screen is explain the visual explain the overall data that they're going to be looking at throughout the
presentation so that they now can understand when we dive into this specific analysis so it's important that you only use text on the screen in a short and concise manner to highlight the main points that you're discussing so after i introduce the visual i can now dive into the analysis so we have our first bullet point happiness levels vary widely by country so with this as it appears my speaker notes can be something along the lines of however as high and low scores are
spread sporadically throughout the map there is little correlation that we find between geographical location and happiness finally we concluded that the geographical location alone was not a strong indicator of happiness so as you can see as i'm discussing and as i'm explaining what we were looking at within the data the overall text on the screen only populated as i began to discuss it so the audience knew exactly where to look and exactly what to be listening to when i'm talking
a very important aspect of the flow of the overall presentation is the transition from one slide to the next so as i'm discussing this you can use a bullet point you can use your speaker notes either way there should be some transition from one slide to the next so that you the audience knows that this part is over and they know what's coming next so for this slide i used my speaker note so i'm going to explain the transition something like our next step was to identify the demographic and economic differences
between the higher and lower countries to isolate the correlated features between them so we get to the next slide very common theme it may be a different visual but the overall title and where the text is going to show up is going to be in the same place so we familiarize them with the overall theme of the presentation within three slides now the title immediately tells what we're going to be discussing the previous one was geographic this one is all based on population
as we move through this slide and as you saw in the messy example we use a lot of scatter plots and scatter plots may not always be the best option because they are rather difficult for people to follow within presentations but if you explain it to them once so that they understand you can use them throughout the presentation because you familiarize them with it so because it's the first time it popped up it's important that you explain the visual in depth and all the features of it that you will be talking about later throughout the presentation
we use animations again we talk about what are the axes on the scatter plot we created a scatter plot in which we plotted countries based on their happiness score and the population to see if there was a correlation between the two the higher up something is on the scatter plot the happier the country is the further to the right that the country is plotted the larger the population and the line that goes between the two is testing for correlation or
if these two different points are related to one another so these annotations and these animations are there to clarify what the chart is plotting now the overall purpose is that we are attempting to identify if there is a relationship between the population size of the country and the overall happiness score so now that you have explained what this visual is you can now dive into the results of it now this slide itself has one bullet
point it is the results of the overall analysis that you can find just based on the data visual we found that there was little to no correlation between happiness and population based on the analysis that we ran so all discussion and in-depth explanation of the visual is kept in the speaking notes besides the overall annotations and again the transition is very important to the next slide so you can say something like so next we dove into the specific demographics of
each country to see if we can identify the features that separate or correlate with the overall happiness of the country again same thing we have the title we know what we're going to be talking about now this is now the health of each country and how it correlates with happiness we have a scatter plot again except the good news is you've already introduced what the scatter plot is and what you are comparing on there so now the audience has been familiarized with the data set you don't have to go through and explain
exactly what the visual is representing you can dive into the overall differences or analysis that you're going to be presenting on this slide you can have something explaining that we found a positive correlation between happiness and health or overall life expectancy of the country now we found this because the correlation coefficient between the two different factors being happiness and health was 0.50 now you just introduced a new
concept this is where you have to now explain the new concept because otherwise you may lose people in the room this is a technical component to your overall analysis and it is important component so it is critical that you do explain what it is but in a simplified way so that everybody understands so you can say something along the lines of a correlation coefficient is a measure of strength and direction of the linear relationship between two variables
so in other words the closer to one that the number is the more positively correlated they are meaning when one of the variables goes up so does the other one the closer to negative one that the number is the more negatively correlated they are meaning as one of the variables such as happiness goes up that the other variable like health would go down and the closer to zero it is it means they are not correlated at all
which is what we saw between population and happiness and means that they have no relationship together so we've now explained exactly what it is that we used as an analysis on this specific slide and it's important again that we discuss the transition to the next so we did find that there was a positive correlation between happiness and health but the question remains are happy people healthy or are healthy people happy we know that
they are related but we don't know what causes the other and finally what contributes to a longer life expectancy if we know that longer life expectancy is related to happiness what is it that helps create longer life expectancy within a country now these are the two questions that we need to answer before the end of the presentation moving on from here so again we are creating a logical flow as we move through this presentation now we we're looking at a new concept
wealth within each country now that you are using such as the scatter plot are familiar with the audience it's okay now that you add in additional ones so you can say something along the lines of we then analyzed how gdp or the overall economic status of the country relates to the overall health of the country because if we know that gdp is related to health and we know that health is related to happiness then we can infer additional information through that
so we found that there is a strong correlation between gross domestic product and the overall health of a specific country with a 0.7 correlation coefficient so higher than the overall correlation coefficient for health and happiness next we found an even stronger correlation between gdp and happiness so whereas we first looked at health and happiness and then gdp and health we're now looking at gdp
and happiness and found that it has the highest correlation coefficient between all three of those comparisons so we have a conclusion within just this slide which is we found that richer countries have a higher average happiness level this is a good transition to the overall conclusion of now your entire presentation so again you're directing your audience through just presenting the text that you want them to look at your first conclusion from your overall
presentation wealthier countries and ones that have sustained economic growth tend to have a higher average happiness level your second conclusion healthier countries also tend to have a happier population however healthier countries also tend to be wealthy and finally this is where you take it home so our evidence suggests that wealth health and happiness all go together it's important to also discuss any
caveats or future analysis that needs to be ran to answer the questions that may come up based on this analysis so we have said that the evidence suggests that wealth health and happiness all go together but that does not mean that one causes the other so there needs to be future analysis to understand any causal effects between them and then you have your final slide and this is where questions would come in so it's important to remember that data
storytelling is an art what we've given you is some high level overview and examples of what not to do and an improved version but don't be afraid to put yourself in there the overall presentation style is going to come from your personality and skill set within data analytics you can use the tools that we use to help you build the layout of your presentation but it's up to you to really put a lot of yourself into it and a lot of your own skills to help people understand the overall analytics
that you've run hey there so far we've learned about using a framework to guide your audience through your presentation and how to weave data in now i want to talk about why these presentation skills are so important and give you some simple tips you can use during your own presentations as a data analyst you have two key responsibilities analyze data and present your findings effectively analyzing data seems pretty obvious it's
in the title data analyst after all but data analysis is all about turning raw information into knowledge if you can't actually communicate what you've learned during your analysis then that knowledge can't help anyone there's plenty of ways data analysts communicate emails memos dashboards and of course presentations effective presentations start with the things we've already talked about like creating effective visualizations and organizing your slides
but how you deliver those things can make a big difference in how well your audience understands them and you want to make sure they leave your presentation empowered by the knowledge and ready to make decisions based on your analysis that's why strong presentation skills are so important as a data analyst and if the idea of giving a presentation makes you nervous don't worry a lot of people feel that way but here's a secret it gets easier the more you practice
now let's look at some tips and tricks you can use when giving your presentations we'll go over some more advanced ones later but let's start with the basics for now it's natural to feel your adrenaline levels rise before giving a presentation that's just because you're excited to be there to help keep that excitement in check try taking deep controlled breaths to calm your body down as a bonus this will also help you channel all that excitement into a presentation style that shows
your passion for the work you've done you might remember we talked earlier about using the mccandless method to present data visualizations well it's also a good rule of thumb for presentations in general start with the broader ideas the obvious questions your audience might have and what they need to understand to put your findings in context then you can get more specific about your analysis and the insights you've uncovered let's go back to our avocado example and imagine how we'd start that presentation
after we introduce ourselves in the title of our presentation we have a slide with our goals for the discussion we start with the most general goals and then get more specific we might say our goal for today is to first provide you all with the state of the world on online avocado searches then we'll examine the opportunities and risks of seasonal trends in online avocado searches we'll move into actionable next steps that can help you start taking advantage of these opportunities
as well as help to mitigate the risks and finally we'd love to make the third part of discussion with you about what you think of these next steps what you'll want to notice here is how our presentation focuses on the general interest in avocados online before getting into specifics about what that means for our stakeholders we also learned about the five second rule as a quick refresher whenever you introduce a data visualization you should use the five second rule and ask
two questions first wait five seconds after showing a data visualization to let your audience process it then ask if they understand it if not take time to explain it then give your audience another five seconds to let that sink in before telling them the conclusion you want them to understand try not to rush through data visualizations this will be the first time some of the people in your audience are encountering your data and it's worth making time in your presentations for them
here's our first date of is in the avocado presentation when we get to this slide we want to introduce our yearly avocado search trends graph and explain the basic background we've included here after we wait five seconds we can ask are there any questions about this graph let's say one of our stakeholders asks could you explain google search trends great after explaining that we wait another five seconds then we can tell them our conclusion
searches for avocados have been increasing every year you'll learn more about these concepts later on but these are some great tips for starting out finally when it comes to presenting data preparation is key for some people that means doing dress rehearsals for others it means writing out a script and repeating it in their head others find visualizing themselves giving the presentation helps try to find a method that works for you the most important thing to remember is
that the more prepared you are the better you'll perform and the lights are on and it's your turn to present coming up we'll cover more best practices for presentations and also look at some examples looking forward to it hey good to see you again by now you've learned some ways to organize and incorporate data into your presentations you've also covered why effective presentation skills are so important as a data analyst now you're ready to start presenting like a pro coming up i'll share some pro tips and
best practices with you let's get started we've talked about how important your audience is throughout this program and it's especially important for presentations it's also important to remember that not everyone can experience your presentations the same way sharing your presentation via email and putting some forethought into how accessible your data vis is before your presentation can help ensure your work is accessible and understandable but during the actual
presentation it can be tempting to focus on what's most interesting and exciting to us and not on what the audience actually needs to hear and sometimes even the best audiences can lose focus and get distracted but here's a few things you can do during your final presentation to help you stay focused on your audience and keep them engaged first try to keep in mind that your audience won't always get the steps you took to reach a
conclusion your work makes sense to you because you did it this is called the curse of knowledge basically it means that because you know something it can be hard to imagine your audience not knowing it it's important to remember that your audience doesn't have the same context you do so focus on what information they need to reach the same conclusion you did earlier we covered some useful things you can add to your presentations to help with this first answer basic questions about where
the data came from and what it covers how is it collected does it focus on a specific time or place you can also include your guiding hypothesis and the goals that drove your analysis adding any assumptions or methods you use to reach your conclusions can also be useful for example in our avocado presentation we group months by season and looked at overall trends and finally explain your conclusion and how you reached it your audience also has a lot on their mind already
they might be thinking about their own work projects or what they want to have for lunch they aren't trying to be rude and it doesn't mean they aren't interested they're just busy people with a lot going on so try to keep your presentation focused and to the point to keep their minds from wandering try not to tell stories that take your audience down an unrelated line of thinking and try not to go into too much detail about things that don't concern your audience you might have found a really exciting
new sql database but unless your presentation is about databases you can probably leave that out your audience can also be easily distracted by information in your presentation for example the more you include in a chart the more your audience will need to explore it so try to avoid including information in your presentations that you don't think will be productive to discussions with your audience sharing the right amount of content to keep your audience focused
and ready to take action it's also good to note that how you present information is just as important as what you present and i have some best practices for delivering presentations first pay attention to how you speak keep your sentences short don't use long words where short words will work build in intentional pauses to give your audience time to think about what you've just said try to keep the pitch of your sentences level so that your statements aren't confused for questions
also try to be mindful of any nervous habits you have maybe you talk faster tap your toes or touch your hair when you're nervous that's totally normal everyone does but these habits can be distracting for your audience when you're presenting try to stay still and move with purpose practice good posture and make positive eye contact with the people in your audience finally remember that you can practice and improve these skills with every presentation
accept and seek out feedback from people you trust feedback is a gift and an opportunity to grow and with that you've completed another module the presentation skills you've learned here like using frameworks weaving data into your presentation and best practices you can apply during your actual presentations are going to help you communicate your findings with audiences effectively hello so let's talk about how you can be sure you're prepared for a q a for starters
knowing the questions ahead of time can make a big difference you don't have to be a mind reader but there's a few things you can do to prepare that'll help for this example we'll go back to the presentation we created about health and happiness around the world we put together these slides cleaned them up a bit and now we're getting ready for the actual presentation let's go over some ways we can anticipate possible questions before our q a to give us more time to think about
the answers understanding your stakeholders expectations will help you predict the questions they might ask as we previously discussed it's important to set stakeholder expectations early in the project keep their expectations in mind while you're planning presentations and q a sessions make sure you have a clear understanding of the objective and what the stakeholders wanted when they asked you to take on this project for this project our stakeholders were interested in what
factors contributed to a happier life around the world our objective was to identify if there were geographic demographic and or economic factors that contributed to a happier life knowing that we can start thinking about the potential questions about that objective they might have at the end of the day if you misunderstood your stakeholders expectations or the project objectives you won't be able to correctly anticipate their questions so think about these things early and
often when planning for a q a once you feel confident that you fully understand your stakeholders expectations and the project goals you can start identifying possible questions a great way to identify audience questions is to do a test run of your presentation i like to call this the colleague test show your presentation or your data vis to a colleague who has no previous knowledge of your work and see what questions they ask you they might have the same questions your real audience does
we talked about feedback as a gift so don't be afraid to seek it out and ask colleagues for their opinions let's say we ran through our presentation with a colleague we showed them our data visualizations then asked them what questions they had they tell us they weren't sure how we were measuring health and happiness with our data in this slide that's a great question and we can absolutely work that information into our presentation sometimes the questions asked during our colleague test help us revise our presentation other
times they help us anticipate questions that might come up during the presentation even if we didn't originally want to build that information into the presentation itself so it helps to be prepared to go into detail about your process but only if someone asks either way their feedback can help take your presentation to the next level next it's helpful to start with zero assumptions don't assume that your audience is already familiar with jargon acronyms past events or other necessary
background information try to explain these things in the presentation and be ready to explain them further if asked when we showed our presentation to our colleague we accidentally assumed that they already knew how health and happiness were measured and left that out of our original presentation now let's look at our second data vis this graph is showing the relationship between health wealth and happiness but includes gdp to measure the economy we don't want to assume that our
audience knows what that means so during the presentation we'll want to include a definition of gdp in our speaker notes we've added gross domestic product total monetary or market value of all the finished goods and services produced within a country's borders in a specific period of time we'll fully explain what gdp means as soon as this graphic comes up that way no one in our audience is confused by that acronym it helps to work with your team to
anticipate questions and draft responses together you'll be able to include their perspectives and coordinate answers so that everyone on your team is prepared and ready to share their unique insights with stakeholders the team working on the world happiness project with you probably have a lot of great insights about the data like how it was gathered or what it might be missing touch space with them so you don't miss out on their perspective finally be prepared to consider and describe to your stakeholders
any limitations in your data you can do this by critically analyzing the patterns you discovered in your data for integrity for example could the correlations found be explained as coincidence on top of that use your understanding of the strengths and weaknesses of the tools you use in your analysis to pinpoint any limitations they may have introduced while you probably don't have the power to predict the future you can come pretty close to predicting stakeholder and audience questions by doing a few key things remember to focus on
stakeholder expectations and project goals identify possible questions with your teams review your presentation with zero assumptions and consider the limitations of your data sometimes though your audience might raise objections to the data before and after your presentation coming up we'll talk about the kind of objections they might have and how you can respond see you next time welcome back in this video we'll talk about how you can handle objections
about the data you're presenting stakeholders might raise objections during or after your presentation usually these objections are about the data your analysis or your findings we'll start by discussing what questions these objections are asking and then talk about how to respond objections about the data could mean a few different things sometimes stakeholders might be asking where you got the data and what systems it came from or they might want to know what transformations happened to it before
you worked with it or how fresh and accurate your data is you can include all this information in the beginning of your presentation to set up the data context you can add a more detailed breakdown in your appendix in case there are more questions when we are talking about cleaning data you learn keeping a detailed log of data transformations is useful that log can help you answer the questions we're talking about here and if you keep it in your presentation's appendix it'll be easy to
reference if any of your stakeholders want more detail during a q a now your audience might also have questions or objections about your analysis they might want to know if your analysis is reproducible so it helps to keep a change log documenting the steps you took this way someone else could follow along and reproduce your process you can even create a slide in the appendix section of your presentation explaining these steps if you think it'll be necessary and it can be useful to keep a clean version of
your script if you're working with a programming language like sql or r which we'll learn all about later also be prepared to answer questions like who did you get feedback from during this process this is especially important when your analysis reveals insights that are the opposite of your audience's gut feelings about the data making sure to include lots of perspectives throughout your analysis process will help you back up your findings during your presentation
finally you might be faced with objections to the findings themselves a lot of the time these will be questions like do these findings exist in previous time periods or did you control for the differences in your data your audience wants to be sure that your final results accounted for any possible inconsistencies and that they're accurate and useful now that you know some of the possible kinds of objections your audience might raise let's talk about how you can think about responding first it can be useful to communicate
any assumptions about the data your analysis or your findings that might help answer their questions for example did your team clean and format your data before analysis telling your audience that can clear up any doubts they might have second explain why your analysis might be different than expected walk your audience through the variables that change the outcomes to help them understand how you got there and third some objections have merit especially if they bring up something
you hadn't thought of before if that's true you can acknowledge that those objections are valid and take steps to investigate further following up with more details afterwards is great too and now you know some of the basic objections you might run into understanding that your audience might have questions about your data your analysis or your findings can help you prepare responses ahead of time and walking your audience through any assumptions about the data or unexpected results are great approaches to responding
coming up we'll go over even more best practices for responding to questions during a q a bye for now hello again earlier we talked about some ways that you can respond to objections during or after your presentations in this video i want to share some more q a best practices let's go back to our world happiness presentation example imagine we've finished preparing for a q a and it's time to actually answer some of our audience's questions
let's go over some ways that we can be sure that we're answering questions effectively we'll start with a really simple one listen to the whole question i know this sounds like a given but it can be really tempting to start thinking about your answer before the person you're talking to has even finished asking their question on slide 11 of our presentation we outline our conclusions after explaining these conclusions one of our stakeholders asks how was happiness measured for this
project it's important to listen to the whole question and wait to respond until they're done talking take a moment to repeat the question repeating the question is helpful for a few different reasons for one it helps you make sure that you're understanding the question second it gives the person asking it a chance to correct you if you're not anyone who couldn't hear the question will still know what's being asked plus it gives you a moment to get your
thoughts together after listening to the question and repeating it to make sure you understand you can explain that participants in different countries were given a survey that asked them to rate their happiness and just like that your audience has a better understanding of the project because you took the time to listen carefully okay so now that they know about the survey they're interested in knowing more at this point we can go into more detail about that data we have a slide
built in here called the appendix this is a great place to keep extra information that might not be necessary for our presentation but could be useful for answering questions afterwards this is also a great place for us to have more detailed information about the survey data so we can reference it more easily as always make sure you understand the context questions are being asked in think about who is your audience and what kinds of concerns or backgrounds they might have
remember the project goals and your stakeholders interests in them and try to keep your answers relevant to that specific context just like you made sure your presentation itself was relevant to your stakeholders we have this slide with data about life expectancy as a metric for health if you're presenting to a group of stakeholders who are in the health care industry they're probably going to be more interested in the medical data and the relationship between overall health and happiness
knowing this you can tailor your answers to focus on their interests so the presentation's relevant and useful to them when answering try to involve the whole audience you aren't just having a one-on-one conversation with the person that's asked the question you're presenting to a group of people who might also have the same question or need to know what that answer is it's important to not accidentally exclude other audience members you can also include other voices if there's someone in your audience or team