Module 4: Data visualizations and presentations

We’ve reached the final practice of EDA,
presenting. As we discuss, visualizing and presenting don’t necessarily come at
the end of your data exploration. You may create visuals of your own data
throughout the data analysis process. The practice of visualizing your data
will help you discover insights and further your own understanding. The presenting practice of EDA is
an important part of both the analyze and execute phases of the PACE framework. Sometimes you’ll create visualizations to
analyze the data during the analyze phase. At other times you will utilize
data visualizations as part of executing an algorithm. In this video, we will focus on
designing data visualizations for the purpose of presenting. In this course, we’ll discuss
concepts such as accessibility, Tableau basics, dashboards,
and data visualizations. You may recall some of these concepts from
the Google Data Analytics Certificate. If you’d like, take a few minutes to
review that content before moving ahead. Coming up, you will learn how to
improve your data visualization skills. The accurate representation of data is
one of the most important aspects of a successful presentation. You’ll learn specific techniques for making data visualizations that
represent the data with precision. Another aspect of designing successful
data visualizations is ensuring they are inclusive. We will talk about tips and techniques for making your data visualizations
accessible to a diverse audience. You will also learn about the data
visualization platform, Tableau. Tableau is a versatile data visualization
software primarily used for presenting data to inform and
improve businesses. Using the software, you’ll learn how
to create data visualizations that tell stories and explain technical
concepts to non-technical audiences. You’ll learn to create dynamic
visualizations with interactive and motion elements and to alter visualizations
based on different audiences’ needs. We will also discuss how to choose
the right graphic or chart, give context during presentations,
and select the proper order and timing for
your data visualizations while presenting. Data professionals often need to
create data visualizations and deliver presentations. That’s why we’ll discuss the most
important skills to help you thrive in this area. After all, you want your audience
to understand your data story. As a data professional, you will have
an opportunity to tell data-driven stories that change your team, department,
company, industry, or even the world. Now, let’s get started.

• Creating a data visualization is an iterative process, which means it involves making changes and improvements along the way.
• The decisions we make when creating a visualization depend on the specific needs of our audience and the purpose of the visualization.
• To create a successful visualization, we need to consider things like colors, text, labels, scale, and areas of emphasis.
• We can use tools like Tableau to create visualizations. Tableau allows us to plot data points on a map, apply filters to narrow down the data, and adjust the formatting to make the visualization easy to understand.
• The goal of a visualization is to meet the needs of our audience. For example, if we want to show the locations of rental properties, we can create a map. If we want to analyze property listings by neighborhood, we may need to update the visualization.
• It’s important to remember that creating visualizations is a multi-step process, and we may need to make changes or fix mistakes along the way.

1. Sales Performance Dashboard: A company may create a dashboard that shows sales data in the form of line charts, bar graphs, and pie charts. This visualization helps the sales team track their performance, identify trends, and make data-driven decisions.
2. Social Media Engagement Metrics: Social media platforms often provide visualizations that show metrics like the number of likes, shares, and comments on posts. These visualizations help businesses understand the engagement level of their content and adjust their social media strategies accordingly.
3. Weather Forecast Maps: Weather forecast websites and apps use visualizations like maps to show temperature, precipitation, and wind patterns. These visualizations help users quickly understand the weather conditions in different regions.
4. Stock Market Trends: Financial institutions and individual investors use visualizations to track stock market trends. Line graphs and candlestick charts are commonly used to show the price movements of stocks over time, helping investors make informed decisions.
5. Health Data Dashboards: Healthcare organizations may create dashboards that display patient data, such as vital signs, lab results, and medication history. These visualizations help doctors and nurses monitor patient health and identify any abnormalities or trends.

Remember, data visualizations are used in various industries and contexts to present information in a clear and meaningful way. The specific types of visualizations used will depend on the data being analyzed and the goals of the organization or individual using them.

Often, creating a data visualization
is an iterative process. The first graph you design may not be
the one you share with stakeholders. The colors, text, labels, scale,
and areas of emphasis are all aspects of a graph that may change
to meet different business needs. But how do we get to that final product? What decision should we make along the way
to create a successful visualization? To answer these questions, we’ll take a
sample dataset and create a data graphic. Then we’ll discuss how to alter
the visualization along the way. This will illustrate the types of
decisions data professionals make when creating graphs and
charts for presentations. Now let’s get started. Imagine you’re working on a new app
which will gather a list of the top rated rental homes, and
apartments in Europe. In one easy to access place. Your company asks you to start by
gathering the best rental listings in Athens Greece. You want to know the locations of rental
units where the owners have 40 or more property listings. You only want properties
with many good reviews, and a rental listing price between 90 and
€250. This helps to clarify the type
of visualization you need. Since you want the locations of
rental units where owners have 40 or more property listings,
you need a geographic map. Additionally, you limit the count of listings based on
the criteria of your analysis. To create a map with the locations
of rental properties, a program like Tableau will
be the most effective. If you’re familiar with Tableau, feel free to add this data set into
the public Tableau, and try it yourself. First, we’ll start with a map of Athens. We’ll plot all the rental
listings on that map. We’ll use filters to remove
rental properties that don’t meet the analysis criteria such as,
prices outside are defined range. Finally, we’ll adjust the formatting
to make our map easily understandable, and accessible. Let’s consider the dimensions
of the graphic. We know that the end result needs to
be a map of listings in Athens Greece. Let’s input the latitude and
the longitude from the data set, and plot those listings with the host
name as the labeled spot on the map. Now that we’ve added those data points to
Tableau, let’s see what it looks like. We have a map of Greece, but we need to
refine it, so that is easier to read. Next, let’s go to the listings we don’t
want based on our analysis criteria. Narrow the results to only those owners
who have more than 40 rental listings. We can do that with the column in the data
set called Calculated Host Listings Count. We limit the amount to
only values over 40. This narrows the fields. Next, we limit the entries by price. Our criteria includes
listings between 90 and €250, which gives us a much more
manageable number of listings. We want to keep only the listings
with a lot of reviews. The word, a lot, of course is relative. So, we’ll use a percentile filter to just
give us the top 50% of total reviews. This is the result. Now, we have a reasonable amount
of data points, but what’s next? We need to make it easier to comprehend. To do so, we add a title, and add a color
coding for the owners of each listing. We also add the price for
each listing, and this is the result. At this point, we have a workable
visualization that meets our criteria. This is a successful first version. However, if we want to make this
visualization part of a formal presentation, we need
to make it accessible. We do that by including a descriptive
caption beneath it, and making the mark slightly smaller,
so more of the prices display. We also want to be sure that colors
are friendly to individuals who might have difficulty seeing color. This is the result. The goal with visualizations
is to meet an audiences needs. You want to share the geolocation
of rental property owners. You accomplish that with
your map of Athens. But, what if the apps business team
decides they need different data display? Or if you want to review property
listings by neighborhood, you would need to update
the visualization. The main point here is that
the resulting visualization may not always fit the business need. Designing visualizations, as you’ll
remember is a multi step process. Even after you’ve
completed a visualization, you will sometimes find
you need to update it. You may also get new information that
requires you to change the original criteria. Making changes, and fixing mistakes
is all part of the process.

We talked a lot about the power and
usefulness of Tableau. Are you ready to log
in and try it out? Great. We’ll discuss how to access a free online
version of public Tableau. Build basic data
visualizations and understand when to use
a variety of charts. You may remember some
of these concepts from the Google Data
Analytic Certificate, and you can revisit that
program for a quick refresher. When you access Tableau Public, it may appear different
from this video. Keep in mind that Tableau Public may have updated
its user interface. This should not be
a problem because the steps you follow
are almost the same. To begin, go to the
public Tableau website, which is the free
online version of Tableau you can access
from your browser, sign into your Tableau
public account. Next, you’ll need to access a new data source
and Tableau Public. We’ll use the NOAA
Lightning Strike dataset again for this video. On the Tableau public
homepage, Select, Create a viz upload your data source
from your computer using the file provided for you. Once the data is uploaded, it will be redirected to
Tableau’s data source screen. The dataset is divided
into four columns, date, number of strikes, x coordinate, and y coordinate. There are symbols above
or to the side of these fields names representing the types of data that are
included in the dataset. There’s a calendar
icon for the date, a pound sign for
the numeric column, number of strikes, and globe icons for the latitudes
and longitudes, or x and y coordinates. There are also tabs
representing a new worksheet, new dashboard, and new story. Create a new worksheet. A worksheet in Tableau Public is a data page that contains a single view of a
data visualization. The new worksheet is blank
other than the data column. The data source field
of the worksheet is pre loaded with the
column headers we identified on the
data source page. There is a thin line separating the list
into two sections. This indicates the
data type and whether the data is discrete
or continuous. Tableau divides all
the data fields into two broad data types,
dimensions and measures. You can tell them apart based on the icons Tableau
assigns to each field. Dimensions are
qualitative data values used to categorize and group data to reveal
details about it. Measures are numeric
values that can be aggregated or placed
in calculations. The green and blue colors in Tableau indicate
another aspect of the list of dimensions and measures in the Tableau
worksheet data tab. Green indicates that the
data field is continuous, blue indicates that
the field is discrete. The term continuous is a mathematical concept
indicating that a measure or dimension has an infinite and uncountable
number of outcomes. Discrete is a
mathematical concept indicating that a measure or dimension has a finite and countable
number of outcomes. With these definitions, we
can learn about our list of data fields even before we plot them on a
chart or graph. Keep in mind that Tableau may
assign the wrong data type. You can always change data
from measure to dimension, or discrete to continuous
by right clicking on the data field if the data type and its label do not match. Next, let’s begin
our visualization by plotting a line graph. Line graphs are useful for
presenting time series data or tracking changes in data values over different periods of time. To start, drag date
into the column field. In the pop up that displays, you can select different
segments of time, select year for this chart, next drag and drop number of
strikes into the row field. You now have a line graph. Now you know how to
create line graphs. Next we’ll create a bar chart. Bar charts are useful
when you want to compare things like data from two
different time periods. To get started, use the toolbar to duplicate
your worksheet, in the new sheet open the dropdown menu in the
Marks field, select Bar. Changing data visualization
types is really that simple. Next, let’s create a bar chart
to compare two datasets. Go to the filters tab
and edit the filter. Check 2009 and 2018. Leave all the other
years unchecked. You’ll find a comparison
of the total number of lightning strikes in the
years 2009 and 2018. Next, add a label to each
bar by dragging number of strikes to the square in
the marks field titled label. Now we know the exact
difference between the number of lightning strikes
in 2009 and 2018, 30.10000 and 44.6
million respectively. For the last visualization
let’s compare 2009 to 2018 by quarter, to start duplicate
the worksheet again, drag date to the column field, Tableau will automatically add quarters to the stacked bars, sectioning out Q1, Q2, Q3 and Q4 for both years. To make the side
by side comparison of each quarter stand out, drag date to the color
square in the marks field. The colors will adjust based on the year with 2009 in the original color and
2018 in a different color. You can keep the chart
like this or click the dropdown arrow and
select quarter instead. Now the quarters are segmented
by different colors. To determine the color scheme, decide if you want to highlight the differences between
quarters or between years. You’re starting to discover how useful Tableau
can really be. Often data professionals
use Tableau for their EDA work because it helps them quickly create
visuals of their data. Soon you’ll be able
to do that too.

So far in this program, you’ve
learned the basics of Tableau Public, like how to upload a data source and
use discrete or continuous measures and
dimensions to plot data visualizations. Next, you’ll design more
complex visualizations, including heat maps,
box plots, and histograms. You’ll also learn how to
input calculations and write code within the designed elements
of a visualization, let’s get started. Go to Tableau Public and
create a new data visualization, we’ll use another data set from
the NOAA Lightning Strike data. Once you’ve uploaded the data source,
you’ll start your data visualization on the data source page with
the NOAA Lightning strike data. The date, longitude, latitude, and
number of strike columns are all there. Let’s open a new worksheet so
that we can create a geographic map. This requires latitude and longitude data
that Tableau can use to plot the points. First, drag longitude from the data
list to our columns field, next, drag latitude into the rows field,
now we have a map, this is a great start. Next, filter the data so that you
have fewer data points to work with, drag date into the filters field and
select year from the drop down. Then configure the filter to
select only the year 2018. Next, use color gradients to help
differentiate the denser locations on the map from the more scattered
lightning strike locations. Drag number of strikes to
the box labeled color, in the drop down menu under marks,
make sure that density is selected. Now that the map is much better,
the default color for this visualization is blue. The lighter blues indicate more
scattered lightning strikes, and the darker blues are heavier lightning
strike areas for the year of 2018. To change the color,
click on the color square and change it from automatic
to just something else. Try one or two, consider which will
make the map most accessible for yourself and for
people who might have visual disabilities. Next, let’s create a heatmap. As discussed in a previous video,
a heatmap is a type of data visualization that depicts the magnitude of an instance
or set of values based on two colors. To create a heat map in Tableau Public,
you need one or more dimensions or one or two measures, to start, drag date
to the row field and select year. Next, you need to create
a calculation that derives the month from the date string. In the date drop down menu,
click create and select calculated field,
a popup will open. Let’s give the calculator a title, for
the calculated field name input month. Use the left function to pull the month
names from the string in the column date, pull the first three letters from
the month listed in the string. Type left parentheses, date, name, parentheses, month in single quotes,
a comma, date in brackets, parentheses,
a comma and three. Then add a closing parentheses, click OK. Now there is a new field on
the list of data on the left, drag month to the columns field. Then drag number of strikes to
the color square under the marks field, be sure that in the marks dropdown,
that square is selected. Now you have a heatmap,
just like the density map, the default color for
this heat map is blue. You can adjust the heat map to
a color range that fits your needs, remember to consider accessibility
as you create your visualizations. Now let’s create a box plot,
you’ll recall from a previous video that a box plot is a data visualization
that depicts the locality, spread and
skew of groups of values within quartiles. You’ve already learned how to
create a box plot chart in Python. To create a box plot in Tableau Public,
first drag number of strikes to the row field then drag date to
the column field and select year. If Tableau doesn’t default to boxplot, select circle from the dropdown
menu under the marks field and scroll to the Show Me tab to
select Box and Whisker plot. What you have so far is not much to look
at, all you’re seeing is a thin line of data points in a blank
field of white background. However, once you drag date into the
detail square under the marks field and select day, you’ll see box
plots appear in full strength. With box plots,
most data pros will include a legend or annotation of the exact numbers of
median and mean for presentation. You can of course change the colors and
size of the circle in this box plot using the color and
size squares under the marks field. Our last complex plot is a histogram. A histogram is a data visualization that
depicts an approximate representation of the distribution of
values in a data set. To create a histogram, first you need
to create bins, a bin is a tableau term that describes the custom segments of
data that values can be grouped into. Bins are an important part of histograms
because the groupings in heat maps determine how the data is segmented and
compared. For a histogram of lightning strike data, group the total number of
lightning strikes into bins. Do this by clicking on the number of
strikes dropdown, select create and then bins, you can give the bin a name and
then determine the size. If you select 50 for the size of bins,
there would be one bin for every 50 lightning strikes, any value between
one and 50 would be included in that bin. Because there are millions of
values of lightning strikes, you should select a number
between five and ten. This will display the distribution
of values in much more detail. Next, drag the new bin
to the columns field, then drag number of
strikes to the row field. Make sure the number of strikes
in rows is the count or CNT rather than sum or average, this
presents the actual count of each bin. To give us more detail, drag number
of strikes to the filter field and limit the field numbers between 1 and 200. Drag number of strikes to the label
square under the marks field, make sure it is set to count. You can adjust the color to make
the data visualization more accessible. Coming up next, you’ll learn how to create
a series of data visualizations to use in a presentation, I’ll meet you there.

Hi, my name is Drew and I am a predictive modeling
specialist here at Google. Originally, I actually
studied to be an accountant. I worked as an accountant
for about seven years. Most of what I did was
working in finance. Most people who work
in finance become incredibly good
at Excel and so I became better and better
and better at Excel all the time and started working
with different formulas. Formulas led me to figure
out coding and then coding led me towards
data analytics. Exploratory data analysis
is everything you can’t do anything without
understanding the data first. You need to first go
in and you didn’t really understand what
every single variable is, what the distributions
look like, what the initial
correlations look like. You can start to go
deeper than that building explanatory models
to start to look at longer correlations and multi correlation between
multiple variables together, it’s everything that
is data science. I can remember a project we
are working with a client who is spending tons of
money on takeover media. Takeover media requires
a large investment and it basically blocks out all other media
span from other people during a day so that you are
concentrated on that day. What we found is that
there wasn’t a reaction between takeover media and
conversions on the other side. What we had to do when
framing it to the client is first figure out what are all the potential objections
the client might have. Once we got those inputs, we were able to put together
a story that wasn’t so destructive to what
they’re doing currently, but was constructive and
show them the way forward. When delivering news about data, it’s important to shift from a data mindset to
a client mindset. You need to marry the ideas
that you learned during your data analysis to how it can actually be used by the client. A lot of the ways that we
do this is through visuals. We try and incorporate visuals that are very
simple yet communicative. We use humor. Humor is a great way of engaging people and it makes it
very memorable for people. You use personal stories. Personal stories help get your point across to
others very quickly. I once had a professor who told me that you have
about 30 seconds to actually communicate with someone until they
stop paying attention. You need to be able
to get in there, make sure they remember it, know what the takeaways are
and have very succinct in next steps of what to do with the data that you have
presented to them.

Data professionals often
need to present their data. Creating, sharing and discussing data visualizations is
an important part of telling data stories. In this video, you will create a series
of data visualizations that work together to tell a story. This will allow you to lead an audience
from one concept to the next and build additional context as you
present each visualization. But first, let’s consider a scenario
imagine you’re working for an organization that wants to learn more about
lightning strikes in the United States. They request a series of data
visualizations that illustrate the increase in lightning
strikes over time and detail lightning strike data in three
states Texas, Oklahoma and Kansas. After you create your visualizations, you will need to share them with
the organization’s directors. With the audience in mind,
you can get started. You need to consider three strategies for
organizing and presenting data
visualizations in a series. Chronological, generic-to-specific and
specific-to-generic a chronological approach to data
visualizations is useful for data that is best understood
in a time series. A generic-to-specific approach helps
an audience consider an issue before describing how it affects them,
and a specific-to-generic approach is useful to highlight impacts
the data can have on a broader scale. Let’s select an approach,
a chronological approach or time series would be most helpful
to consider data over time. This does not address an important
aspect of the request. Visualizing strike data in
three specific states with a specific-to-generic approach. The climax of the presentation would focus
on the United States as a whole rather than lightning strikes in Texas,
Oklahoma and Kansas. This does not meet the needs
of the organization. A generic-to-specific approach would
allow you to illustrate the countrywide increase in lightning strikes followed
by specific data on each state. You can highlight national trend before
targeting the lightning strike data in Texas, Oklahoma and Kansas. This meets the organization’s needs, so a generic-to-specific
approach is the best choice. Before you design your visualizations, you need to create three different
tableau public worksheets. These will form a story in tableau public,
a story is a tableau term for a group of dashboards or
worksheets assembled into a presentation. For the first worksheet, you will create
a line graph showing an upward trend in lightning strikes starting with 2009. For the second worksheet, you will show that the greatest amounts
of lightning strikes have shifted from the east coast of the United States
to the South Central Mainlands. And for the third worksheet, you’ll show
the number of lightning strikes in Texas, Oklahoma, and Kansas in 2018,
the last year with complete data. These three sheets will form
the generic-to-specific organization for the story, let’s open tableau public and
start designing. As always, start by uploading your data
source for the column field drag in date, which is a discrete dimension and
select year for it and for the row. Add the number of strikes
measure into that field. A line graph should appear if it doesn’t, open the show me tab and
find the line’s discrete selection. You’ll notice the line moves
steadily upward from 2009. Next, we will make the data visualization
easier to interpret for example, add two annotations to the line graph. First, add 30.1m strikes
in 2009 to the beginning of the graph to give the audience
a clear starting point. Then, add 45m strikes in 2018
to its applicable position. Next, let’s create a geographic
data visualization. Since we want to progress from
generic-to-specific, it is important to show the trend of lightning
strikes moving west over the decades. To do that, we needed to create a time
series map that shows the location of lightning strikes in the US for
each year since 2009. In the new worksheet, place the x
coordinate in the column field and the y coordinate in the row field. This will create a map focused
on the eastern and central US. If you’re still seeing a line chart,
make sure that both your x and y coordinates are marked as continuous. Once you have the geographic map,
click on the dropdown for both the x and y coordinates and
select measure then average. This will give us a manageable view of
the average number of lightning strikes in the US for each year. Next, add the date discrete dimension
to the filters and the pages fields. Select year for the filter method,
the filter field allows us to create a dynamic filter which can be used to
select just one year of lightning strikes. The pages field can create a distinct
snapshot or page of lightning strikes for each year. Finally, add the number of
strikes to the detail field. The map showing lightning strike locations
is comprehensive we just need to add the interactive part to it. With the year dimension in the pages and
filters fields, you can format interactive legends to
the side of the data visualization. These allow users to self select
the years they wish to see. We have two of the three worksheets
done that we need for our presentation. The last thing to create is the most
specific visualization of the three a snapshot of 2018 lightning strikes for
Texas, Oklahoma and Kansas. To do this, we’ll create three new
worksheets one for each state, then we’ll place the three
snapshots into one dashboards. We’ll work together on the Texas
visualization then you can create the Oklahoma and
Kansas visualizations all on your own. Our map of lightning strikes in the state
of Texas starts in almost the same way as the geographic chart we just made. We put the x coordinate
in the column field and the y coordinate in the row field. We put the number of strikes
measure in the detail field. This time, we add number of
strikes to the color field and make the color coincide
with the number of strikes. Use red for most strikes and
yellows for fewest. Because we will limit these state
snapshots to just 2018, we drop the date into the filters field, select year,
then uncheck all the years except 2018. Finally, we need to create
a set in tableau public. A set is a tableau term for
a custom field of data created from a larger data set
based on custom conditions. We can create a set by first
selecting all the data points in Texas to do this,
we’ll use the Lasso tool. You will follow the state line as best
as you can all the way around Texas. Once complete,
you’ll find a pop up window, select Keep only, then click create Set. Drag your newly created
set to the filter field. Now only the average lightning strikes for Texas will show up on our map,
we can now assemble them into one story. First, add the two other state
worksheets for Oklahoma and Kansas, which were created in the same
way as the Texas worksheet. Then create a dashboard for the three
states illustrating the locations of lightning strikes in the US for
the year 2018. You should also create dashboards for
the other two worksheets created earlier, the line chart and
the interactive map of the US. Now that we have three dashboards,
we can create a story in tableau. On the first page of the story,
insert the dashboard with the line chart. Fill in the caption to give details
about what the chart is showing, on pages two and three of the story. Put the interactive map of the US
followed by the dashboard that includes all three states,
Texas, Oklahoma and Kansas. With the three captions filled in,
we have a complete story coming up. You’ll learn how to create an interactive
dashboard in tableau, I’ll meet you soon.

So far in this program, you’ve been
practicing essential Tableau skills. Now, you will learn how to
put these skills together and create an interactive dashboard. The interactive dashboard you create
will display the quantity and the location of all lightning strikes in
the United States, from 2009 to 2018. The dashboard will be color coded based
on the quantity of lightning strikes and it will allow viewers
to select any year or location to learn
the count of the strikes. Let’s get started. Begin by opening Tableau public and
uploading the data source provided. To build the dashboard, you will need
to create three different worksheets. First, you’ll create an interactive US map
with locations of lightning strikes for each year. Then you’ll create a dynamic stat sheets
listing the metrics for each year. Finally, you’ll design a way to pull and
display the lightning strike metrics for the individual locations
on each year’s map. Let’s start with the interactive
lightning strike map. Begin at the x coordinate and
the y coordinate in the column and the row field. Next, drag date into the filters field and
select Year. Make sure the show filter
option is checked. For the last part of this map,
drag number of strikes into both the color square and
detail square in the marks field. The color square will categorize
the dots on the map with a color code based on the quantity of strikes. The detail square will
plot the locations for each of the lightning strikes for
each year. Make sure when you do this, that both number of strikes
variables are set to Continuous. For the next worksheet, you will create a
list of three metrics that will change on the dashboard whenever a year is selected. The three metrics are total number of
strikes, average number of strikes for each location for that year, and
maximum number of strikes in any location. This worksheet will differ
from our other visualizations. It will be text rather than a bar chart or
line graph. It will sit to the side of
the interactive map and update whenever a new year is selected. First, create a calculation from
the number of strikes dimension. In the blank field space, type number
of strikes and put it in brackets. Next, drag the new calculation into
the tool tip square in the marks field. In the drop down,
select Measure and Average. Repeat this process, but
instead of selecting Average, you should select Sum and Maximum. This creates dynamic fields for
three metrics. The total number of strikes, the average
number of strikes for each location for that year, and the maximum number
of strikes in any location. Finally, create a place to
display these calculations. To keep it simple, go to worksheet and select Show Title,
input the title Metrics for the Year. Under that you will include the three
calculations in the tool tip. To do this, put an asterisk,
followed by total colon, and then sum, and number of strikes,
inside the left and right angle brackets. Repeat the step for the maximum and
average tool tips you created. And there you have it. For the last worksheet, drag number of strikes to the text
square in the marks field. In the dropdown,
select Measure and Average. This creates a dynamic field for
the dashboard we’ll create next. Now, select New Dashboard. You should find all your worksheets
in the list on this page, drag over the worksheet with
the interactive map in it. It may take a few seconds to load. Next, drag your other two
worksheets into the dashboard. Before we clean these up, we need to
create an action for the dashboard. An action is a Tableau tool to help
a user interact with a visualization or dashboard by allowing
control of a selection. When a year is selected
from the filter bar, all the other fields will update with it. We will connect the three
worksheets together with an action. Click on the map in the dashboard,
select Worksheet and Actions. In the Actions pop up,
select Add Action and Filter in the ad filter action menu
in the source worksheet section, select your dashboard from the drop down. Check the box for your map, and for the run action on list, click Select. For target sheets,
select your dashboard from the drop down, then select the map and the recently completed worksheet that will
gather metrics for individual locations. From the list of clearing the selection
will, select Show the Values. Lastly, select all fields for
the filter section, click Okay. And now you have an interactive dashboard. Update the text and
titles before you’re done. Add a title to the filter
legend interactive filter. You should also title the location
average variable, location metrics, and write brief instructions
to clarify what the tool does. For example, you might type, choose a point on the map to
view location specific metrics. Take a few minutes to test your dashboard,
select different years and locations on the map to make sure
the tools function properly. What a collection of Tableau skills. The tools and techniques you’ve learned in
this video will help you communicate and present ideas more effectively throughout
your career as a data professional. Good luck and happy designing.

If you were to design a data visualization
of what you’ve learned in this program, what might it look like? Would you plot your engagement
level over time on a line graph? Or would you create a heat map showing the
task you completed with different colors representing the task that
took the least and most time. No matter what visualization you choose, you’ve learned the skills you need
to create an effective graphic or visualization that
an audience can understand. You’ve learned to use Tableau public
to tell your data stories, and to adjust the content
depending on the audience. We also discuss the best
ways to share technical concepts with non technical audiences. You learned to use Tableau at a high
level, and you’ve applied the technique data professionals used
to create visualizations. No data visualization discussion would be
complete without considering ethics and accessibility. You learned that as a data professional,
it is your responsibility to ensure visualizations accurately
represent the data. You also learned how to make data
visualizations accessible for people who have visual impairments. You learned a lot. Congratulations on completing
this section of the program and good luck on the rest of your journey.