Salaries of Data-Related Jobs: Your First Graph

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From the course by Duke University

Data Visualization and Communication with Tableau

1643 оценки

Duke University

Data Visualization and Communication with Tableau

1643 оценки

Course 3 of 5 in the Specialization Excel to MySQL: Analytic Techniques for Business

One of the skills that characterizes great business data analysts is the ability to communicate practical implications of quantitative analyses to any kind of audience member. Even the most sophisticated statistical analyses are not useful to a business if they do not lead to actionable advice, or if the answers to those business questions are not conveyed in a way that non-technical people can understand. In this course you will learn how to become a master at communicating business-relevant implications of data analyses. By the end, you will know how to structure your data analysis projects to ensure the fruits of your hard labor yield results for your stakeholders. You will also know how to streamline your analyses and highlight their implications efficiently using visualizations in Tableau, the most popular visualization program in the business world. Using other Tableau features, you will be able to make effective visualizations that harness the human brain’s innate perceptual and cognitive tendencies to convey conclusions directly and clearly. Finally, you will be practiced in designing and persuasively presenting business “data stories” that use these visualizations, capitalizing on business-tested methods and design principles.

From the lesson

Data Visualization with Tableau

Welcome to week 2! This week you'll install Tableau Desktop to learn how visualizing data helps you figure out what your data mean efficiently, and in the process of doing so, helps you narrow in on what factors you should take into consideration in your statistical models or predictive algorithms. Over the next two weeks, we’re going to learn how to use Tableau to implement this type of visualization and to help you find, and communicate, answers to business questions, as well as work with the Tableau functions that all data analysts should be familiar with. You will learn to install Tableau Desktop and learn to use the program by working with two data sets. In addition, through a series of practice exercises, you will use a data set to do example analyses and to answer specific sample questions about salaries for certain data-related jobs across the United State. Then for graded exercises, you will use a different data set to work out analyses and questions that will require you to directly apply the Tableau skills you have acquired through practice. <p>By the end of this week, you will be able to: <ul><li>Create the most important graphs used in business analysis and transform data in Tableau </li></ul><p>Once you have watched the "Why Tableau" video, review the "Written Instructions to install Tableau Desktop" and install the software. Remember to refer back to the Salary Data Set and to the Dognition Data Set resources posted on the course site this week. You will also complete a graded quiz at the end of the week. <p>As always, if you have any questions, post them to the <b>Discussions</b>.<p>To get started, please begin with the video “Use Data Visualization to Drive Your Analysis" and then review the "Written Instructions to install Tableau Desktop.<p>I hope you enjoy this week's materials!

Salaries of Data-Related Jobs: Your First Graph11:37

Formatting and Exporting Your First Graph6:49

Digging Deeper Using the Rows and Columns Shelves13:10

Meet the Instructors

Daniel Egger
Executive in Residence and Director, Center for Quantitative Modeling
Pratt School of Engineering, Duke University
Jana Schaich Borg
Assistant Research Professor
Social Science Research Institute

So now we have our data set.

We have a plan for how we're gonna analyze it and answer our questions and

we've downloaded Tableau.

We're ready to make our first graph.

Now to do that we should all be using the same data set.

So please go to the course website and download the data set called salary data.

Be sure to save it somewhere on your computer when you'll be able to

find it later.

Now I'm gonna be going through these videos as if you're doing these exercises

with me, at the same time, but feel free to just watch the videos and

do the exercises on your own later.

Ready to get started?

Great, so go ahead and open your copy of Tableau and

when you open it up it's gonna look something like this.

Now when you're using Tableau Desktop,

you're gonna see this section here on the left.

You can see that it connects to different servers, or databases.

You can see MySQL, Oracle, and if you click on more servers you can see the long

list of different databases that Tableau can connect to.

Now if you're using Tableau Public, you're not gonna be able to see these,

because Tableau Public cannot connect to databases.

But since Tableau so kindly generously, donated keys so

that all of our students in this course can use Tableau Desktop,

you all should have access to these databases as well.

So today, we're not gonna connect to a database,

we're going to connect to an Excel file.

So go up here to connect to a file, and click on Excel.

Navigate to where ever you saved the data set and click on salary data, say Open.

Sometimes, especially if you have a Mac,

when you connect the first time this can take a little bit of time.

Our data set is not the biggest data set in the world, but it is quite big for

Excel standards.

So this can take many seconds, tens of seconds,

or in some cases, with slower computers, even as long as a minute.

Now when the data has loaded,

you're gonna come to a screen that looks something like this.

Down here, are all the names that Tableau has automatically interpreted from

the dataset.

So each one of these names is in the header column or

the header row of the Excel file.

And you can see that it automatically classified the name as well

as the type of variable.

So this,

these little icons here tell you the type of variable that Tableau thinks it is.

Now usually Tableau does a pretty good job, but

it doesn't get it perfect every time.

So for example here you can see that case received date it thinks is a string,

that's what that Abc means.

But you can click on this and change the variable so that it's correct, so

we can change this to date, for example.

Do the same thing with decision date and

here's another example of where it didn't get it quite right.

Prevailing wage submitted to be a number and it thinks it's a string.

So we can change it to number, decimal.

And you can do this for every single variable in your data set.

Now there's another place to do this on the next screen.

So we'll go ahead with this for now and then I'll show you how to do it in

a different way when we get to the next screen.

When you think you have you variables named and organized the way you want them,

go ahead and click Update Now.

And you'll start to see the data wheel cuz it's gonna take quite a bit of time to

load the data especially the first time.

Now, as this loads, I wanna prepare you for

what you're gonna see on the next screen.

Tableau is a very powerful program and I really put a lot of effort into trying to

make the user experience as pleasant as possible.

As a consequence, it has a lot of different capabilities and

there are usually two to three different ways to do just about everything.

Now unfortunately, or fortunately depending on how you see it, there isn't

gonna be time for me to show you how to do every single one of those things.

In fact, I would argue it's almost impossible for

anyone to know how to do them all.

And that's why people go to Tableau conferences every year,

to keep up to date and to learn what all the new bells and whistles are.

My strategy is, to go through exercises that I think will show you the most

important things you need to know as a business analyst.

And as we go through them, I'm gonna show you as many tips and

tricks and options as I can.

But I'd like to encourage you to explore the data and

explore Tableau on your own, as well.

The best way to learn all the ins and outs and

really become comfortable with the program is discover these things on your own.

Plus, this will be a great time to practice that curiosity and

fearlessness that's great to have as a data analyst.

Whenever you don't know how to do something, practice looking it up

on your own and proving to yourself that you can figure it out.

Because that fearlessness will be a great thing to have as a data analyst,

and this is a great time to practice it.

This is what you see when you open up the work space in Tableau.

May orient you to see the screen.

Over here in the left is where all of your variables are and

Tableau picks them up according to measures and dimensions.

Measures are basically continuous variables and

dimensions are your discreet or categorical variables.

Now remember I told you in the tips at the very beginning of the course,

sometimes there are variables that can be either continuous or discreet.

In this case either a measure or a dimension and

you need to choose which one you wanna define it as depending on your analysis.

So to change that it's actually very easy in Tableau.

Another time you might wanna do this is if Tableau incorrectly assigns the variable

as either a dimension or measure.

So there actually are two examples of that here.

Two of the main things we're gonna be looking at in our data set are prevailing

wage per year and paid wage per year.

These actually are both continuous meaning they're both measures, but

Tableau thinks they're dimensions.

So to change that, very simple like I said you just click and drag it down.

Another way you can do that is you can click on this drop-down menu and

you can say Convert to Measure, and that will do the same thing.

Now I wanna show you a couple other things you can do with that drop-down.

You can see that this is where you would Rename the variable, or Duplicate it.

But this is also where you would change the data type.

Remember, I told you in the last screen that

there'd be a place to do it here in the work space.

This variable's experience required or the number of months of experience required.

Tableau thinks that that's a string but it's actually a number.

So here, I can change that to number,

just like I did when we are bringing in the data.

One more thing I wanna show you over here in the variable column is this

very little icon, see this little magnifying glass.

This is a way that you can search all of your variables when you have a ton of

different columns and it can be hard to navigate.

So you can just type something in and it will go directly to it.

Now up here is a toolbar.

There are lot of neat things up here that I encourage you to explore.

The main ones I'm gonna point out now are this very important one.

This is the Undo button.

You're gonna use this a lot, imagine.

This is another important one.

It's a little icon that has a graph with an X on it.

That clears the sheet, so it completely clears the workspace and

up here is the larger toolbar or ribbon.

And there are a lot of interesting things up here too.

The main ones we're gonna focus on are the analysis one.

We'll come to this later, but note right now that you see aggregate measures here.

We're gonna be using that a lot and also the format menu.

Now this is one of the things that's very redundant or

it's another way of doing things, almost everything on this list,

you can do in the work space just by right-clicking.

That's good to know that this is here.

Now, down here in the work space which is the sole area here,

these things over here are called Cards.

There's a Marks card, a Filter card, and a Pages card.

These rows here are called Shelves.

This one is the Rows shelf, and this is actually where you're going to tell

Tableau literally what to do with the rows of your data.

How you're gonna aggregate over them.

And there's also a Columns shelf, and this is where you're gonna show Tableau,

how to break up your data, and what categories to put it in.

This is the show me card over here, and

this gives you all the different possible options, all the different ways that

you could represent the data that you have in your work space.

It will automatically tell you the types of charts that are appropriate for

the data you have.

So when they're grayed out, you can't use them, it actually won't allow you to.

So it wants to make sure that you will only use best practices.

But when it's not grayed out, and it will let you make [INAUDIBLE],

we'll show you more about this before, or show you more about this later.

And our first question,

is how much are we likely to make in different data related jobs?

For this question, our dependent variable, or the rows of data that we're

most interested in that we want to aggregate over, is Paid Wage Per Year.

Our Independent variable is Job Title Subgroup.

Remember, I told you that our course assistant was kind enough

to go through all the titles of the different job applications and

put them into subgroups that we might be interested in.

So luckily for us, that part of the analysis is done already.

So now that we know our dependent independent variable,

we are ready to make the first graph.

And I want you to take a deep breath, and don't blink,

because if you do you might miss this.

Now, in order to get paid wage per year, over into the rows shelf,

you can either double click it, or drag it.

Then you put a job title subgroup.

Again, into the column shelf, either drag it or double click it.

Boom, you have your first graph.

It really was that simple and that quick, much quicker than Excel.

Now the first thing I want to orient you to is this access here.

Now if you look carefully, you will see that it says

that the paid wage per year are some very high numbers, in the billions in fact.

Unfortunately, we wouldn't make billions of dollars in any of these

data related jobs.

So what is happening here?

So to show you that we need to go up to the rows shelf and the columns shelf and

I also wanna point out to you that this blue and

green little rectangle looking like things, what Tableau calls pills.

The green ones are measures and the blue ones are dimensions.

So it's a good way to keep track of whether Tableau is thinking your variable

is either dimension or measure.

Now in the rows column, you'll see that it says SUM and

then it opens parenthesis Paid Wage Per Year.

So, remember, when we dragged this variable onto the shelf,

we're telling Tableau we want you to look at these rows of data and

we want you to break them up according to different job title sub groups.

But we have to tell Tableau what we want it to do with all the rows within one

of those different subgroups.

And right now,

it automatically thought we wanted to add them together or some of them.

In reality, we wanted to know what the average was, or

something like the average.

So to change that, we click on this drop-down and you can go down here, and

you see that it says it's treating Measure as a Sum, and it has a check next to Sum.

If we wanna change it to an average or a median,

we would simply click on either average or median.

And my preference is always median,

especially if you don't know what the distribution of your data are.

So let's choose median.

Now if you look at the axis again,

now you see that it's in the tens of thousands, which is much more accurate and

represents what we would truly make in one of these jobs.

Congratulations, you've now made your first graph in Tableau.

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