Analyzing Data-Related Salaries Over Time Using Date Hierarchies

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Do curso por Duke University

Data Visualization and Communication with Tableau

1907 classificações

Duke University

Data Visualization and Communication with Tableau

1907 classificações

Curso 3 de 5 em Specialization Excel para MySQL: Técnicas Analíticas para Negócios

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.

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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!

When to Use Line Graphs2:46

Dates as Hierarchical Dimensions or Measures8:39

Analyzing Data-Related Salaries Over Time Using Date Hierarchies8:22

Analyzing Data-Related Salaries Over Time Using Trend Lines9:15

Analysing Data-Related Salaries Over Time Using Box Plots10:26

Conheça os instrutores

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

In this video we are going to build up what we learned in the last video

to answer the question of whether specific categories of data related jobs

are changing over time, especially compared to other types of jobs.

Let's go back to our workbook, exactly where we left it last time.

Now that we understand how dates work,

we are ready to answer our question whether specific categories of data

related salaries are going up, especially compared to other types of jobs.

Now to do that, we need to add job title subgroup information to this graph and

ideally we would do that by adding a separate line onto the chart for

each job subcategory.

Any idea how we would do that?

In order to get all the subcategory lines in the same graph we won't be able to use

either the column shelf or the row shelf.

So, what do we do when we can't use the column shelf or the row shelf?

We use the marks card of course and

I'm gonna suggest that we code different job subcategories by color.

So, go ahead and take Job Title subgroup and drag it onto Color.

Now you see that Tableau has made a different line the different color for

each Job Title subgroup.

Now before we continue, this data shows us something very clearly.

Looks like we really don't have a lot of data for 2008.

In fact we don't have any data at all for one, two, three,

four, five, six of our job subcategories.

So, we really shouldn't be interpreting what's happening in 2008.

So, if we go back to our chart that we've just made.

I'm gonna suggest that from now on we only look at data from 2009 on.

So, we can do that by using a filter like we did last time.

And so you can filter based on

range of dates and to do that,

we can move this all the way to 2009,

the first of 2009, okay.

Now, when you look at the data this way.

Gives you a different picture than it did before.

Now it looks like really maybe jobs overall are increasing in salary,

so maybe it's they've taken a dip in 2015.

Let's go back and focus on looking at how these change for

our different job title sub groups.

Go ahead and put job title sub group directly on color again,

and you'll see that cuz you're using the same graph it's still only looking at data

from 2009 on and it's also worth noting still that there are two job titles of

categories that don't have any data for 2009, so that's important to remember.

Now I know this chart is a little busy, so

I'm gonna show you a cool feature in Tableau that makes it much

easier to navigates these types of more complicated graphs.

If you go to Job Title Subgroup, you'll see this little icon

that's a highlighter and it says highlight selected items.

So, if you click on that, it's actually a toggle so it will toggle on and off.

When you click on one of the categories in your legend here,

it will gray out everything else and just show you that particular category.

So, this makes it much easier to look at each different

category on it's own without using a filter.

It's a different way of doing it and

this doesn't actually filter out the data by the way,

it's just showing you specific, or it's graying out specific parts of the data.

So, when you look at this you'll see that data scientists looks like

salaries are relatively constant.

They have a bit of a dip in 2015.

Software engineers look like maybe they're starting to go up.

Data analysts unclear and business analysts also unclear.

So, we don't have a lot of information here to go on.

So, I'm gonna suggest that we actually look at this in more detail

to get a better sense of what the data actually are and the way to do that

is to look at a scatter plot again so we can see all the data in one place.

I want you to go up to Analysis and before you do that,

notice that the is green here.

Now go to Analysis and go to Aggregate Measures to unaggregate the data.

As a trick for right now, to make it easier to see everything,

we are going to change year to a dimensional variable cuz it will condense

the x axis a bit due to the way Tableau treats dimensional dates.

Then go to the Show Me card and click on Scatterplot.

And let's make the size of the marks just a little bit smaller.

Okay, so now, when we do this let's, again, look at our highlight,

just specific subgroups and I'm gonna actually put these in order.

According the way we've been looking at them.

And business analyst next.

I'll try to always go in this order to make it easier for

us to pay attention, too.

So, if we just look at the data from data scientist, it looks like,

well, if you look at the very bottom values, they seem like maybe they're going

down and there don't seem to be a whole lot of outliers up here.

What about software engineer?

Software engineer looks very different.

If you look at the very bottom values of these pink dots,

it does look like their going down, but

it also looks like the top values of each one of these blobs is going up.

It's also obvious that there are a whole lot more outliers over time.

So, there are a lot more individual instances of very high salaries for

software engineers.

What about data analysts?

Again we're seeing this pattern perhaps even more pronounced this time.

Where the lowest values seem to be going down while the highest values

seem to be going slightly up.

There aren't as many outliers.

Same thing with business analysts.

So, this suggests is that it might be the case that overall,

either salaries are the same or maybe slightly going up but also

might be the case that you have a greater opportunity to make more, a higher salary.

You also have a greater risk to make less salary.

Let's look at that more directly.

I think the best way to do that is to go back to the line chart.

We need to reaggregate our measures.

So, this is the line chart we had before and now for

purposes right now, let's take job title subgroup off of our workspace.

What I want to do is I want to look more directly at whether it is actually

the case that the lowest salaries for each one of these categories is going down but

the highest salary levels are going up.

To do that, let's put paid wage per year on rows and

this time let's, instead of the median, let's aggregate it and

take the maximum and let's do the same thing and show the minimum.

So, what this is showing us here, this is the median paid wage for

all of our data set over time, this is the maximum paid wage over time, and

this is the minimum paid wage over time.

Now when you look at it this way,

it makes it pretty obvious that at least overall in the whole data set it's

definitely the case that despite the fact that median wages are staying the same.

It's possible to make way more money in individual instances but

it's also possible to have salaries that are way less than you expect

if you were looking for the median.

How can we figure out whether the affects we just saw on our line graphs

are robust enough that we should take them into consideration when we do the rest

of our analysis and when we make decisions?

I'm gonna suggest that the best way to do that in this context is to use statistics.

In the next video we're gonna learn how to compute those statistics in Tableau.

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