Tools for Data Visualization

Loading...

來自 University of California, Davis 的課程

Fundamentals of Visualization with Tableau

1723 個評分

Explore Related Videos

At Coursera, you will find the best lectures in the world. Here are some of our personalized recommendations for you

University of California, Davis

Fundamentals of Visualization with Tableau

1723 個評分

課程 1（共 5 門，Specialization Data Visualization with Tableau）

In this first course of the specialization, you will discover just what data visualization is, and how we can use it to better see and understand data. Using Tableau, we’ll examine the fundamental concepts of data visualization and explore the Tableau interface, identifying and applying the various tools Tableau has to offer. By the end of the course you will be able to prepare and import data into Tableau and explain the relationship between data analytics and data visualization. This course is designed for the learner who has never used Tableau before, or who may need a refresher or want to explore Tableau in more depth. No prior technical or analytical background is required. The course will guide you through the steps necessary to create your first visualization story from the beginning based on data context, setting the stage for you to advance to the next course in the Specialization.

從本節課中

Getting Started & Introduction to Data Visualization

Welcome to this first module, where you will begin to discover the power of data visualization. You will define the meaning and purpose of data visualization and explore the various types of data visualization tools, beyond Tableau. You will install Tableau on your own device and create your first visualization.

Introduction to Data Visualization0:47

What is Data Visualization and Why Do We Do It?6:12

Tools for Data Visualization6:07

Installing Tableau2:37

Creating Your First Visualization5:19

與講師見面

Suk S. Brar, M.B.A.
Lead Business Consultant
Blue Shield of California
Govind Acharya
Principal Analyst
Budget and Institutional Analysis

[MUSIC]

Welcome back.

Because there are so

many data visualization tools out there, it can get rather confusing.

In this lesson, we're going to examine the types of technology tools used to do data

visualization.

We will be using the public version of Tableau for our course.

But keep in mind the things you will learn in this course

are very much software neutral.

So it doesn't actually matter what piece of software you use.

First, let's look at the apps that everyone has some passing familiarity

with, which is Microsoft Excel.

For the versions beginning in 2010, Microsoft included

a tool called PowerPivot that allows advanced data connections and

improved pivoting capabilities far beyond of what was on offer before in Excel,

which was the standard pivot tables.

If you don't see it immediately available in your version of Excel,

check the add-ins and see if it's activated.

There's a real learning curve for the tool called PowerPivot.

But it is in fact a very powerful option for those who are tied into

the Microsoft architecture, especially through SharePoint and PowerBI.

If you don't have those products, then sharing it across teams will still have

the same limitations as before with Excel, when you were using standard pivot tables.

However, you have much improved data connections

in ways that simply were impossible before.

Secondly, when you're searching for data visualizations on the web,

you often run across either Python or R as data visualization tools.

For machine learning and big data, which we sort of defined in the last lesson,

those are possibly good options.

But I would strongly caution you to learn and

become an expert in either Excel or Tableau before diving into Python or R.

Even though both of this products are free, they are both

incredibly difficult and overwhelming to learn if you're a beginner in this area.

It will be very frustrating for you unless you're a strong programmer.

That being said, both Python and R are in fact tools that are extremely powerful.

And once programmed, can process unstructured data and

big data much more quickly and efficiently than any other tool out there today.

Finally, we come to Tableau's products.

They're different flavors of the software, which can be confusing to a lot of people.

So I'm going to go over them one by one so that you have a sense of them.

First is Tableau Public.

That's the software we're going to be using in the course.

It's free but it has a notable limitations.

First of all, all of the information,

including your raw data are stored in the Tableau cloud.

And can be downloaded by anyone, with no controls possible.

That means you have to be extra careful with your data before it's saved.

The other caveat is that the files cannot be saved locally.

You have to save it to the cloud.

If you have data that's not anonymized in any way, you'll have to prep it ahead of

time in Excel or some other program before importing it into Tableau Public.

Because once the data are imported into Tableau,

it becomes a public document that can be downloaded by anyone.

Tableau Desktop, on the other hand, which is not free, can connect to

an amazing array of data sources and has very strong security for your data.

It's the full version of Tableau.

And you can get data from your own data sources, from your own databases,

your own data warehouses, SQL Server,

Google Analytics, Oracle, Amazon Web Services.

Virtually any data source can be connected through Tableau Desktop.

And it really is a very powerful tool if you can get a copy of that license.

Tableau Server allows the most flexibility of all of the Tableau products.

But it is only available to those who can support a server that is

dedicated to Tableau.

As such, there's a very large financial investment for

an organization having this version.

But if your organization does, count yourself lucky because you could harness

the full capability of the software in ways that no other version can have.

For example, you can control how others see your data,

even when it's embedded on a public website.

Then you can have more secure access to data and

prevent people from downloading those data sets.

And the final type of Tableau product is called Tableau Online.

Tableau Online is intended to be a way to allow control of your

data when embedding data visualizations.

But it doesn't require a large investment of money into a server.

The disadvantage of this approach is that in order to view the visualization online,

the viewer needs a Tableau Online account as well.

So visualizations produced with Tableau Online cannot be shared with the public

like it can with Tableau Server or Tableau Public,

unless you save it as a Tableau Public file.

Whatever piece of software or app you choose, you can be assured that this

course and this specialization can address your needs.

So say tuned for the other parts of the class.

探索我們的目錄

免費加入並獲得個性化推薦、更新和優惠。

Coursera 致力於普及全世界最好的教育，它與全球一流大學和機構合作提供在線課程。

© 2019 Coursera Inc. 保留所有權利。

通過 App Store 下載

通過 Google Play 獲取