Analyzing Data Visualization Requirements

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Chris Achard

3:00:57

47 View

01 - Course Overview

01 - Course Overview.mp4

01:22

02 - Visualizing Quantities or Qualities

02 - Introducing Qualitative and Quantitative Visualizations.mp4

01:42

03 - Defining Qualitative and Quantitative Visualizations.mp4

03:20

04 - Exploring Examples of Quantitative Visualizations.mp4

01:31

05 - Exploring Examples of Qualitative Visualizations.mp4

02:41

06 - Evaluating Strengths and Weaknesses of Qualitative and Quantitative Visualizations.mp4

05:12

07 - Combining Quantitative and Qualitative Visualizations.mp4

04:32

08 - Demo - Choosing a Qualitative or Quantitative Visualization for a Dataset.mp4

04:53

03 - Visualizing Numerical Data and Categories

09 - Introducing Numerical and Categorical Data.mp4

01:38

10 - Defining Numerical and Categorical Data.mp4

02:31

11 - Splitting Numerical Data into Discrete and Continuous .mp4

03:18

12 - Splitting Categorical Data into Nominal and Ordinal.mp4

03:32

13 - Determining Whether Data is Numerical or Categorical.mp4

04:46

14 - Converting Between Numerical and Categorical Data.mp4

05:18

15 - Choosing Visualizations for Numerical Data.mp4

04:39

16 - Choosing Visualizations for Categorical Data.mp4

03:08

17 - Demo - Dividing Data Into Numerical and Categorical.mp4

04:10

04 - Visualizing to Explain or Explore

18 - Introduction to Exploratory and Expository Visualizations.mp4

01:03

19 - Defining Exploratory and Expository Visualizations.mp4

04:22

20 - C 03.mp4

06:48

21 - Examples of Expository Visualizations.mp4

05:20

22 - How Not to Exaggerate with Expository Data Visualizations.mp4

04:36

23 - Demo - Exploratory and Expository Visualizations.mp4

07:05

05 - Selecting Data to Explain or Explore

24 - Introduction to Data Selection.mp4

01:06

25 - Why Data Selection Matters.mp4

03:01

26 - Case Study - Dashboard vs Report.mp4

05:03

27 - Causation vs Correlation - Be Careful with Expository Visualizations.mp4

04:00

28 - When to Throw Data Away.mp4

03:45

29 - Demo - Selecting Data for Visualizations.mp4

02:51

06 - Displaying the Appropriate Level of Detail

30 - Introduction to Data Detail Levels.mp4

01:26

31 - Why Choosing a Level of Detail Matters.mp4

01:47

32 - Showing Trends vs. Detailed Reports.mp4

05:02

33 - Examples of Different Detail Levels in Visualizations.mp4

06:22

34 - Considering the Visualization Medium.mp4

05:02

35 - Changing Scales - When to Adjust the Y-Axis.mp4

05:30

36 - Demo - Choosing an Appropriate Level of Detail.mp4

03:48

07 - Providing an Appropriate Visualization for Your Audience

37 - Introduction to Appropriate Visualizations for your Audience.mp4

01:08

38 - Understanding Viewer Context for Visualizations.mp4

06:22

39 - Understanding Visualization Context.mp4

06:05

40 - Medium Matters - Resolution, Color and Surrounding Text.mp4

04:58

41 - Demo - Adjusting Visualizations for an Audience.mp4

04:51

08 - Applying Data Visualization Requirement Analysis

42 - Introduction to Applying Data Visualization Requirement Analysis.mp4

01:05

43 - Classifying Data Types.mp4

03:01

44 - Creating Exploratory Data Visualizations.mp4

03:08

45 - Selecting Data for an Expository Visualization.mp4

03:31

46 - Creating an Expository Visualization.mp4

04:06

47 - Adapting an Expository Visualization for Different Audiences.mp4

03:24

48 - Reviewing the Data Visualization Process.mp4

03:08

Description

Being able to create visualizations is a powerful skill that you can learn. This course teaches you how to analyze your dataset and requirements, and how to adjust your visualization to create the most impactful visualization possible.

What You'll Learn?

At the core of creating meaningful data visualizations is a thorough knowledge of understanding your data and your visualization requirements. In this course, Analyzing Data Visualization Requirements, you’ll learn how to prepare to build data visualizations. First, you’ll learn how to distinguish different types of data. Next, you’ll discover how to use visualizations to explore a dataset, or explain a point. Finally, you’ll examine how to adapt a visualization for a specific audience and medium. When you’re finished with this course, you’ll have a foundational knowledge of data visualizations that will help you as you move forward to create visualizations for your own datasets.

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Data Science

Data Visualization

Chris Achard

Instructor's Courses

Chris is an independent software consultant focused on web, mobile, and machine learning. He primarily uses React.js with Node.js or Ruby on Rails for web applications, React Native for mobile applications, and Python for machine learning and data science. He's written an ebook about React, and is excited about teaching software development to others. Recently, he's been experimenting with generative AI models and other advanced AI techniques.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.