Fundamentals of Data Visualization

Fundamentals of Data Visualization

This course is part of Vital Skills for Data Science Specialization

Instructor: Danielle Szafir

Access provided by Alliance University

8,435 already enrolled

3 modules

Gain insight into a topic and learn the fundamentals.

41 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

41 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Develop a toolkit for exploring and communicating complex data using visualization
Produce basic data visualizations using a chosen dataset
Compare methods for visualizing data and understand how these methods may guide users towards different conclusions
Evaluate how effectively a visualization conveys target data

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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This course is part of the Vital Skills for Data Science Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 3 modules in this course

Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation.

This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

In this module, you will learn the foundations of visualization design. You will walk through the key components of a visualization, how we effectively represent data using channels like color, size, and position, and some ground rules for honest and effective visualization. You will also gain preliminary exposure to Altair, a Python library for rapidly generating interactive visualizations. Each week will also include either two readings or one reading and one notebook activity.

What's included

13 videos16 readings2 assignments2 discussion prompts2 ungraded labs

13 videos Total 157 minutes

Course Overview 3 minutes
Why Visualization? 14 minutes
A Grammar of Graphics 11 minutes
Introduction to Altair 7 minutes
Hands on with Altair - Part 1 13 minutes
Hands on with Altair - Part 2 12 minutes
Mapping Basics 11 minutes
Mapping Tips 12 minutes
Color 18 minutes
Rules of Thumb 17 minutes
Introduction to Interaction 13 minutes
Interactions Part 1 14 minutes
Interactions Part 2 12 minutes

16 readings Total 160 minutes

Earn Academic Credit for your Work! 10 minutes
Course Support 10 minutes
Assessment Expectations 10 minutes
AI Citation and Acknowledgement 10 minutes
Assessment Expectations 10 minutes
A Tour Through the Visualization Zoo 10 minutes
Data Humanism: The Revolutionary Future of Data Visualization 10 minutes
Vega-Lite: A Grammar of Interactive Graphics 10 minutes
Workshop Overview of Altair Basics 10 minutes
Exploratory Data Visualization with Altair 10 minutes
The Structure of the Information Visualization Design Space 10 minutes
Extra Resources: Data Types, Graphical Marks, and Visual Encoding Channels 10 minutes
The Good, the Bad, and the Biased 10 minutes
Extra Resources: WTF Visualizations 10 minutes
Extra Resources: Ethical Dimensions of Visualization Research 10 minutes
Toward a Deeper Understanding of the Role of Interaction in Information Visualization 10 minutes

2 assignments Total 35 minutes

AI Policy Quiz 5 minutes
Week 1 Quiz 30 minutes

2 discussion prompts Total 20 minutes

Introduce Yourself 10 minutes
Visualization Project Part 1: Finding your Data 10 minutes

2 ungraded labs Total 120 minutes

Introduction to Altair 60 minutes
Implementing Interaction in Altair 60 minutes

In this module, you will learn how to choose the right visualization for a given scenario. You will learn how to reason about the different kinds of questions people ask with visualization and, how to align your design with that task. The module will cover basics of task analysis, methods for task elicitation, and foundational knowledge of visual perception for design. Each week will also include two external readings or one reading and one notebook activity.

What's included

9 videos7 readings1 assignment1 discussion prompt

9 videos Total 110 minutes

Visualization Tasks 13 minutes
Task Elicitation 16 minutes
Basic Design Methods 12 minutes
Design Studies 17 minutes
Perception Overview 6 minutes
Preattention 14 minutes
Attention & Search 14 minutes
Uncertainty 11 minutes
Ethical Questions 8 minutes

7 readings Total 70 minutes

A Design Space of Visualization Tasks 10 minutes
Design Study Methodology: Reflections from the Trenches and the Stacks 10 minutes
Overview: The Design, Adoption, and Analysis of a Visual Document Mining Tool for Investigative Journalists 10 minutes
Criteria for Rigor in Visualization Design Study 10 minutes
On the Prospects for a Science of Visualization 10 minutes
Attention and Visual Memory in Visualization and Computer Graphics 10 minutes
Why Authors Don't Visualize Uncertainty 10 minutes

1 assignment Total 30 minutes

Week 2 Quiz 30 minutes

1 discussion prompt Total 10 minutes

Visualization Project Part 2: Sketching your Data 10 minutes

In this module, you will learn how to assess the effectiveness of your visualization. You will learn both qualitative and quantitative approaches for evaluating visualizations as well as how to isolate key elements for assessment and iteration. The module will cover basics of insight-based evaluation, interview studies, and experimental design and analysis. Each week will also include two external readings or one reading and one notebook activity.

What's included

7 videos7 readings1 assignment1 discussion prompt

7 videos Total 80 minutes

Module Overview 2 minutes
Evaluation Overview 8 minutes
Defining Insight 12 minutes
Qualitative Evaluation 16 minutes
Experimental Evaluation Part 1 14 minutes
Experimental Evaluation Part 2 17 minutes
Evaluation Trade-Offs 11 minutes

7 readings Total 70 minutes

Empirical Studies in Information Visualization: Seven Scenarios 10 minutes
Toward Measuring Visualization Insight 10 minutes
Criteria for Rigor in Visualization Design Study 10 minutes
Experimental Research in HCI 10 minutes
A Design Space of Vision Science Methods for Visualization Research 10 minutes
Empirical Studies in Information Visualization: Seven Scenarios 10 minutes
Evaluating Information Visualizations 10 minutes

1 assignment Total 30 minutes

Week 3 Quiz 30 minutes

1 discussion prompt Total 10 minutes

Visualization Project Part 3: A Plan for Evaluation 10 minutes

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Build toward a degree

This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Instructor

Instructor ratings

(12 ratings)

Danielle Szafir

University of Colorado Boulder

1 Course 8,435 learners

Offered by

University of Colorado Boulder

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Reviewed on Dec 16, 2023

Very Lively interaction from the mentor. Simplified explanations

Reviewed on Jan 23, 2026

Not your basic data visualization course - very in depth and interesting with concepts that are fresh and new. Professor is very thorough and understandable.

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