Fundamentals of Data Visualization

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

Danielle Szafir

Offered By

Vital Skills for Data Science SpecializationUniversity of Colorado Boulder

About this Course

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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 Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Course 4 of 4 in the

Vital Skills for Data Science Specialization

Intermediate Level

Python, foundation in Data Science

Approx. 12 hours to complete

English

Subtitles: English

What you will 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 will gain

Evaluation DesignVisualizing data with AltairUser-Centered Design (Create basic visualizations that match data and user needs)Task Analysis (Define elements of a data analysis and/or communication problem)

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 4 of 4 in the

Vital Skills for Data Science Specialization

Intermediate Level

Python, foundation in Data Science

Approx. 12 hours to complete

English

Subtitles: English

Instructor

Danielle Szafir

Assistant Professor

ATLAS Institute and Computer Science

14 Learners

1 Course

Offered by

University of Colorado Boulder

CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.

Syllabus - What you will learn from this course

Week

Week 1

5 hours to complete

Basics of Design

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.

5 hours to complete

13 videos (Total 156 min), 11 readings, 1 quiz

13 videos

Course Overview3m

Why Visualization?13m

A Grammar of Graphics10m

Introduction to Altair6m

Hands on with Altair - Part 113m

Hands on with Altair - Part 211m

Mapping Basics10m

Mapping Tips11m

Color17m

Rules of Thumb16m

Introduction to Interaction12m

Interactions Part 113m

Interactions Part 212m

11 readings

A Tour Through the Visualization Zoo10m

Data Humanism: The Revolutionary Future of Data Visualization10m

Vega-Lite: A Grammar of Interactive Graphics10m

Workshop Overview of Altair Basics10m

Exploratory Data Visualization with Altair10m

The Structure of the Information Visualization Design Space10m

Extra Resources: Data Types, Graphical Marks, and Visual Encoding Channels10m

The Good, the Bad, and the Biased10m

Extra Resources: WTF Visualizations10m

Extra Resources: Ethical Dimensions of Visualization Research10m

Toward a Deeper Understanding of the Role of Interaction in Information Visualization10m

1 practice exercise

Week 1 Quiz30m

Week

Week 2

4 hours to complete

User Needs

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.

4 hours to complete

9 videos (Total 113 min), 7 readings, 1 quiz

9 videos

Visualization Tasks12m

Task Elicitation15m

Basic Design Methods11m

Design Studies17m

Perception Overview5m

Preattention13m

Attention & Search14m

Uncertainty13m

Ethical Questions8m

7 readings

A Design Space of Visualization Tasks10m

Design Study Methodology: Reflections from the Trenches and the Stacks10m

Overview: The Design, Adoption, and Analysis of a Visual Document Mining Tool for Investigative Journalists10m

Criteria for Rigor in Visualization Design Study10m

On the Prospects for a Science of Visualization10m

Attention and Visual Memory in Visualization and Computer Graphics10m

Why Authors Don't Visualize Uncertainty10m

1 practice exercise

Week 2 Quiz30m

Week

Week 3

3 hours to complete

Evaluation

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.

3 hours to complete

7 videos (Total 80 min), 7 readings, 1 quiz

7 videos

Module Overview1m

Evaluation Overview8m

Defining Insight12m

Qualitative Evaluation15m

Experimental Evaluation Part 113m

Experimental Evaluation Part 217m

Evaluation Trade-Offs11m

7 readings

Empirical Studies in Information Visualization: Seven Scenarios10m

Toward Measuring Visualization Insight10m

Criteria for Rigor in Visualization Design Study10m

Experimental Research in HCI10m

A Design Space of Vision Science Methods for Visualization Research10m

Empirical Studies in Information Visualization: Seven Scenarios10m

Evaluating Information Visualizations10m

1 practice exercise

Week 3 Quiz30m

About the Vital Skills for Data Science Specialization

Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss.

This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash

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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

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Fundamentals of Data Visualization