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

4.8

stars

15 ratings

Danielle Szafir

3,139 already enrolled

Offered By

Part of the Master of Science in Data Science degree
Learn More

Vital Skills for Data Science SpecializationUniversity of Colorado Boulder

About this Course

20,529 recent views

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

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 4 in the

Vital Skills for Data Science Specialization

Intermediate Level

Python, foundation in Data Science

Approx. 14 hours to complete

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 Design
Visualizing data with Altair
User-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.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 4 in the

Vital Skills for Data Science Specialization

Intermediate Level

Python, foundation in Data Science

Approx. 14 hours to complete

English

Instructor

Instructor rating

4.59/5 (5 Ratings)

Danielle Szafir

Assistant Professor

ATLAS Institute and Computer Science

3,139 Learners

1 Course

Offered by

University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

Learn More

Syllabus - What you will learn from this course

Week1

Week 1

8 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.

8 hours to complete

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

13 videos

Course Overview3mWhy Visualization?13mA Grammar of Graphics10mIntroduction to Altair6mHands on with Altair - Part 113mHands on with Altair - Part 211mMapping Basics10mMapping Tips11mColor17mRules of Thumb16mIntroduction to Interaction12mInteractions Part 113mInteractions Part 212m

13 readings

Welcome and Where to Find Help 10mInformation on Peer Reviews10mA Tour Through the Visualization Zoo10mData Humanism: The Revolutionary Future of Data Visualization10mVega-Lite: A Grammar of Interactive Graphics10mWorkshop Overview of Altair Basics10mExploratory Data Visualization with Altair10mThe Structure of the Information Visualization Design Space10mExtra Resources: Data Types, Graphical Marks, and Visual Encoding Channels10mThe Good, the Bad, and the Biased10mExtra Resources: WTF Visualizations10mExtra Resources: Ethical Dimensions of Visualization Research10mToward a Deeper Understanding of the Role of Interaction in Information Visualization10m

1 practice exercise

Week 1 Quiz30m

Week2

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 110 min), 7 readings, 1 quiz

9 videos

Visualization Tasks12mTask Elicitation15mBasic Design Methods11mDesign Studies17mPerception Overview5mPreattention13mAttention & Search14mUncertainty10mEthical Questions8m

7 readings

A Design Space of Visualization Tasks10mDesign Study Methodology: Reflections from the Trenches and the Stacks10mOverview: The Design, Adoption, and Analysis of a Visual Document Mining Tool for Investigative Journalists10mCriteria for Rigor in Visualization Design Study10mOn the Prospects for a Science of Visualization10mAttention and Visual Memory in Visualization and Computer Graphics10mWhy Authors Don't Visualize Uncertainty10m

1 practice exercise

Week 2 Quiz30m

Week3

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 Overview1mEvaluation Overview8mDefining Insight12mQualitative Evaluation15mExperimental Evaluation Part 113mExperimental Evaluation Part 217mEvaluation Trade-Offs11m

7 readings

Empirical Studies in Information Visualization: Seven Scenarios10mToward Measuring Visualization Insight10mCriteria for Rigor in Visualization Design Study10mExperimental Research in HCI10mA Design Space of Vision Science Methods for Visualization Research10mEmpirical Studies in Information Visualization: Seven Scenarios10mEvaluating 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

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Fundamentals of Data Visualization

About this Course

What you will learn

Skills you will gain