Skip to main content
Data Visualization
Graduate Program (& Advanced Certificate) Status
Course Description

Brief introduction to the course

The Internet and modern computers have given us vast amounts of data, so it is more important than ever to understand and analyze large amounts of information. As additional tools to statistics and analytical computations, visualizations allow us to harness our inherent cognitive skills to detect and understand patterns in complex data, as well as to summarize and communicate new discoveries in an intuitive manner.

The goals of the course

The major goals of this course are to understand how visual representations can help in the analysis and understanding of complex data, how to distinguish a good from a bad visualization, how to design effective visualizations, and how to create your own visualizations using programming skills and visualization software. We will achieve these goals by evaluating existing visualizations, teaching general theoretical principles of information design, and by walking students through the analysis and visualization of real world datasets during hands-on classes.

Course organization

Lectures: 12 classes of 100min. For most classes, we will spend the first 30 minutes introducing the day's concepts, and spend the rest of the class doing the lab exercises or evaluating visualizations. Therefore, use of a computer will be required during some lectures. Students can form groups and use their own laptops.

Topics

  • Basics of human perception and cognition of visualization
  • Meaningful visualization for research
  • Data “munging” or cleaning to process and visualize data
  • Visualization of static data with Matplotlib, using Python
  • Principles of Network visualizations (layouts, information reduction)
  • Network visualization using Gephi

  • Basic interactive visualizations using Plotly

Textbooks

Further information, such as the course website, assessment deadlines, office hours, contact details etc. will be given during the course.

The instructor reserves the right to modify this syllabus as deemed necessary any time during the term. Any modifications to the syllabus will be discussed with students during a class period. Students are responsible for information given in class.

Cheating

In short: don't do it! You may work with friends to help guide problem solving or consult stack overflow (or similar) to work out a solution, but copying—from friends, previous students, or the Internet—is strictly prohibited. NEVER copy blindly blocks of code – we can tell immediately.

If caught cheating, you will fail this course. Ask questions in recitation and at office hours. If you're really stuck and can't get help, write as much code as you can and write comments within your code explaining where you're stuck

Learning activities and teaching methods

Lectures: 12 classes of 100min. For most classes, we will spend the first 30 minutes introducing the day's concepts, and spend the rest of the class doing the lab exercises or evaluating visualizations. Therefore, use of a computer will be required during some lectures. Students can form groups and use their own laptops.  

Learning Outcomes

By the end of the course, students will have acquired the following skills:

  • Apply methods for visualization of data from a variety of fields;
  • Write code to create scientifically reproducible figures;
  • Distinguish good from bad visualizations;
  • Use basic principles of human perception and cognition in visualization;
  • Create some basic web-based interactive visualizations;
  • Learning to layout and visualize network data.
Assessment

Students are expected to attend lectures and hands-on sessions, to hand in one assignment during the course and to develop a project during the entire term.

Grading

  • Attendance of the classes and hands-on sessions: 30% of the final grade
  • Assignment(s): 30% of the final grade
  • Final project: 40% of the final grade

Final Project

For the final project, students will have to apply and show proficiency with the principles and tools used during the course. A few options for projects will be suggested in class.

Prerequisites

DNDS 6288 Data Analysis in Python

Course Level
Master’s
Course Open to
Students on-site
Academic Year
2023-2024
Term
Winter
US Credits
2
ECTS Credits
4
Course Code
DNDS 6002