Syllabus: Introduction to Data Visualization

A free, open online course at MulinBlog Online J-School

(This syllabus is available for download by following this link)

Course instructor:  Mu Lin
Email: mlin@mulinblog.com
Twitter: @mututemple (Digital Journalism)
Blog: http://www.mulinblog.com
Facebook page: https://www.facebook.com/mulinblogjschool

ABOUT THIS COURSE

This course is designed for people who want to acquire working knowledge of data visualization. Topics discussed include what data visualization is, common types of data visualization, how to choose the right visualization for a data set, and how to create visualizations using popular tool and software programs.

HOW THIS CLASS WORKS

  • This course consists of four weekly modules; in each weekly module, there are readings, quizzes, assignments and class discussions.
  • There is no required textbook for this course. This course makes use of selected online articles/tutorials as lesson materials.
  • You do not need to buy any software for this class; we’ll use three free digital data tools: infogr.am, Google Fusion Tables and Tableau Public.
  • For a satisfying learning experience for both you and your classmates, we ask that you work on the assignments and actively participate in class activities.
  • A course completion certificate will be issued to students who satisfactorily complete all the required coursework.

WEEKLY SCHEDULE

Course structure and contents will be updated periodically to reflect the best and most up-to-date industry practices. (Last updated in December 2014)

Week 1: Getting Started

Learning objectives: After finishing this module, students will be able to:

  • tell 5 reasons why data visualization is desirable
  • tell popular resources for open data on the web
  • conduct a search for open data

Class activities: study the lesson and reading; participate in class discussions; examine sample datasets for hidden messages; practice searching public data.

Week 2: How to choose the right visualization

Learning objectives: After finishing this module, participants will be able to

  • explain common types of visualization
  • identify the best visualization type for a dataset

Class activities: study the lesson; examine sample data sets and choose the right chart; participate in class discussions.

Week 3: Create basic data visualization

Learning objectives: After finishing this module, participants will be able to

  • extract raw data from web sources using import.io
  • clean up a raw dataset for use in visualization programs
  • describe common design issues in a visualization
  • create and share visualizations using infogr.am and Google Spreadsheets

Class activities: study the lesson and reading; re-create demo projects following the tutorials; extract data sets using import.io; create and share visualizations using Google Spreadsheets and infogr.am; participate in class discussions.

Week 4: Advanced visualization with Tableau Public

Learning objectives: After finishing this module, participants will be able to

  • tell what Tableau is and does
  • navigate Tableau interface
  • create and share interactive visualization projects

Class activities: study the lesson and reading; take a quiz; create demo project following the tutorials; create and share a visualization dashboard; participate in class discussions.

Last modified: Friday, December 26, 2014, 12:51 PM