WDV-LS88-F17

This is the repository for L&S - 88: Web Data Visualization

This project is maintained by yasmina85

L&S88: Web Data Visualization

Fall 2017
CCN: 46820 Instructor: Yasmin AlNoamany
Email: yasminal@berkeley.edu
Lecture: Tuesdays 12:00-2:00 pm, Cory 105
Office Hours: Tuesdays, 2:00-3:00 pm, Cory 105

Course Description

The course introduces students to Web science with a focus on how the Web works, types of Web data, and how to generate effective visualizations for Web data (e.g., social networks). The course covers basic principles and tools for understanding and visualizing Web data. It focuses heavily on project work that aims to give students hands-on experience with handling Web data.

Because Web science is interdisciplinary by nature, this course connects students to different disciplines such as social science, computer science, and information science. At the end of the course, students should be able to apply Web mining techniques on and draw insights from real-world data.

Calendar

Date Topic Resources Activity/ Lab Assignment
August 29 Intro to Web Data Visualization - History of the Web
- Evolution of the World Wide
- Ch 1 of Visualization Analysis and Design by Tamara Munzner
- The Value of Visualization
- Ch 1 of Computational and Inferential Thinking
  Assignment 1 (Due Sept. 5)
September 5 Web Data Working with Data on the Web (tutorial)
Intro to Web Scrapping (A Notebook)
Working with APIs: Data and Twitter
Working with APIs: Accessing Databases via Web APIs
Web Mining : Accomplishments & Future Directions (pdf), Visualize This, Chapter 2: Handling Data (pdf)
Intro to NJ;
Web Data Formats;
Web APIs
Assignment 2 (Due Sept. 19)
September 12 Systems and Toolkits for Visualizing Web Data Part 1 (Data Analysis and Visualization in R from Data Carpentry lessons
Producing Simple Graphs with R
Web Scraping in Python
What I Learned Recreating One Chart Using 24 Tools by Lisa Charlotte
Web Scraping
Data Cleaning
Intro to R
Semester Project
September 19 No Lecture     Assignment 2 Due
September 26 Systems and Toolkits for Visualizing Web Data Part 2 Data Analysis and Visualization in R
Producing simple graphs with R
Getting started with charts in R
Get started with Tableau Desktop
Data Analysis and Visualization in R
Notebook
Assignment 3 (Due Oct. 10)
October 3 Visualizing Text Documents Ch 9 of Interactive Data Visualization book
Working with Text Data
Text preprocessing notebooks in python from Stanford
Ch. 10 of Search user interfaces
Ch. 11 of Search user interfaces
Practical Data Mining with Python
Tag Cloud vs. Wordle PM1- Pitches
October 10 Visualizing Text Documents Slides 30-70   Tag Cloud and Wordle solutions
Text Analysis
Assignment 4 (Due Oct. 26)
October 17 Project Proposals
Social Networks
All about social network analysis- Mining the social web
Introduction to Network Analysis and Visualization by Martin Grandjean
Lectures by Jennifer Goldbeck, the author of Analysing the Social Web book
Network degree PM2 Due
October 24 Albert Cairo’s Talk     Assignment 5 (Due Oct 31
October 31 Guest Lecture     Assignment 5 due
November 7 Social Networks 30-end
Web Usage Data
Chapter 12: Web Usage Mining by B. Mobasher
Access Patterns for Robots and Humans in Web Archives
Guest Lectures evaluation PM3 (moved to Nov. 13)
November 14 Designing Effective Visualizations Effectively Communicating Numbers, Few 2005
A Tour through the Visualization Zoo, Heer 2010
Data Visualization Principles: Lessons from Tufte
Critique an existing visualization  
November 21 Telling Stories from the Past Web</br> Projects Feedback http://bit.ly/YasminPhD Projects feedback  
November 28 Project Presentations   Project Peer Evaluation Report Due Project presentations and demos
December 5 Course wrap-up and evaluation      

Grading

  1. Attendance and Participation 10%
  2. Class activity 15%
  3. Assignments 40%
  4. Semester project 35%
    • Milestone 1 (dataset and task definitions) - 4 points
    • Milestone 2 (project proposal) - 7 points
    • Milestone 3 (project status update) - 4 points
    • Demo and Presentation - 10 points
    • Paper - 10 points

We will follow the default grade scale at UCB. All grades will be posted on bCourses.

Late policy:

Late assignments lose 1 points for every 24 hours they are late. Students should contact the instructor if they have an excuse. No assignment will be accepted more than 7 days late.

Attendance:

Attendance is required for class participation and discussion. Contact instructor prior to the class if you have a reason for the absence.

Participation

Participation will in the form of the discussion in the class and/or questions or comments on required readings of the previous week. There will be a discussion forum for each week readings on bCourses. Students are supposed to submit their comments/questions on readings as a reply to the discussion.

Collaboration and Academic Honesty

We encourage collaboration and working in teams. However, homework assignments should be completed independently. You can discuss assignments with others with mentioning that at the bottom of your assignment. We will be following the EECS departmental policy on Academic Honesty, so be sure you are familiar with it.

Seeking Help:

If you have questions, first check the course Web site. Feel free to ask in the class, or post your question on bCourses. There will be office hours on Tuesdays, from 3:00-4:00 pm. If students can not make it to the office hours, they can schedule an appointment by email: yasminal@berkeley.edu.

Disability accommodations:

If you experience a disability which will impact your ability to access any aspect of the class, please contact me and check out the university policy of accommodating special needs from here: http://www.dsp.berkeley.edu/accommodationpolicy