QMSS_G4063, Data Processing and Visualization Course at QMSS Columbia University

Spring 2016 Tuesdays 6:10pm - 8:00pm, IAB 413

Instructor:

• Navid Hassanpour

Teaching Assistants

• Daniel Kronovet
• Luronne Vaval

Coordinator

• [Kay Kastner](emailto: [email protected])

##Course Description:

This course is designed to equip QMSS students with tools from datascience for collecting, processing, and presenting new modes of data. The focus of the courseis on collection, processing, and visualization of novel modes of data (Big Data analysis in the popular discourse). Students will engage in the empirical analysis of a major real world devent, the U.S. Presidential Elections' Primaries during this course, in order to put the methods they learn into practice. During the course students are expected to work on a project that involves

• harvesting data from web platforms such as Twitter API,
• running analysis with social scientific import on the data they collected (R and Python), and
• using effective visualization methodology for

1. Interactive web-based design (RShiny and D3)
2. Social network analysis (D3 and RShiny)
3. GIS (D3 and RShiny)
4. Text analytics and visualization (in R)
5. Statistical analysis and visualization (R's ggplot2, RShiny).

Familiarity with relevant software tools would be a plus, but is not necessary as we will have tutorials on the essential software through the semester.

Part 0: Introduction, Visualizing Temporal Dynamics, Twitter API connection up and running

Week 1, January 19: Course Introduction, Visualization types - TwitterAPI set up

###Lecture 1

Part 1: Interactive Graphs with Twitter Data (Shiny, D3)

Week 2, 1.1, January 26: Shiny Apps, Word frequency plots, Time-series visualization

###Lecture 2

Week 3 & 4, 1.2 & 1.3, February 2 & 9: Interactive Visualization with Shiny & D3 using Twitter Data

###Lecture 3

###Lecture 4

Part 2: Social Network Analysis and Visualization

###Lecture 5

###Lecture 6

Part 3: GIS Analysis and Visualization

###Lecture 7

###Lecture 8

Part 4: Text Analysis and Visualization

###Lecture 9

###Lecture 10

Part 5: Statistical Visualization, Machine Learning & Prediction

###Lecture 11

###Lecture 12

Sample Final Reports

####Report1 ####Report2 ####Report3 ####Report4 ####Report5