Social Media Analytics

MIS 373 04260, offered in Spring 2017
Schedule: T Th 2:00-3:30 pm in UTC 1.116
TA: Sam Blazek (blazeks@utexas.edu)
Office hours: Wed 2:00-3:00 pm or by appointment
TA office hours by appointment
Pre-requisites: None
Syllabus PDF

This course is split into five main parts:

  1. Network Patterns, which describes and seeks to explain several common patterns found in real-world social networks,
  2. Branding and Community, which explores the best methods for maintaining a strong brand online and managing the user community,
  3. Importance and Influence, which discusses an individual's place in the network, and how memes, early adoption, and such cascades propagate,
  4. Advertising and Marketing, which focuses on viral marketing and social advertising techniques, and
  5. Advanced Analytics, which describes the latest methods for inferring user interests and recommending items to them, and related topics.
A tentative schedule is in the syllabus.

Books:

  • Networks, Crowds, and Markets, by Easley and Kleinberg (see here)
  • Networks: An Introduction, by Newman
Case studies will be available in an online coursepack here. Slides will be made available on Canvas.

1 Group assignment 10%
1 Group assignment with presentation 20%
Group project with presentation 25%
Midterm 20%
Final exam 25%

Advanced Analytics Programming

MIS 373 04235, offered in Spring 2017
Schedule: T Th 12:30-2:00 pm in UTC 1.116
Office hours: Wed 1:00-2:00 pm or by appointment
Pre-requisites: MIS 304 (Intro to Programming)
Syllabus PDF

This course is split into five main parts:

  1. Introductory Python, where we learn the basic language syntax, and gain familiarity with general-purpose tools such as string manipulation,
  2. Pandas, which is a powerful data analysis toolkit (similar to R) that makes it easy to explore and visualize data,
  3. Classification, where we develop an understanding of how to make predictions,
  4. Clustering, where we learn how to discover the major groups or components of a given dataset, and
  5. Other Topics, including regression and hypothesis testing.
A tentative schedule is in the syllabus.

Books:

  • Think Python, by Downey (see here)
  • Python for Data Analysis, by McKinney
Slides will be made available on Canvas.

3 Group assignment 30%
Group project with presentation 20%
Midterm 20%
Final exam 30%

Data Analytics Programming

MIS S381N 71935, offered in Summer 2015
Schedule: M T W Th 1:00-3:00pm in GSB 5.142A
TA: Jingshi Sun (hanzi@utexas.edu)
Office hours: Monday 3:00-4:00pm or by appointment
TA office hours by appointment
Syllabus PDF

This course is split into five main parts:

  1. Introductory Python, where we learn the basic language syntax, and gain familiarity with general-purpose tools such as string manipulation,
  2. Pandas, which is a powerful data analysis toolkit (similar to R) that makes it easy to explore and visualize data,
  3. Classification, where we develop an understanding of how to make predictions,
  4. Clustering, where we learn how to discover the major groups or components of a given dataset, and
  5. Other Topics, including regression and hypothesis testing.
A tentative schedule is in the syllabus.

Books:

  • Think Python, by Downey (see here)
  • Python for Data Analysis, by McKinney
Slides will be made available on Canvas.

2 Group assignment 30%
Group project with presentation 30%
Final exam 40%