CSE 6242 Data and Visual Analytics

Welcome to the course website for CSE 6242 Data and Visual Analytics, part of the Online Masters in Computer Science (OMSCS) program at Georgia Tech!

Note: The course website from Spring 2018 onwards will be hosted on Canvas. Please login there to get the most accurate information.

Overview

Data analytics are an important part of many academic, personal and professional endeavors. Whether you are evaluating the results of a human factors study, trying to understand your own finances a little better, or building a product recommendation engine–you will likely end up with a bunch of data to make sense of. Often the most useful analytics to begin with are simple summary statistics such as mean and variance, but for more complex tasks you may need to build statistical models of your data, e.g. using linear regression.

The ability to effectively convey insights gained from such analysis, often using engaging visualizations, is a highly sought-after skill. This requires a scientific approach to ensure accuracy and completeness, but also an element of creativity to tell an aesthetically appealing and compelling story about the data.

This course introduces you to data analysis and visualization techniques using the statistical programming language R. It will emphasize the practical side of things more than the underlying theory and math. You will need to understand some formulas and computations, but the course is geared towards preparing you for hands-on analysis, modeling and presentation, as opposed to researching new algorithms.

Prerequisites

This course assumes prior knowledge of:

  • Probability and Statistics
  • Linear algebra
  • Calculus

In addition, you must have some programming experience. Familiarity with any modern programming or scripting language should suffice. The course is taught in R, which will be introduced in the first module.

Syllabus

The course consists of 6 modules:

  • R Programming
  • Data Visualization
  • Data Processing
  • Logistic Regression
  • Linear Regression
  • Regularization

Assessments and grading may vary between offerings, so please refer to your semester’s resources below.

Semester Resources

Navigate to your specific semester for more information, including schedule and assessments: