CSE 6242: Data and Visual Analytics

Instructional Team

Polo Chau

Prof. Duen Horng (Polo) Chau
Instructor
Matthew Hull

Matthew Hull
Head TA
Susanta Routray

Susanta Routray
Head TA

Overview

This course will introduce you to broad classes of techniques and tools for analyzing and visualizing data at scale. It emphasizes how to complement computation and visualization to perform effective analysis. We will cover methods from each side, and hybrid ones that combine the best of both worlds. Students will work in small teams to complete a project exploring novel approaches for interactive data & visual analytics.

More information is available on the CSE 6242 course website.

This course counts towards the following specialization(s):
Machine Learning

Foundational Course           Machine Learning Specialization Elective

Course Goals

In this course, you will:

  • Learn visual and computation techniques and tools, for typical data types.
  • Learn how to complement each kind of methods.
  • Gain a breadth of knowledge.
  • Work on real datasets and problems.
  • Learn practical know-how (useful for jobs, research) through significant hands-on programming assignments.

Preview

Sample Syllabus

Fall 2019 syllabus and schedule

Note: Sample syllabi are provided for informational purposes only. For the most up-to-date information, consult the official course documentation.

Before Taking This Class...

Suggested Background Knowledge

It is recommended that you:

  • Have taken CSE 6040 and did very well in it (for Georgia Tech Analytics students)
  • Have taken CS 1301 (for Georgia Tech Analytics students)
  • Are proficient in at least one high-level programming language (e.g., Python, C++, Java) and are efficient with debugging principles and practices; if you are not, you should instead first take CSE 6040 (for OMS Analytics students) and if needed, CS 1301 and CS 1371.
  • Are confident in your ability to learn multiple tools, skills, and programming languages on the fly.
  • Have basic knowledge of linear algebra, probability, and statistics

Technical Requirements and Software

  • 8 GB RAM (16 GB recommended)
  • 512 GB disk (SSD recommended)
  • Dual-core Core i5 (8th generation or better recommended)

Academic Integrity

All Georgia Tech students are expected to uphold the Georgia Tech Academic Honor Code. This course may impose additional academic integrity stipulations; consult the official course documentation for more information.