Minor in Computational Data Analysis
The Computational Data Analysis minor will provide students with the necessary mathematical and statistical background to develop and apply various data analysis techniques to real world datasets. The minor has three main objectives related to knowledge, skills, and application:
- provide students with foundational knowledge of topics such as probability and statistics, algorithms and data structures to solve data analysis problems arising in practical applications,
- develop students' skill in software development techniques using one or more high level programming languages relevant to data analytics,
- enable students to effectively apply computational methods to solve exemplar data analysis problems arising in relevant applications.
Program of Study
This minor must comprise at least 15 credit hours, of which at least 9 credit hours are upper-division coursework (numbered 3000 or above).
| Code | Title | Credit Hours |
|---|---|---|
| Prerequisite | ||
| Introduction to Object Oriented Programming 1 | ||
| Required Courses | ||
| CX 4240 | Introduction to Computing for Data Analysis | 3 |
| CX 4242 | Data and Visual Analytics | 3 |
| Probability and Statistics | ||
| Select one of the following: | 3 | |
| Introduction to Probability and Statistics | ||
| Honors Probability and Statistics | ||
| Prob/Stats for ECE | ||
| Probability with Applications | ||
| Computational Methods | ||
| Select one of the following: | 3 | |
| Computational Problem Solving for Scientists and Engineers | ||
| Introduction to Database Systems | ||
| Introduction to Information Visualization | ||
| Electives | ||
| Select one of the following: | 3 | |
| Genomics and Applied Bioinformatics | ||
| Geomatics | ||
| Introduction to Perception and Robotics | ||
| Introduction to Database Systems | ||
| Introduction to Information Visualization | ||
| Computer Vision | ||
| Computational Problem Solving for Scientists and Engineers | ||
| Special Topics (Computational Sustainability) | ||
| Remote Sensing and Data Analysis | ||
| Environmental Data Analysis | ||
| Fundamentals of Digital Signal Processing | ||
| Introduction to Automation and Robotics | ||
| Computational Computer Vision | ||
| Special Topics (Game Theory and Multi-agent Systems) | ||
| Capital Investment Analysis | ||
| Stochastic Manufacturing and Service Systems | ||
| Financial Markets: Trading and Structure | ||
| Fixed Income | ||
| Applied Experimental Psychology | ||
| Total Credit Hours | 15 | |
| 1 | CS 1331 prerequisite for the minor required (this course does NOT count toward the 15 credit hours required for minor) and a grade of A or B is required |
- A CS Minor application is required
- No Special Problems or Internship coursework may be used towards the CS minor.
- All minor courses must be completed with a grade of C or higher.
- A maximum of 6 credit hours of Special Topics courses may be included in a minor.
- A maximum of 3 credit hours of transfer credit may be used to satisfy the course requirements for a minor. This includes courses taken at another institution or credit earned through the AP or IB program, assuming the scores meet Georgia Tech minimum standards.
- It is the major advisor’s responsibility to verify that students are using only courses from the designated block(s) from the student’s major field of study that are allowed to satisfy a minor program, that they are not using any Core Area A-E courses (including humanities and social sciences), and that they are not using any courses for more than one minor or certificate. Any free elective course used to satisfy the course requirements of the student’s major degree program may also be used to satisfy the course requirements for a minor.