Four Courses in GaTech's MS - Analytics

Four Courses in GaTech's Master of Science, Analytics:

This year I completed my first four courses in Georgia Tech's Master of Science – Analytics degree. Generally, I have been very impressed with the planning and course structure. Each core course aligns well with the others and it seems that anytime a deeper question about content in one course arises, it is answered in another class.

For instance, In ISYE 6501 we did a project on Principle Component Analysis using the prcomp library in R. Just a few weeks later, CSE6040 had us implementing PCA using numpy. Each course has provided tremendous value and the coursework has left me feeling prepared to apply what was learned outside of an academic setting.

Core Coursework completed:

• ISYE6501: Introduction to Analytics Modeling
• CSE6040: Computing for Data Analysis
• MGMT8803: Business Fundamentals for Analytics

Electives completed:

• CS6400: Database Systems Concepts and Design

ISYE6501: Introduction to Analytics Modeling

This course typically covered a single topic with a lab/report due each week. It moved quickly, but the labs were challenging enough to require an understanding of the lecture material.

Course topics included:

• Classification
• Validation
• Clustering
• Change Detection
• Time Series Models
• Regression
• Variable Selection
• Design of Experiments
• Missing data/Imputation
• Optimization

This course does not get too in-depth with the mathematics of these models. However it does required some mathematical intuition and programming.

Suggested Pre-reqs: Linear Algebra, Calculus, an Intro CS course (understand functions, looping, etc.)

Hours of study per week: 12-15

CSE6040: Computing for Data Analysis

This course is the introduction to computer science for MSA students. If you have never taken a CS course before this course will move at an incredibly fast pace. The first few weeks are a rapid introduction to Python covering data structures and syntax. Each week a programming assignment is due.

The assigments quickly go from basic to challenging depending on your background. Early on we were web scraping information from a Yelp review search, and by the end, we were implementing image compression in numpy using PCA and Singular Value Decomposition.

Tests in this course are 24-36 hour time trials. The tests are open notes and open internet. On average the tests took me about 15 hours of work to complete with a good grade.

Suggested Prereqs: Calculus, Linear Algebra, working knowledge of Python

Hours of study per week: 8-15 (+15 hours for test weeks)

CS6400: Database Systems Concepts and Design

Dr. Leo Mark definitely has a wealth of knowledge when it comes to DB systems. The reading and lectures for CS6400 primarily cover the finer details of relationships between data and how relationships are models of the world.

In addition to the reading and lecture material, we had a three phase group project with presentation. This semester, the project was to create an early stage mishmash pinterest/facebook clone. We ended up successfully deploying our project using: AWS, Javascript(Angular), Java, MySQL.

Primary topics were:

• Relational mapping
• Relational Algebra/Calculus
• SQL
• Database Normalization

Suggested Prereqs: General Software development, Web development experience

Hours of study per week: 15

MGMT8803 Business Fundamentals for Analytics

MGMT8803 Covers a range of business topics including: Financial accounting, managerial accounting, financial analysis, entreprenurial finance, marketing, and business strategy. Overall, this course was mediocre at building knowledge in these topics.

However, it did one thing very well. This course left a profound impact on my perspective for business. If the objective of the professors was to leave their student with a sense of the goals, motivations, and limitations for business management and decision making, then they were successful in me.

Hours of study per week: 5-9