The MS Analytics curriculum is a hybrid interdisciplinary data science/analytics curriculum structured to be completed in a single year (fall, spring, and summer) with a total of 36 credits required for each student. Trained by world-class faculty, MSA students learn the fundamentals of machine learning, visualization, data pipelining, statistical and operations research modeling, and application. Our graduates are experts at identifying and framing problems; acquiring, cleaning, and managing large and fast-moving streams of data; building, analyzing, and interpreting machine learning, statistical, and operations research models; and integrating these interdisciplinary skills to develop and execute important and valuable analytics successfully and data science projects.

The MS Analytics interdisciplinary core includes 15 credits of coursework across computing, statistics, operations research, and business. On top of this broad, integrated core, each student has 15 credits of electives to satisfy one of MSA’s specialized tracks and to gain advanced depth in specific areas of data science and analytics that best fit the student’s personal interests and career goals. The final piece of the MSA curriculum is an applied practicum course, in which each student works with a company or organization on a real data science or analytics project. Most of our MSA Atlanta students choose to take an internship or other job while registered for the practicum course, in order to gain more hands-on experience before graduating.

TEACHING METHODOLOGY

The MSA program also includes a “learning-how-to-learn” component. Our teaching material is state-of-the-art, but we know that a few years after you graduate there will be new techniques, software, languages, and platforms that didn’t exist when you were a student, so we train our students to quickly pick up new techniques, software, languages, and platforms as they are developed in the future. MSA students are also welcome to return and take additional courses after graduation, to stay current with the state-of-the-art throughout their careers.

Curriculum

Base Curriculum - All Tracks

  • (15 credit hours) Interdisciplinary data science/analytics core
  • (15 credit hours) Focused/specialized electives in data science/analytics
  • (6 credit hours) Applied Analytics Practicum (most students choose to take this course concurrently with an internship or other job)
  • Each student’s elective choices must satisfy the requirements of at least one of the defined tracks (Analytical Tools, Business Analytics, Computational Data Analytics)

Analytical Tools Track

The analytical tools track allows students to build on the interdisciplinary core curriculum to provide depth and specialization in data analytics, with a focus on machine learning, statistical, and operations research models. Depth electives include machine learning, data mining, regression, time series, Bayesian statistics, forecasting, optimization, simulation, stochastics, etc.

Business Analytics Track

The business analytics track allows students to build on the interdisciplinary core curriculum to gain a deeper practical understanding of the use of data science and analytics in business and industry, with a focus on understanding, framing, and solving problems in marketing, operations, finance, strategy, supply chain, management of information technology, human resources, accounting, etc.

Computational Data Analytics Track

The computational data analytics track allows students to build on the interdisciplinary core curriculum to provide depth and specialization in data science, including ML, deep learning, natural language, AI, visualization, databases, high-performance computing, etc. Depth elective options also include topics in data acquisition and data engineering. All students in this track are required to select at least one elective in machine learning.

Analytical Tools Track Requirements

  • CSE 6040 Computing for Data Analysis (can be replaced by an elective if you have sufficient background)
  • ISyE 6501 Introduction to Analytics Modeling (can be replaced by an elective if you have sufficient background)
  • MGT 8803 Introduction to Business for Analytics (can be replaced by an elective if you have sufficient background
  • CSE 6242 Data and Visual Analytics
  • MGT 6203 Data Analytics in Business
  • Five (5) electives in basic ML and statistical modeling (at least one) and operations research (at least one)
  • CSE/ISyE/MGT 6748 Applied Analytics Practicum (most students choose to take this course concurrently with an internship or other job)

Business Analytics Track Requirements

  • CSE 6040 Computing for Data Analysis (can be replaced by an elective if you have sufficient background)
  • ISyE 6501 Introduction to Analytics Modeling (can be replaced by an elective if you have sufficient background)
  • MGT 8803 Introduction to Business for Analytics (can be replaced by an elective if you have sufficient background
  • CSE 6242 Data and Visual Analytics
  • At least three (3) business analytics courses beyond the introductory core, including MGT 6203 Data Analytics in Business
  • Two electives in basic ML and statistical modeling, and one elective in operations research
  • CSE/ISyE/MGT 6748 Applied Analytics Practicum (most students choose to take this course concurrently with an internship or other job)

Computational Data Analytics Track Requirements

  • CSE 6040 Computing for Data Analysis (can be replaced by an elective if you have sufficient background)
  • ISyE 6501 Introduction to Analytics Modeling (can be replaced by an elective if you have sufficient background)
  • MGT 8803 Introduction to Business for Analytics (can be replaced by an elective if you have sufficient background)
  • MGT 6203 Data Analytics in Business
  • At least three (3) computing courses beyond the introductory core, including CSE 6242 Data and Visual Analytics (can also include CSE/ISyE Computational Data Analysis (Machine Learning), which must be taken as a computing elective or as a statistics elective)
  • Two electives in basic ML and statistical modeling (can include CSE/ISYE 6740 Computational Data Analytics (Machine Learning)), and one elective in operations research
  • CSE/ISyE/MGT 6748 Applied Analytics Practicum (most students choose to take this course concurrently with an internship or other job)

In addition to learning from world leaders and cutting-edge researchers in data science and analytics, students in the MSA Atlanta program will have access to a variety of specialized resources and opportunities, including:

  • Georgia Tech's state-of-the-art high-performance computing infrastructure for massive-scale analytics

  • Job and placement support through MSA Career Services

  • One-on-one academic and professional advising

  • Professional communication training

  • Free cloud computing resources

  • Free and discounted analytics, engineering, and productivity software

  • Professional development funding to attend conferences, training, etc.

  • Interview skills training and practice

  • Framing, storytelling, teamwork, and ethics training

  • Free and discounted certification training

  • Access to the MS Analytics Seminar Series

  • MSA “Fun Committee”!

Topics Covered

The MS Analytics core and elective courses cover a broad range of analytics and data science knowledge and skills. The table below shows most of the topics covered in core (required) courses and those that are available in elective courses. Note that elective courses often provide multiple topics simultaneously; for example, data mining, neural networks, association/classification, and statistical learning are closely-related topics and may be covered in a single course, along with appropriate software, computing resources, and advanced data skills.

Modeling and Analysis

Topic Core Elective
Association/classification x x
Basic probability x  
Bayesian data analysis   x
Causality x  
Continuous-time simulation   x
Data mining x x
Deep learning   x
Descriptive statistics x  
Discrete-event simulation x x
Distribution fitting x x
Forecasting x x
Integer optimization x x
Linear optimization x x
Linear regression x x
Machine learning x x
Markov chains x x
Data mining x x
Natural language processing   x
Neural networks x x
Nonlinear optimization x x
Pattern recognition   x
Social network analysis x x
Statistical inference x x
Statistical learning x x
Stochastic models x x
Text mining x x
Time series analysis x x
Uncertainty x x
Variability x x

Data and Computing

Topic Core Elective
Cloud computing x  
CPLEX   x
Data cleaning x  
Data exploration x  
Deep learning   x
Excel x x
Hadoop x x
High performance computing   x
High-volume data x x
Hive x x
Machine learning x x
Natural language processing   x
Neural networks   x
NoSQL x x
Parallel computing   x
Pig x x
Python x x
Relational databases x x
SAS x x
Scaling issues x x
Spark x x
SQL x x
Star schema   x
Stata   x
Tableau x x
Visualization x x
Web scraping x  

Business Context & Problem Solving

Topic Core Elective
Accounting  x  
Business processes  x x
Customer analytics  x x
Econometrics  x x
Finance  x x
Financial analytics    x
Law/ethics/privacy/security    x
Marketing  x x
Marketing analytics  x x
Negotiation  x  
Pricing/revenue management  x x
Problem definition  x x
Problem framing  x x
Problem solving  x x
Project management    x
Risk analytics    x
Strategy  x x
Supply chains  x x