StudyQA — Master: Analytics — Georgia Institute of Technology (Page 79)

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Description Contents Requirements Funding

Analytics is a comprehensive degree program offered by the Georgia Institute of Technology designed to equip students with the essential skills and knowledge to excel in data-driven decision making across diverse industries. This program provides a rigorous curriculum that blends theoretical foundations with practical applications, ensuring graduates are well-prepared to meet the growing demand for data analysts, business intelligence specialists, and data scientists in today’s competitive marketplace.

Students enrolled in the Analytics program gain a deep understanding of statistical methods, data management, and computational techniques necessary for analyzing complex datasets. The curriculum covers foundational topics such as probability, statistics, and data analysis, alongside advanced subjects like machine learning, predictive modeling, big data technologies, and data visualization. Emphasis is placed on developing proficiency in modern programming languages and tools commonly used in the industry, including Python, R, SQL, and Tableau.

The program is designed not only to develop technical competencies but also to enhance critical thinking, problem-solving abilities, and effective communication skills. These attributes are essential for translating data insights into strategic business decisions. To facilitate experiential learning, students have access to state-of-the-art laboratories, collaborative projects with industry partners, and internship opportunities that provide real-world experience. The program also encourages innovation through coursework, research initiatives, and participation in analytics competitions.

Graduate students will benefit from the faculty’s expertise, which includes renowned researchers and industry professionals dedicated to mentoring aspiring analysts. The degree prepares graduates for careers as data analysts, analytics consultants, operations analysts, or data scientists in sectors such as finance, healthcare, technology, consulting, and government agencies.

Overall, Georgia Tech’s Analytics program is committed to fostering a rigorous academic environment that promotes technical mastery, ethical data practices, and leadership in the analytics field. Graduates will be equipped to harness the power of data to solve complex problems, improve processes, and contribute to data-driven strategic initiatives within their organizations.

The MS Analytics curriculum is structured to be completed in a single year (fall, spring, and summer), with a total of 36 credit-hours required for each student. Trained by world-class faculty, students will learn identification and framing of problems; acquisition, management, and utilization of large and fast-moving streams of data; creation, analysis, solution, and interpretation of mathematical models using appropriate methodology; and the integration of these interdisciplinary skills to enable graduates to successfully develop and execute analytics projects.

The interdisciplinary core includes 15 hours of coursework across business, computing, statistics, and operations research. On top of this integrated breadth of study covering the core areas of analytics, each student has 15 hours of electives to satisfy one of the specialized tracks to give them depth in an analytics area of specialization: Analytical Tools, Business Analytics, and Computational Data Analytics. Each student's elective choices can be personalized to support their individual career goals. The final piece of the curriculum is an applied analytics practicum, in which students will work with companies and organizations on real analytics problems.

Required core and capstone courses

*CSE 6040 Computing for Data Analytics
*ISyE 8803 Introduction to Analytics Models
*MGT 8803 Introduction to Business for Analytics
CSE 6242 Data and Visual Analytics
MGT 6203 Data Analytics in Business
CSE/ISyE/MGT 6748 Applied Analytics Practicum

Statistics courses

CSE/ISyE 6740 Computational Data Analytics (Machine Learning)
ISyE 6402 Time Series Analysis
ISyE 6404 Nonparametric Data Analysis
ISyE 6405 Statistical Methods for Manufacturing Design/Improvement
ISyE 6412 Theoretical Statistics
ISyE 6413 Design of Experiments
ISyE 6414 Regression Analysis
ISyE 6416 Computational Statistics
ISyE 6420 Bayesian Statistics
ISyE 6783 Financial Data Analysis
ISyE 6810 Systems Monitoring and Prognostics
ISyE 7406 Data Mining and Statistical Learning

Computing Courses

CSE 6010 Computational Problem Solving for Scientists and Engineers
CSE 6140 Computational Science and Engineering Algorithms
CSE 6141 Massive Graph Analytics
CSE 6220 High Performance Computing
CSE 6230 High Performance Parallel Computing
CSE 6240 Web Search and Text Mining
CSE 6241 Pattern Matching
CSE/ECE 6730 Modeling and Simulation: Fundamentals and Implementation
CSE/ISYE 6740 Computational Data Analysis (Machine Learning)
CSE 8803 Big Data in Healthcare
CS 6400 Database Systems Concepts and Design
CS 7450 Information Visualization
CS 7646 Machine Learning for Trading
CS 8803 Visual Data Analysis

Business courses

MGT 6057 Business Process Analysis and Design
MGT 6304 Customer Relationship Management
MGT 6310 Marketing Research
MGT 6400 Pricing Analytics and Revenue Management
MGT 6450 Project Management
MGT 8803 Risk Analytics
MGT 8803 Business Forecasting
MGT 8803 Marketing Analytics and Pricing Strategy
MGT 8803 Business Analytics Practicum
CS/MGT 6725 Information Security Strategies and Policy
CS/MGT 6726 Privacy, Technology, Policy and Law

Operations research courses

ISyE 6333 Operations Research I
ISyE 6334 Operations Research II
ISyE 6644 Simulation
ISyE 6650 Probabilistic Models
ISyE 6663 Nonlinear Optimization
ISyE 6669 Deterministic Optimization
ISyE 6679 Computational Methods

Applications of analytics courses

CP 6514 Introduction to Geographic Information Systems
ISyE 6201 Manufacturing Systems
ISyE 6202 Warehousing Systems
ISyE 6203 Transportation and Supply Chain Systems
ISyE 6230 Public Impact Applications of OR
ISyE 6335 Supply Chain Engineering I
ISyE 6336 Supply Chain Engineering II
ISyE 6337 Supply Chain Engineering III

Requirements

Admission to the MS in Analytics is highly selective. We are looking for exceptional students with a strong interest in analytics and a high level of ability that has been demonstrated by past performance on appropriate coursework and/or by workplace experience as well as standardized testing. All applicants are expected to have basic background in mathematics (at least one college-level course or equivalent knowledge in calculus, and probability/statistics) and computing (at least one college-level course or equivalent knowledge in computer programming using a high-level language like C, C++, Java, Python, FORTRAN, etc.), as well as a four-year bachelor's degree or equivalent. Applicants must submit either GRE or GMAT scores, a professional resume, a personal statement, and contact information for three people who will submit letters of recommendation. TOEFL scores (minimum score of 100) are also required for international applicants; Georgia Tech does not accept IELTS.

Applicants who do not have sufficient background in mathematics and computing may still be admitted, with the expectation that they either learn the necessary background material on their own before arriving, or take one or more preparatory courses such as Math 1712, ISyE 6739, and/or CS 1301 when they arrive on campus.