StudyQA — Master: Analytics — Georgia Institute of Technology (Page 8)

Photos of university / #georgiatech

Description Contents Requirements Funding

The one-year Master of Science in Analytics is an interdisciplinary degree program that leverages the strengths of Georgia Tech in statistics, operations research, computing, and business by combining the world-class expertise of the Scheller College of Business, the College of Computing, and the College of Engineering. By blending the strengths of these nationally ranked programs, graduates will learn to integrate skills in a unique and interdisciplinary way that yields deep insights into analytics problems.

Why an Interdisciplinary Master’s in Analytics?

Analytics is an important, fast-growing field that has quickly become a key facet of business strategy. There is an increasing need for analytics-savvy employees who can think uniquely across disciplines to transform data into relevant insights for making better business decisions.

Georgia Tech's interdisciplinary approach to analytics gives students the opportunity to learn directly from top international authorities on business intelligence, developers of cutting-edge analytics techniques in statistics and operations research, and world leaders in big data and high-performance computing. Students will use advanced resources across campus such as Georgia Tech's state-of-the-art high-performance computing infrastructure for massive-scale data analytics, work in cross-disciplinary teams to solve real analytics problems for a range of companies and organizations, and more. It all adds up to a unique ability to generate deeper insights into analytics problems.

With the Georgia Tech Master’s in Analytics degree, graduates will enter the workplace with the computing, business, statistics, and operations research skills needed to immediately identify, analyze, and solve analytics problems for better business intelligence and decision support.

Networking and Career Placement

One of the central objectives of the program will be to produce and place graduates ready to make both immediate and long-term impacts in business, industry, and government. In addition to making contacts with leading analytics organizations during the course of the program, students will be funded to attend a major analytics conference, gain valuable exposure at Georgia Tech's Big Data Industry Forum, and be supported in their job search by a dedicated professional.

The curriculum will also facilitate internal connections. To establish a strong professional network within each cohort, students will take several courses together, developing interdisciplinary working relationships and forging connections that can be relied upon throughout their career.

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.