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Master of Science in Analytics

Data analysts are forging new relationships in virtually every discipline: business, healthcare, geology, mathematics and statistics, biology, chemistry, computer science, information systems and technology, engineering, psychology, behavioral science, operations research, and more, in addition to potential interactions between these disciplines, using role-based interaction with information and analytics to enable highly- collaborative, data-driven organizations. The graduate of this program enters the workforce prepared for the complex, information-intensive world.

About The Programme

Graduates of the Master of Science in Analytics program will be able to:

Identify and assess the opportunities, needs and constraints for data usage;
Make clear and insightful analyses changing direction quickly as required by these analyses;
Measure, evaluate, and explain the level of quality of a dataset and develop a plan to improve the quality;
Work effectively in a team to develop data analytic solutions;
Recognize and analyze ethical issues related to intellectual property, data security integrity, and privacy; and
Communicate clearly and persuasively to a variety of audiences.

Graduates become data scientists and analysts in finance, marketing, operations, and business intelligence working groups that generate and consume large amounts of data.

Course Details

Program Courses - 36 Credit Hours

This program requires a total of 36 semester hours: 15 semester hours from the core courses, 6 semester hours of experiential courses, and 15 semester hours of Concentration courses. The semester hour value of each course appears in parentheses ( ).

ANLY 500 – Analytics I: Prin & Applicatiions (3 credits)
ANLY 502 – Mathematical Foundations for Data Analysis (3 credits)
ANLY 505 – Data Simulation, Bayesian Modeling, and Inference (3 credits)
ANLY 506 – Exploratory Data Analysis (3 credits)
ANLY 510 – Analytics II: Principles and Applications (3 credits)
ANLY 512 – Data Visualization (3 credits)
ANLY 515 – Risk Modeling and Assessment (3 credits)
ANLY 520 – Natural Language Processing: Text Summarization and Classification (3 credits)
ANLY 525 – Quantitative Decision Making (3 credits)
ANLY 530 – Principles and Applications of Machine Learning (3 credits)
ANLY 535 – Principles and Applications of Deep Learning (3 credits)
ANLY 540 – Natural Language Processing: Semantic Representations (3 credits)
ANLY 545 – Categorical Data Analysis (3 credits)
ANLY 560 – Advanced Programming for Data Analytics (3 credits)
ANLY 620 – Ethics for Data Analytics (3 credits)
GRAD 695 – Research Methodology & Writing (3 credits)