COURSE CURRICULUM

The Master of Science in Data Analytics requires the completion of 36 credit hours, covering a series of introductory, reinforcement, proficiency, and subject-specific courses designed to provide comprehensive knowledge and skills in data analytics. The introductory courses lay the foundation with essential topics such as Basic Finance for Managers, Applied Business Statistics, and Analytics Programming with R. Students also study Social and Ethical Issues in Analytics to understand the responsibilities and implications of data use, along with Databases and Data Warehouses, which covers the storage and management of large datasets. Following the introductory courses, reinforcement courses build technical skills with Database Programming, Data Visualization, and Analytics Applications using Python. These courses equip students with the programming and analytical tools necessary for effective data handling and insight generation. Machine Learning for Predictive Analytics is included to introduce advanced techniques for forecasting and decision-making. Proficiency courses focus on specialized analytics techniques, including Time Series Analytics and Predictive Analytics, which enhance students’ ability to analyze data trends over time and create predictive models for real-world applications. The program culminates with the Analytics Practicum, a subject-specific course that offers hands-on experience through practical projects, allowing students to apply their knowledge in realistic business contexts. This structure ensures that graduates develop a strong theoretical foundation, technical expertise, and practical skills necessary to address complex data challenges. By integrating knowledge from statistics, programming, and business, the program prepares students to extract valuable insights from large and complex datasets, support informed decision-making, and contribute effectively to the growing field of data analytics across various industries.