Curriculum for MS in Data Analytics

This course focuses on the art and science of transforming raw data into insightful visual narratives through a blend of theory and practical applications. Students will gain the skills needed to distill complex data into impactful visuals that drive understanding, decision-making, and action in a variety of professional contexts.

This course explores the key concepts, methodologies, and technologies underpinning modern business intelligence practices. Whether you're a seasoned analyst or a business leader seeking to unlock the power of data, this course equips you with the knowledge and tools to understand the analytics process and excel in today's data-driven business environment.

This course is designed to equip students with the advanced skills and techniques necessary to harness the full potential of spreadsheets in business. Students will develop foundational spreadsheet knowledge and then delve into sophisticated data analysis, modeling, and visualization methods using industry-standard technology.

This course focuses on data mining. This includes predictive analytics (the process of using historical and current data to make predictions about future events), cluster analysis (market segmentation), and association analysis (market basket analysis).

This course focuses on the fundamentals of database management and database design. Topics include relational databases, SQL queries, reports and other interfaces to database data, plus documentation.

This course will immerse students in the dynamic intersection of business strategy and analytics. Students will learn to align analytics initiatives with organizational objectives as well as leverage data to drive innovation and gain competitive advantages.

The purpose of this course is to provide the students an understanding of the concepts of unstructured data analysis. It is estimated that at least 80% of an organization's useful data is stored in unstructured formats such as emails, memos, free-form surveys, etc. Various techniques to analyze this data will be covered.

This course introduces students to supervised and unsupervised AI machine learning models using industry-standard platforms. Students apply algorithms such as decision trees, random forests, K-nearest neighbor, support vector machines, neural networks, naïve Bayes, clustering, association rule mining and bootstrapping to analyze data and make decisions. The emphasis is on practical AI application and AI assisted interpretation as well as use of large language models (LLM) for communication of results. No prior coding experience is required.

This course focuses on advanced topics in data analytics currently in use in both technology and artificial intelligence today. This includes modern data science practices utilized in both data mining and machine learning.

Effective project management is essential for delivering successful data analytics initiatives. This course provides a comprehensive exploration of project management methodologies, tools, and best practices tailored to the unique challenges of data analytics projects. Students will learn to engage with stakeholders, manage resources, and implement Agile frameworks such as Kanban and Scrum to enhance team collaboration and efficiency. Topics include project scheduling, documentation, risk management, and resource allocation, ensuring students develop the skills needed to lead and execute data-driven projects effectively. Through case studies and hands-on exercises, students will gain practical experience in managing real-world analytics initiatives from initiation to delivery.