M.S. in Data Analytics

M.S. in Data Analytics Program Information

Available online at Franklin University .

The Master of Science in Data Analytics (MSDA) at Franklin University is a fully online program that is designed for current and aspiring data analytics professionals and provides them with the knowledge and advanced skills necessary to support strategic organizational decisions based on data. As they progress in the program, students learn to manage, visualize and analyze complex data sets; apply a number of analytics methods to solve business problems and effectively communicate their results through a combination of interactive and relevant coursework. The capstone project gives them an opportunity to integrate and synthesize the skills and knowledge gained throughout the program. The MSDA at Franklin University offers the convenience and flexibility of a quality online education, expert instructors who have relevant and real-world experience, and strong student support from dedicated faculty, tutors and advisors.

Curriculum & Course Descriptions

32 Semester Hours
Major Area Required
MATH 601 - Introduction to Analytics (4)

This course provides an introductory overview of methods, concepts, and current practices in the growing field of statistics and data analytics. Topics to be covered include data collection, data analysis and visualization as well as probability, statistical inference and regression methods for informed decision-making. Students will explore these topics with current statistical software. Some emphasis will also be given to ethical principles of data analytics.

DATA 605 - Data Visualization & Reporting (4)

This course focuses on collecting, preparing, and analyzing data to create visualizations, dashboards, and stories that can be used to communicate critical business insights. Students will learn how to structure and streamline data analysis projects and highlight their implications efficiently using the most popular visualization tools used by businesses today.

DATA 610 - Big Data Analytics and Data Mining (4)

This course explores data mining methods and tools, examines the issues in the analytical analysis of massive datasets, and unstructured data. Students will learn the concepts and techniques to discover the patterns in large datasets, which support organizational decision making.

DATA 611 - Applied Machine Learning (4)

This course explores two main areas of machine learning: supervised and unsupervised. Topics include the fundamental concepts, roadmap of a machine learning project, classification algorithms, regression algorithms, dimensionality reduction, model evaluation, natural language processing, neural networks and deep learning, typical issues in real-world machine learning problems, and Python programming in data science.

DATA 612 - Computing for Data Analytics (4)

This course explores data mining methods and procedures for diagnostic and predictive analytics. Topics include association rules, clustering algorithms, tools for classification, and ensemble methods.

DATA 621 - Advanced Analytics (4)

This course examines the data analysis process using some advanced statistical methods. Students will improve their skills in data analytics and predictive analytics which will allow them to develop algorithmic methods for decision-making using popular industry software for data-driven solutions.

DATA 695 - Capstone (4)

The purpose of this capstone course in Data Analytics is to assess students' ability to synthesize and integrate the knowledge and skills they have developed throughout their coursework. The course provides students with the opportunity to demonstrate competency on the key domains of data analytics through a comprehensive project including problem scoping, data preparation and analysis, and a model development.

DATA 630 - Applied Database Management (4)

This course teaches data management from an applied perspective. The topics include fundamentals of database management systems, structured query language (SQL) for data analytics, relational database design, and data warehousing.