M.S. in Business Analytics Program Information
Available onsite at Franklin University , online at Franklin University .
|COMP 630||R||I, R|
|DATA 605||R, A|
|DATA 610||I, R, A|
|BUSA 604||R, A|
Develop foundational statistical and data analytics skills applicable to business decision making and problem solving
Collect, clean, prepare, and visualize data to support fact-based decision making
Apply descriptive, predictive, prescriptive and diagnostic approaches to analyzing structured and unstructured data for specific business-related scenarios
Model and Interpret results from analytics using business-relevant and applicable scenarios
Present and communicate analytics results in ways that are visually appealing and applicable to business scenarios
This course provides an introductory overview of methods, concepts and current practices in the growing field of Data Analytics. Topics to be covered include data collection, analysis and visualization as well as statistical inference 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.
This course focuses on the fundamental design considerations in designing a database. Specific topics include performance analysis of design alternatives, system configuration and the administration of a popular database system. The course also offers an in-depth analysis of the algorithms and machine organizations of database systems. Note, this course has proctored exam(s). This exams requires additional technology, if student uses online proctoring.
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.
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.
This course covers the application of analytics tools, techniques, strategies and methods to marketing management. Students learn to analyze market data, enabling management decisions to be based on data-driven facts and customer insights. Using marketing analytics tools to model scenarios, students learn how organizations can measure returns on investment relative to their marketing efforts, drive performance and strengthen the effectiveness of its campaigns.
This course is built on the theory, strategy and practice of financial management, emphasizing computer-based modeling and forecasting. Students learn to model financial scenarios using analytics tools. The impact of financial decisions relative to financial statements analysis, cash budgeting, cost of capital determination, capital budgeting, and capital structure choices are covered. A variety of techniques, such as sensitivity and scenario analysis, optimization methods, Monte Carlo simulation, and regression analysis are also covered.
This course taken prior to the Capstone in Business Analytics course allows students to apply the knowledge gained from the previous courses in the development of an analytics strategy for a business of choice. Given a range of options, students will research and choose the best analytics strategy under given scenarios. The course uses case studies, employing a problem and project-based approach to the development of a strategy.
Students demonstrate an integrative knowledge of analytics in this course by developing a project plan to implement analytics for an important function, unit or department of the organization chosen in the Business Analytics strategy course. Students apply analytics tools, techniques, methods and strategies to drive business outcomes for the chosen company using relevant project-based methodologies. The course allows students to develop a professional portfolio that will highlight the work completed throughout the degree program. This may serve as a relevant employability resource.
This course introduces the student to statistics with business applications. The course covers both descriptive and inferential statistics. Topics included are: measures of central tendency; measures of dispersion; graphical displays of data; linear regression; basic probability concepts; binomial and normal probability distributions; confidence intervals; and hypothesis testing. These topics will be covered using a basic knowledge of algebra and Microsoft Excel. Note, this course has proctored exam(s).
Complete the above course or the equivalent from an accredited school. Prerequisite must be completed with a grade of C or better.