M.S. in Data Analytics Program Information
Available online at Franklin University .
A learning outcome map functions as a roadmap to help guide students' progress through their program of study. Click HERE to view the M.S. Data Analytics matrix.
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.
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 explores two main areas of machine learning: supervised and unsupervised. Topics include linear and logistic regression, probabilistic inference, Support Vector Machines, Artificial Neural Networks, clustering, and dimensionality reduction, and programming.
This course explores the methods of analytics computing and the procedures for diagnostic and predictive analytics. Topics include data manipulation, clustering algorithms, and regression methods using basic programming techniques.
This course examines the data analysis process with the emphasis of quantitative and qualitative findings from data. Students will develop skills in data analytics methods and predictive analytics that will allow them to develop algorithmic methods and use them along with popular industry software for data-driven solutions.
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.
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.
Complete the above course or the equivalent from an accredited school. Prerequisite must be completed with a grade of C or better.