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Applied Data Science

The MS in Applied Data Science degree from Bay Path University consists of 36 credits, 12 courses, that are offered completely online or a combination of online and on campus. Most students complete the degree in 12 to 24 months while continuing to work full-time. The degree positions you for an array of data science opportunities available across every industry. 

The degree focuses on the knowledge and skills required to analyze large, complex data sets in the context of real-world problems, applying industry-specific tools that generate actionable intelligence for decision-making and communicating the results using advanced visualization applications.

Curriculum & Schedules

Code Course Name Credit Hours
ADS531 Applied Linear Algebra 3

This course covers important concepts in linear algebra with emphasis on statistical applications. Topics include vector spaces, inner products, orthogonality, matrix decomposition/factorization, least squares, linear models, principal component analysis, Markov processes.

ADS532 Applied Statistics I 3

This is an introduction to the foundation and applications of statistics. Topics include basic probability concepts, limit theorems, data sampling, experimental design, estimation of parameters, hypotheses testing, ANOVA and linear regression. The course will use R or similar software.

ADS533 Applied Statistics II 3

This course covers additional topics in the applications of statistics. Topics include multivariate analysis, more on regression methods, categorical data analysis, more on ANOVA, time series analysis, bootstrap, simulation methods. The course will use R or similar software.

ADS635 Data Mining I 3

This course will be an introduction to data mining. Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. Topics including feature selection, shrinkage methods, discriminant analysis, regularization methods, kernel smoothing methods and model assessment, all with hands-on application utilizing statistical packages and programming languages.

ADS636 Data Mining II 3

This course will cover additional topics in data mining including tree-based methods, neural networks, support vector machines, cluster analysis, graphical models, ensemble learning and text mining, all with hands-on application utilizing statistical packages and programming languages.

ADS637 Data Exploration and Visualization 3

This course is an introduction to data visualization. It includes data pre-processing and focuses on specific tools and techniques necessary to visualize complex data. Data visualization topics covered include design principles, perception, color, statistical graphs, maps, trees and networks, data visualization tools, and other topics as appropriate. Visualization tools may include Tableau, Python, and R, etc. The course introduces the techniques necessary to successfully implement visualization projects using the programming languages studied. 

ADS670 Case Analysis Capstone 3

This is a project-oriented course at the end of the program. Students will demonstrate their competence in the theory and practice learned from the program through the whole process of a complex data analysis project, including data collection, exploration, preparation, analysis, interpretation and presentation. The project can be either relevant to students’ experience or aspired filed, accompanied by a final essay in which students reflect upon the goals of the program and their personal goals, demonstrate how they met these goals, and what work supports their arguments.

CIM507 Applied Research Strategies 3

Applied Research Strategies provides students with strategies for designing, conducting and evaluating research so that they can solve problems and recommend solutions pertaining to communications and information science. Students acquire the knowledge of skills to formulate research problems; plan studies; gather, organize, analyze and interpret results; prepare research reports; and present findings and recommendations in professional contexts. Specific areas include: qualitative and quantitative research, sampling, measurement techniques, data collection, observational methods, and general principles of research design. Students use bibliographies and other print and computerized databases in conducting research. Throughout the course, students broaden and deepen their understanding of the relationships between research and theory.

CIM605 Decision Support Systems 3

Business Intelligence is a process that helps managers make evidence-based, rational decisions by applying an analytic approach to decision making. Good business decisions should lead to efficient operations, effective utilization of scarce resources, satisfied customers, and increased profits. The course examines two logical components of management information system: the structured The course examines two logical components of management information system: the structured decision system which lends itself to providing actual computer-generated decisions, and decision support systems, in which computer-based systems aid decision makers in confronting problems through direct interaction with data and analytic models. Several of the topics covered in this course include: decision theory, data warehousing and data mining, business analytics (i.e., descriptive and predictive statistics), rational and behavioral economic theories of decision-making.

INF642 Project Management 3

Students focus on project management through critical examination of project planning, design, production, documentation, and presentation techniques. The course distinguishes among the three primary purposes of project management: (1) planning and scheduling project tasks, (2) critical diagnosis and prediction of success or failure in meeting schedules, and (3) estimation of requirements for the project. Topics include: problem identification and definition, project design and analysis, feasibility measures, project charting methods (PERT, GANTT, CPM), process documentation techniques, information modeling, project design specifications and error diagnosis, and task monitoring. Various software packages are used as tools to assist in all phases of project management, development, and presentation.

INF654 Organizational Knowledge Management 3

This course develops the student’s understanding of how intellectual capital is created, shared, stored and manipulated. Students are required to do scholarly research on critical theories and applications of knowledge management in organizations. Special emphasis is placed on knowledge creation, the evaluation of knowledge as an organizational asset, and the transfer of knowledge within learning organizations.

INF658 Strategies of Information Management 3

This course considers the parameters an organization may use to identify strategic information and integrate information throughout all functions and processes of the business. Information flow and strategic integration of information as well as business management processes and change management are stressed.