Year 1, Fall Semester
Introduces the key concepts of data science and data analytics as applied to solving data-centered business problems. Emphasizes principles and methods covering the process from envisioning the problem to applying data science techniques to deploying the results to improve financial performance, strategic management, and operational efficiency. Topics include an introduction to data-analytic thinking; application of data science solutions to business problems; data mining, supervised and unsupervised machine learning; methods for the detection of co-occurrences and associations; and achieving and sustaining competitive advantage with data science. Presents the application of these disciplines in the areas of marketing, supply chain management, finance, sales, and innovation.
Covers basic principles and techniques of descriptive and predictive analytics. What are the essential data analysis concepts underlying business analytics? Topics include descriptive statistics, data visualization, probability and modeling uncertainty, sampling, estimation and confidence intervals, hypothesis testing, analysis of variance, simple and multiple regression analysis, time-series analysis, and forecasting. Emphasizes an understanding of how these tools can support decision making and analytics initiatives in a business context with real-world examples and case studies. Uses various software packages for analyzing data sets and creating visualizations.
Year 1, Spring Semester
Introduces key analytics methods for using data through the perspectives of applied statistics and operations analysis. Covers application of these methods to business areas including marketing, supply chain management, and finance. Topics include business-analytic thinking; application of business analytics solutions to business problems; data mining, supervised and unsupervised machine learning; methods for detecting co-occurrences and associations; and achieving and sustaining competitive advantage by using business analytics methods.
This course covers contemporary data management and design practices for business strategy and performance. Topics include enterprise data management, business intelligence and analytics tools. Techniques and perspectives for designing and managing use-inspired information products for companies are emphasized. Multiple distributed sources of real-world data and examples will be used by students to design a company’s strategic information products. Students will learn to use various user-friendly database management, retrieval and dashboard software tools.
Year 1, Summer Semester
This course covers communication methods with data for business. Topics include visualization techniques and methods used in practice. Students will learn to use various visualization software tools to explore effective and creative ways of visualizing and communicating analysis processes and results in business. The course emphasizes acquiring and using feedback from multiple stakeholders to most effectively visualize data in a business context.
This course provides an advanced perspective, techniques and methods for data mining in the business context. The course will cover several frequently-used machine learning and data mining techniques using real-world data and examples in the business context.
Year 2, Fall Semester
This course utilizes all concepts and methods covered in previous coursework to implement a real business project. Emphasizing experiential learning, the course will include a customized real-world project with a student’s environment and career goal in mind.
Year 2, Fall Semester
This course focuses on building key knowledge and skills to profitably acquire, satisfy, and retain customers. Students will have an opportunity to learn how to choose which customer markets to pursue, identify benefits that are valued by customers, and then develop, communicate, and deliver a product or service, offering it in a way that meets the objectives of both the customer and the organization. As organizations have become increasingly customer-oriented, all employees are now expected to engage in satisfying customer needs regardless of their job title. Students will have an opportunity to learn advanced analysis of customer desires and behaviors (customer analysis) and the array of forces affecting customers in the market (competitive market analysis).
Year 2, Spring Semester
This course focuses on the marketing research process and the analysis of data using a de-facto industry standard statistical software application. It provides students an opportunity to develop an understanding of consumer attitudes and behavior processes as the basis of the design and understanding of marketing problems. The course covers topics such as problem definition, research design, sampling, consumer attitude measurement, questionnaire design, data collection, and data analysis.
This course focuses on contemporary marketing analytics perspectives and techniques.
*The curriculum is subject to change by D’Amore-McKim faculty. Please monitor for updates.
Featured MS in Business Analytics Instructors
Associate Professor, Marketing; Marketing Group Chair; MBA Marketing Career Track Chair
Assistant Professor, Supply Chain and Information Management Systems
See all faculty who regularly teach in the MS in Business Analytics program.
Upon completion of the program, you will have the knowledge, skills, and abilities to demonstrate critical thinking in a business environment.