Building business intelligence in a data-driven world.
The M.S. in Business Data Science and Analytics program focuses on three core areas:
The courses listed are delivered as a full-time, fixed curriculum, cohort-based program. Students will start in January (spring) and graduate the following December (fall).
We register students for all their classes. For specific dates and deadlines on MSU scheduling, please see the academic calendar.
Students must maintain a cumulative grade-point average of 3.0 or higher in all graduate courses. For more information, please refer to the handbook:
How digitized business processes and data analytics are essential to the performance and competitive advantage of a modern corporation. Different approaches for strategic data management and business analytics. Real-world cases of successes and failures with analytics-based business strategies. Software tools exposed to include Tableau, SQL, SAP, and Cognos Insight.
Emerging issues in big data (e.g., collection, warehousing, pre-processing and querying; mining, cluster analysis, association analytics; MapReduce, Hadoop; out-of-core, online, sampling-based, and approximate learning algorithms; model evaluation and applications, etc.). The following tools are used: Python, Hadoop, and the query languages SQL/NoSQL, and Hive.
Development of managerial level business communication skills focusing on oral and written formats. Translation of technical data for communication to non-technical audiences.
Application of statistical concepts including random variables, distributions, parameter estimation, hypothesis testing, analysis of variance and time series analysis. Develop modeling understanding of when to use what analytical capability. SPSS and R software introduced.
Introduction to the project management process in an analytics framework using real world data. One business’ problems presented and solved by the full cohort, broken into smaller student teams. Approximately 10–11 weeks long.
Application of regression models including simple and multiple regression, model diagnostics, model selection, one and two-way analysis of variance, mixed effects models, randomized block designs, and logistic regression. R software utilized. Three-week intensive at start of semester.
The collection and analysis of information from the web, including predicting future behavior, search engine optimization, landing page optimization, and mobile marketing and analytics. Online throughout the summer with in-person meetings in early May.
Corporate analytics project or internship designed to integrate strategic business understanding with analytical and modeling skills. Manage project engagement with organization. 10–12 weeks, last week of May through late August.
This course explores the application of network analysis in business contexts. Focus is placed on establishing the basic methods and terminology associated with network analysis and text analytics and then progresses into broad-based applications. Applications of these techniques span a broad range of business contexts, including human resource management, CRM systems, supplier networks and online networks. (3 credits)
Techniques and algorithms for knowledge discovery in databases, from data pre-processing and transformation to model validation and post-processing. The following tools are used: Weka, Hadoop, Mahout, and the query languages SQL/NoSQL, Pig and Hive.
Application of data mining and analytical modeling techniques to solve corporate business problems (e.g., customer churn, customer loyalty, market segmentation) using data sets from within and across companies. Software tools used include SAS and SPSS.
Corporate practicum in the development and delivery of predictive data analysis for strategic decision making in organizations. Application of the principles and tools of analytics to real-world problems in R&D, marketing, supply chain, accounting, finance and human resources management. Development and presentation of analytical insights and recommendations.