North Business College Complex
632 Bogue St. N270
East Lansing, MI 48824
Business Analytics: Building Business Intelligence in a Data-Driven World
The MS in Business Analytics offers courses in three core areas:
- Training our students to be leaders in business data management process and business analytics approaches
- Providing our students with experiential project management opportunities using live data sets for analysis and application
- Developing student’s expertise in key data management areas such as:
- Data mining, marketing technology, applied statistics
- How to interpret and communicate data analysis
- Introduction to Business Analytics (3 cr) 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.
- Introduction to Statistics (2 cr) 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.
- Computational Techniques for Large-Scale Data Analysis (3 cr) 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.). Recommended background: CSE 232 or permission of instructor.
- Communications Strategies for Analytics (1 cr) Development of managerial level business communication skills focusing on oral and written formats.
- Co-Curricular Corporate Experiential Project: Sponsored by IBM SPSS and a featured corporate partner.
- Applied Statistical Methods (3 cr) 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. 2 week intensive at start of semester. Recommended background: STT 442 or STT 862; MTH 415 or concurrently.
- Marketing Technology and Website Analytics (3 cr) 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
- Internship or Practicum (3 cr) Corporate analytics project or internship designed to integrate strategic business understanding with analytical and modeling skills. Manage project engagement with organization.
- Data Mining (3 cr) Techniques and algorithms for knowledge discovery in databases, from data pre-processing and transformation to model validation and post-processing. Recommended background: Programming skills in C, C++, Java, and Matlab. Basic knowledge in calculus, probability and statistics.
- Applying Analytics to Solve Business Problems (3 cr) 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.
- Capstone Project (3 cr) 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.
- Network Analytics (3 cr) 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.