Building Business Intelligence in a Data-Driven World
The MS in Business Analytics program focuses on 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
The courses listed below are delivered as a full-time, fixed curriculum, cohort based program. Students will start in January (spring) and graduate the following December.
OPTIONAL PER-ENROLLMENT FALL SEMESTER (Bridge program for MSU May and August graduates only)
- Fall Project Shadow Experience: This is a opportunity for admitted students to shadow current students on the capstone project course from September thru December. Students will have a opportunity to learn, test and put into action key elements of our program. This can be a great resume builder for students entering the program and also provide early networking opportunities with corporate sponsors.
- Fall Analytics Career Fair Student Ambassador: Admitted students to our program can share their time and skills to help us run the event. Students gain direct employer contact by assisting with various areas such as registration, employer booth setup and general event management needs.
- SAS Training Seminar: This occurs the last week of August.
- Interested? Contact the program director for additional details.
||Courses (30 credits total, descriptions below)
- Data Management and Visualization in Analytics
- Computational Techniques for Large Scale Data Analysis
- Large Scale Data Learning Lab
- Communication Strategies for Analytics
- Business Analytics Problem Solving (Statistics)
- Co-curricular Corporate Project (1 company, 8 student teams)
- Applied Statistics Methods (2-week summer intensive in early May)
- Web Analytics (2-week summer intensive in early May)
- Internship (End of May thru early August)
- Social Network Analytics
- Data Mining
- Machine Learning and Optimization in Analytics
- Capstone Business Analytics Corporate Project (10 companies and 10 student teams)
- Data Management and Visualization in Analytics (ITM 818 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. Software tools exposed to include: SQL, SAP, and Cognos Insight.
- Business Analytics for Problem Solving with 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. SPSS and R software introduced.
- Computational Techniques for Large-Scale Data Analysis (CSE 891a 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.). The following tools are used: Weka, Hadoop, Mahout, and the query languages: SQL/NoSQL, Pig and Hive. Recommended background: CSE 231 OR CSE232 .
- Large Scale Data Learning Lab (1 cr)
- Communications Strategies for Analytics (MGT888 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. One business problems presented and 6-7 student teams. Approximately 10-11 weeks long. Training project to prepare students for their summer internship.
- Applied Statistical Methods (STT805 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. R software utilized. 2 week intensive at start of semester. Recommended background: STT 442 or STT 862 or MTH 415.
- Web Analytics (MKT829 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 throughout the summer with two in-person meetings in early May.
- Internship or Practicum (ITM893 3 cr) Corporate analytics project or internship designed to integrate strategic business understanding with analytical and modeling skills. Manage project engagement with organization. 10-12 weeks.
- Data Mining (CSE 891b 3 cr) 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. Recommended background: Programming skills in C, C++, Java, and Matlab. Basic knowledge in calculus, probability and statistics.
- Machine Learning and Optimization in Analytics (MKT865 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. Software tools used include: SAS and SPSS.
- Capstone Project (ITM888 2 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.
- Social Network Analytics (ITM 881 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.
GPA requirements: 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. MSBA Student Handbook