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Course Descriptions

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 designed around a fixed curriculum cohort based structure. Students will start in January (spring) and graduate the following December.

SPRING SEMESTER

  • Introduction to Business 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.
  • 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 (CSE 891 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 231 OR CSE232 .
  • 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.

SUMMER SEMESTER

  • Applied Statistical Methods (STT863 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 or MTH 415.
  • Marketing Technology and Website 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.
  • 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. 10-12 weeks.

FALL SEMESTER

  • 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 (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.
  • Capstone Project (ITM888 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 (ITM 883 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.


Eli Broad College of Business

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