MS in Business Analytics Curriculum
Intensive industry-focused tracks in customer, supply chain, financial technology, and healthcare analytics
The 18-month Master of Science in Business Analytics (MSA) program capitalizes on a growing trend that combines technical programming skills with strategic business acumen to extract insights from vast storehouses of data. Using data collected from product scanners, cash registers, smart devices, patient records, or wearable devices, skilled analysts can get ahead of business trends, forecast customer behavior, uncover solutions to business problems, and develop targeted marketing strategies.
Starting fall 2018, the specialized masters in analytics will offer four STEM (Science, Technology, Engineering and Mathematics) designated tracks—customer analytics and three new options: supply chain analytics, financial technology analytics, and healthcare analytics.
Students in the 39-credit degree program begin in July with a set of foundational courses, including Basics of R Programming and a choice of SPSS, SAS, or Stata programming.
Core Analytics Courses
A total of 18 credits are common to all tracks and build your analytic knowledge base.
The first fall semester introduces key concepts and tools including Database Design and SQL and Big Data and Cloud Computing, as well as:
- Intro to Python and Data Science introduces programming language to acquire, clean, analyze, and visualize data (descriptive analytics) for reporting and complex optimization.
- Predictive Analytics covers advanced analytic techniques such as neural networks and stochastic gradient boosting to convert raw and messy business data into robust predictions of future customer behavior or critical organizational elements.
- Prescriptive Analytics builds upon the descriptive and predictive analytics course work through the use of optimization models and software tools to suggest decision options for a wide variety of business decisions.
In addition, Managerial Communications introduces students to fundamental best practices in business writing and business speaking.
Core requirements conclude in the spring semester with an Introduction to Cybersecurity and two advanced analytic topics:
- Causal Inference teaches statistical and experimental methods to identify causal relations among data sets and reject prescriptive options based on biased samples or reverse causality.
- Text Mining provides techniques, algorithms, and tools for collecting, organizing, summarizing, and analyzing textual data for topic and sentiment analysis and predictive modeling.
Four Analytics Tracks
In track-specific courses (21 credits), you dive into industry and functional applications of analytics and complete an intensive industry-specific project or assignment. Track requirements are introduced in the first fall semester and comprise all your courses in the final fall semester. The majority of these courses are required.
- Marketing Management
- Marketing Research
- Advanced Marketing Research
- Data Analysis for Brand Management
- Digital Marketing
- Customer Analytics Using Probability Models
- Intensive Industry Experiential Project
- Electives: 7.5 credits
Supply Chain Analytics
- Operations Management
- Foundations of Supply Chain Management
- Stochastic Models
- Supply Chain Finance
- Operations Analytics
- Revenue Management
- Advanced Operations Strategy
- Supply Chain Analytics Capstone
- Global Supply Chain and Logistics System Design Experiential Project or Practicum
- Electives: 3 credits
Financial Technology Analytics
- Intro to Financial Accounting
- Financial Management
- Advanced Corporate Finance I – Valuation
- Investment Theory
- Options and Futures
- Fixed Income Securities
- Financial Technology – Methods and Practice
- Seminar in Financial Technology
- Experiential Project or Internship
- Electives: 3 credits
- Olin Grand Rounds: The Business and Practice of Medicine
- Research in Healthcare Management
- Healthcare Management
- Health Economics and Policy
- Healthcare-Related Experiential Project
- Electives: 6 credits