MS in Business Analytics Curriculum

The 18-month Master of Science in Business Analytics (MSA) program prepares graduates for today’s world of big data. By combining technical programming skills with a rigorous approach to business problem-solving, MSA students gain the tools and the confidence to extract strategic insights from vast storehouses of data. Across every industry, businesses are collecting data—from retail inventory records to hospital patient data to credit card transactions and more—and relying on skilled analysts to stay ahead of business trends, build forecasts and drive creative solutions to business problems.

WashU Olin’s specialized master’s in business analytics offers six STEM (science, technology, engineering and mathematics)-designated tracks focused on accounting analytics, customer analytics, financial technology analytics, healthcare analytics, supply chain analytics and talent analytics.

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.

Six 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.