Academics

Core: Data Science Programming (3 credits)

Data-driven analysis has wrought a quiet revolution in business. As disk storage and computing power have become cheaper, companies have started maintaining detailed logs of inventories, sales, and customer activity, among others. Yet, this is only half the job; the real need is for insights, and this course teaches you the tools for that. This course uses Python & Pandas.

Core: Information Management (3 credits)

Explore various concepts of data management and develop expertise in managing data from the design and modeling of a database to data querying and processing. Learn big data storing principles that can be applied to various database products, such as Hadoop, Map Reduce, and Spark.

6 Credits in Total

Core: Introduction to Machine Learning (3 credits)

Selected topics in the applications of data science to business problems. Topics include regression analysis, including linear, logistic, and multinominal regression; tree models for regression and classification; concepts surrounding model building and model validation, including the bias-variance trade-off, cross-validation, and variable selection; basic data wrangling and data visualization; factor models, including principal components analysis and partial least squares regression; clustering; networks; and text.

Core: Financial Management (3 credits)

Recent advances in cost accounting, inputs into the design of cost systems, maximizing shareholder value through the investment decision and the financing decision; time value of money to value projects, bonds, stocks, and an entire firm.

6 Credits in Total

Core: Advanced Machine Learning (3 credits)

This course will involve the study of a variety of machine learning techniques for predictive analytics. Particular emphasis will be given to approaches that are scalable to very large data sets and/or those that are relatively robust when faced with a large number of predictors, and algorithms for heterogeneous or streaming data. Many of these capabilities are essential for handing BIG DATA. Connections to relevant business problems shall be made via example studies. We will mostly be using Python (especially Scikit-Learn). The central goal of this course is to convey an understanding of the pros and cons of different predictive modeling techniques so that you can (i) make an informed decision on what approaches to consider when faced with real-life problems requiring predictive modeling, (ii) apply models properly on real datasets so to make valid conclusions. This goal will be reinforced through both theory and hands-on experience.

Core: Analytics for Unstructured Data (2 credits)

Unstructured data - text, images, video, and voice -- is everywhere, and yet businesses have started leveraging these newer forms of data only recently. This 2-credit hour course largely focuses on the analytics of text and images and their business applications. Starting with basics, students learn the cutting edge in natural language processing and computer vision analytics. All assignments and the final project are designed to apply technical concepts and principles to solving real-world problems and creating new opportunities. Specifically, students learn to:

• Use Python to conduct analysis of text and images to improve business outcomes
• Build text and image-based recommender systems
• Derive insights about customers, brands, products, and features
• Perform advanced sentiment analysis
• Use generative models for text
• Use computer vision to increase engagement in social media

5 Credits in Total 

Core: Optimization-I (2 credits)

This course deals with optimization methods that help in decision-making. It will cover a broad range of relevant quantitative techniques for decision-making. Each technique will be motivated using important applications and discussed along with some relevant theory. The focus however will be on formulating and solving problems. Specific topics/techniques will include linear, quadratic, nonlinear, and integer programming. The course will use python extensively.

Core: Unsupervised Learning (2 credits)

Unsupervised statistical learning techniques and their role in creating actionable information. Measures of information, principal components analysis, factor analysis, cluster analysis, dimensionality reduction and related techniques.

Elective (2 credits)

6 Credits in Total

Core: Optimization-II (2 credits)

This course deals with optimization methods that help in decision-making. It will cover a broad range of relevant quantitative techniques for decision-making under uncertainty. Each technique will be motivated using important applications and discussed along with some relevant theory. The focus however will be on formulating and solving problems. Specific topics/techniques will include advanced simulation methods, stochastic programming, dynamic programming, and reinforcement learning. The course will use Python extensively.

Core: Capstone Preparation (1 credit)

Elective (2 credits)

Elective (2 credits)

7 Credits in Total 

Core: Capstone Project (2 credits)

Elective (2 credits)

Elective (2 credits)

6 Credits in Total

The following are core courses in the MSBA-WP program:

• Data Science Programming
• Information Management
• Introduction to Machine Learning
• Financial Management
• Advanced Machine Learning
• Analytics for Unstructured Data
• Optimization-I
• Optimization-II
• Unsupervised Learning
• Capstone Preparation
• Capstone Project

The following are elective courses in the MSBA-WP program:

• Marketing Analytics
• Supply Chain Analytics
• Demand Analytics/Pricing
• Advanced Data Analytics in Marketing
• Financial Technology
• Data Driven Healthcare Operations
• Social Media Analytics
• Time Series Analysis
• Ethics of Analytics
• Data Security