Program Structure and Curriculum
The part-time MS in Business Analytics curriculum consists of foundation business courses, custom tailored to data science and analytics. This rigorous and fast-paced cohort-based graduate program offers the flexibility of online, evening, and condensed-format classes for working professionals.
Students take 6-9 credits per semester (including summer) and complete the program in two years (45 credits total). Classes are offered online, during weekday evenings, or in condensed formats. New students begin the program each fall semester.
MSBA 6250 Analytics for Competitive Advantage (3 credits)
Case- and discussion-based examination of a variety of analytics-related issues and examples in business, including business value, impact, benefits and limitations, as well as ethical, legal, and privacy issues; use of case studies, examples, guest speakers.
MSBA 6120 Introduction to Statistics for Data Scientists (3 credits)
Concepts/principles of business statistics, data analysis, and presentation of results. Topics include exploratory data analysis, basic inferential procedures, statistical process control, time series/regression analysis, and analysis of variance. These methods are selected for their relevance to managerial decision making and problem solving.
Core Business Classes (9 credits)
Students are required to take a total of nine credits of core business courses. The core business courses can be taken during any semester, based on availability and student’s schedule. Core courses are taught on campus, online, or in condensed format. Some courses may be completed during summer semester. Suitable courses include the following:
- Business Ethics (2 credits)
- Financial Accounting (3 credits)
- Financial Management (3 credits)
- Management and Organizational Behavior (2 credits)
- Managerial Accounting (3 credits)
- (Managerial) Economics (2 credits)
- Marketing Management (3 credits)
- Operations Management (3 credits)
- Strategic Management (3 credits)
MSBA 6310 Programming for Data Science (3 credits)
According to recent industry surveys, Python is one of the most popular tools used by organizations data analysis. We will explore the emerging popularity of Python for tasks such as general purpose computing, data analysis, website scraping, and data visualization. You will first learn the basics of the Python language. Participants will then learn how to apply functionality from powerful and popular data science-focused libraries. In addition, we will learn advanced programming techniques such as lambda functions and closures. We will spend most of our class time completing practical hands-on exercises.
MSBA 6320 Data Management, Databases, and Data Warehousing (3 credits)
Fundamentals of database modeling and design, normalization; extract, transform, and load; data cubes and setting up a data warehouse; data pre-processing, quality, integration, and stewardship issues; advances in database and storage technologies.
MSBA 6330 Big Data Analytics (3 credits)
Exploring big data infrastructure and ecosystems. Ingesting and managing big data, including analytics with big data. Using Hadoop, MapReduce, Sqoop, Pig, Hive, Spark, SQL. Machine learning and real-time streaming. Cloud computing and other recent developments in big data.
MSBA 6410 Exploratory Data Analytics and Visualization (3 credits)
Fundamentals of data exploration; detecting relationships and patterns in data; cluster analysis, hierarchical and partition-based clustering techniques; rule induction from data; advances in multi-dimensional data visualization.
MSBA 6420 Predictive Analytics (3 credits)
Fundamentals of predictive modeling and data mining; assessing performance of predictive models; machine learning and statistical classification and prediction; logistic regression; decision trees; naïve Bayesian classifiers; support vector machine, ensemble learning, deep learning and their applications in structured and unstructured data.
MSBA 6430 Advanced Issues in Business Analytics (3 credits)
Analysis of unstructured data, fundamentals of text mining, sentiment analysis; fundamentals of network analysis, mining digital media and social networks, peer effects and social contagion models; personalization technologies and recommender systems.
MSBA 6440 Causal Inference via Econometrics and Experimentation (3 credits)
Controlled experiments in business settings, experiment design, A/B testing; specialized statistical methodologies; fundamentals of econometrics, instrument variable regression, propensity score matching.
MSBA 6450 Modeling and Heuristics for Decision Making and Support (3 credits)
Fundamentals of decision analysis, optimization, linear and integer programming, risk analysis, heuristics, simulation, decision technologies.
IDSC 6490 Advanced Topics in MIS: Math Foundations for Business Analytics (3 credits)
This course is specific to the part-time MS in Business Analytics program, designed to provide a foundation and refresher for working professionals to succeed in the mathematically rigorous MSBA curriculum. The course has three primary components: discrete mathematics and probability review, calculus review, and matrix algebra review.
Capstone Project (3 credits)
Hands-on, integrative application of analytics methodologies, techniques, and tools learned throughout the program in the context of a specific analytics problem. Experience with the entire data analytics cycle, starting from business and data understanding as well as data cleaning and integration and ending with the development and presentation of results, interpretations, insights, and recommendations.
Sample Course Plan for Part-Time MSBA
|YEAR 1||YEAR 2|
Introduction to Statistics for Data Scientists (3 cr.)
Programming for Data Science
Math Foundations for Business Analytics (3 cr.)
Predictive Analytics (3 cr.)
Exploratory Data Analytics and Visualization
Analytics for Competitive Advantage (3 cr.)
Data Management, Databases, and Data Warehousing (3 cr.)
Big Data Analytics (3 cr.)
Advanced Issues in Business Analytics (3 cr.)
Modeling and Heuristics for Decision Making and Support (3 cr.)
Causal Inference via Econometrics and Experimentation (3 cr.)
Core Business Course
Capstone Project in Analytics (3 cr.)
Core Business Course