Program Structure and Curriculum
An evening and online program designed for working professionals, the Applied Business Analytics program provides a strong foundation in business analytics by combining technical know-how that includes machine learning, artificial intelligence, applied statistics, optimization, econometrics, experimentation, data visualization, data engineering, and big data with an understanding on how they may be applied in domains such as marketing, consumer behavior, operations, financial and risk management, information management, and strategic management in both public and private sectors.
Note: International students who require an F-1 visa are not eligible for the Master of Applied Business Analytics program. Alternatively, the Master of Science in Business Analytics program is a full-time day program available to International students.
MABA 6121 - Practical Statistics for Business Applications (2 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.
MABA 6311- Programming for Business Analytics (2 credits)
Introduction to Python with a focus on steps of using data for decision making; topics include: data acquisition, parsing, handling missing data, summarization, augmenting, transformation, subsetting, sampling, aggregation, and merging.
MABA 6341 - Data Visualization (2 credits)
The use of visualization for exploring (and communicating with) data: discover patterns, answer questions, convey findings, drive decisions, and provide persuasive evidence. The students will have practical, hands-on experience with interactive data visualization using modern, state-of-the-art software on real-world datasets.
MABA 6251 - AI for Competitive Advantage (2 credits)
Case-, technical-, and discussion-based introduction to the strategic use of artificial intelligence for firm strategy. Topics include business value, impact, benefits, and limitations. The course is equally divided into cases, discussions, lectures, and technical demonstrations.
MABA 6321 - Data Management & Big Data (2 credits)
Fundamentals of database modeling and design; extract, transform, and load; data pre-processing, quality, integration, and stewardship issues; advances in database and storage technologies for unstructured and big data.
IDSC 6490 - Mathematics Essentials for Business Analytics (2 credits)
This course is designed to provide a foundation and refresher for working professionals to succeed in the mathematically rigorous Applied Business Analytics curriculum. The course has three primary components: discrete mathematics and probability review, calculus review, and matrix algebra review.
MABA 6451 - Prescriptive Analytics (2 credits)
Fundamentals of decision analysis, optimization, linear and integer programming, risk analysis, heuristics, simulation, decision technologies.
MABA 6441- Causal Inference via Econometrics and Experimentation (2 credits)
Controlled experiments in business settings, experiment design, A/B testing; specialized statistical methodologies; fundamentals of econometrics, instrument variable regression, propensity score matching.
MABA 6411 - Exploratory Data Analytics (2 credits)
Fundamentals of data exploration; detecting relationships and patterns in data; cluster analysis, hierarchical and partition-based clustering techniques; rule induction from data.
MABA 6421 - Predictive Analytics (2 credits)
Fundamentals of predictive modeling and data mining; assessing the performance of predictive models; machine learning and statistical classification and prediction; logistic regression; decision trees, random forests; k- nearest neighbor techniques, naïve Bayesian classifiers, neural networks.
MABA 6431 - Advanced Topics on Business Analytics (2 credits)
Analytics with complex and specialized data, e.g., text mining, time series analysis, network data analysis, personalization.
MABA 6141 - Ethics, Data Privacy, and Governance (2 credits)
Introduction to the legal, policy, and ethical implications of data, including privacy, surveillance, security, classification, discrimination, etc. Examines legal, policy, ethical, and governance issues throughout the full data-science life cycle - collection, storage, processing, analysis, and use.
MABA 6511 - Experiential Learning (4 credits)
Hands-on, integrative application of analytics methodologies, techniques, and tools learned throughout the program in the context of a specific analytics problem. Introduction to agile project management. 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.
Electives (floating; 4 credits)
- MKTG 6084 - Persuasion and Influence
- IDSC 6040 - Information Technology Management
- MBA 6110 - Leading Others
- FINA 6322 - Financial Modeling
- MGMT 6004 - Negotiation Strategies
- MGMT 6033 - Managing the Strategy Process
- MGMT 6032 - Strategic Alliances
- MGMT 6084 - Management of Teams
- ENTR 6036 - Managing the Growing Business
- ENTR 6020 - Business Formation