MABA Program Curriculum & Structure

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 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 6131 - Mathematics Essentials for Business Analytics (2 credits)
Fundamentals of decision analysis, optimization, linear and integer programming, risk analysis, heuristics, simulation, decision technologies.

MABA 6451 - Prescriptive Analytics (2 credits)
Fundamentals of decision analysis, optimization, linear and integer programming, risk analysis, heuristics, simulation, decision technologies.

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.

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)                                            Analysis of time series data; understanding its components including trend, seasonality, autocorrelation, and stationarity; model interpretation and forecasting; traditional statistical and modern machine-learning views of temporal dependency; combining time series analysis with other business analytical tools to discover hidden knowledge and gain competitive advantages ahead of time.

MABA 6141 - Ethics, Data Privacy, and Governance (1 credit)
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 6490 Special Topics in Applied Business Analytics (1 credit)

Fall 2021 Topic: Machine Learning System Design

Machine Learning System Design is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. The course is aimed at teaching students beyond jupyter notebooks and to deploy models at scale. ML engineering is at the center of building ML-enabled systems, which concerns the development and operationalization of production-grade ML systems. ML engineering provides a superset of the discipline of software engineering that handles the unique complexities of the practical applications of ML.

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.