MSA 8000 Consumer and Managerial Decision Making
Credit Hours 3.0
Description

Prequisites: ECON 2105 AND ECON 2106 or MBA 7035. This course presents a microeconomic framework of consumer and managerial decisions from which analytically informed strategies can be developed. The first part presents a model of consumer preferences and how individuals make purchasing choices for products or services. Topics include preferences and utility theory, demand analysis, and the impact uncertainty and incomplete information have on consumer decisions. The second part extends the theory of individual choice to corporate managerial decision-making. Topics covered include risk and return analysis, cost of capital, project selection, and capital budgeting techniques. Illustrative applications using large data will be included as necessary.

MSA 8005 Mathematical Foundations for Analytics
Credit Hours 1.0 - 3.0
Requirements Permission from instructor
Description

CSP’s: 1, 2, 7. This is an introductory and review course on calculus, linear algebra, and probability foundations, which provides the mathematical basics for MSA students. The course covers a variety of topics including functions, limits, derivatives, integrals with single and multiple variables, and some probability foundations such as measures, expectations, and the central limit theorem. Content will be linked to various topics in other analytics courses such as machine learning, statistics, operations research, and econometrics.

MSA 8010 Data Programming for Analytic
Credit Hours 3.0
Prerequisites MRM 8000
Description

This course builds upon the student’s foundation of programming principles through the introduction of application programming for data analysis. Major areas covered include inheritance and polymorphism, common programming data structures, and file and database access. Students will implement data analysis applications, which will be evaluated according to advanced programming principles. The programming language will be noted in the course listing for each semester.

MSA 8050 Unstructured Data Management
Credit Hours 3.0
Prerequisites CIS 8040
Description

This course addresses the unstructured data management skills needed for modern data analysis including those salient to big data and real-time data environments. The focus is on unstructured data and its environment. Unstructured data includes web data (blogs, text), user generated content, social media, location-aware data, and digital media among others. Topics covered include extraction methods for real time audio and video data, data capture, cleaning, representation, storage, queries, manipulation, and real-time data management. Also included as they apply to unstructured data environment are data security, governance, and visualization. Students will learn natural language processing and geo-spatial analytical tools.

MSA 8100 Operations Research Models and Methods
Credit Hours 3.0
Prerequisites MSA 8190 or MRM 8000
Description

The focus of this course is operations research (OR) as a discipline of applying advanced analytical methods to help make better business decisions. It introduces formulation, solution techniques, and sensitivity analysis for optimization problems that include linear, integer, network flow, non-linear and dynamic programs such as traditional LP/ILP/MILP models, transportation and network models. Students are exposed to multidisciplinary applications from areas including but not limited to logistics, manufacturing, transportation, marketing, project management, health care, urban planning, and finance. Students use software packages to solve linear, integer, and network problems.

MSA 8150 Machine Learning for Analytic
Credit Hours 3.0
Prerequisites MSA 8010
Description

The course will cover theory, methods, and tools for automated inference from data. This introductory course will include (1) supervised learning, (2) unsupervised learning methods, (3) graphical structure models, and (4) deep learning. The course will prepare students in the fundamentals of machine learning, as well as provide practical skills in applying current software tools to machine inference from large data sets.

MSA 8190 Statistical Foundations for Analytics
Credit Hours 3.0
Prerequisites None
Description

The course covers basic probability and mathematical statistical theory, and provides a basic introduction to linear models, with an eye on application. The course starts with a primer on linear algebra, discussing the solution of linear equation systems, the rank of a matrix, determinants, eigenanalysis, and diagonalization; and basic probability theory, including probability spaces, dependence, random variables, (conditional) expectations, and sampling. It continues with the introduction of discrete and continuous distributions, and basic statistical theory of estimation and inference. Topics include consistency, unbiasedness, efficiency, maximum likelihood estimation, central limit theorem, confidence intervals, and hypothesis testing.

MSA 8200 Econometric Modeling for Analytics
Credit Hours 3.0
Prerequisites MSA 8190 or consent of the instructor
Description

This course introduces students to econometric methods used in business analytics with a focus on real-world applications and datasets. The course covers two primary topics: econometric methods for panel data including how to account for basic heterogeneity effects; the most important models used for the analysis of time series including estimation and inference methods for univariate and vector auto-regressive models. After discussing these models in the classical context, the course revisits them using Bayesian methods with a focus on issues of parameter and model uncertainty. The course closes with a discussion of state-space models and Kalman filtering.

MSA 8300 Value Through Analytics: Model Deployment and Life Cycle Mgmt
Credit Hours 3.0
Prerequisites MGS 8040
Description

This course serves as a practicum to apply aspects of the life cycle of a predictive model with real data. Students review all phases of the cycle to identify the need for models based on the business situation, define the appropriate inputs to the model, identify sources of data, and prepare data for modeling. They develop and validate the model, and discuss strategies for deployment. They develop and put in place processes for testing and monitoring the quality of the models to ensure optimal performance. Champion/challenger strategies and standardized as well as custom monitoring reports are discussed. As models degrade over time, strategies for updating and replacing models and assessing the business benefit over time will be addressed to complete the model life cycle.

MSA 8389 Directed Readings in Analytics
Credit Hours 1.0 - 3.0
Prerequisites Consent of the adviser, good academic standing and open to MSA students only
Description

The directed readings is a supervised research culminating in a term paper or a short thesis. Students are responsible for choosing their directed readings topic and presenting a plan of study with deliverables to be approved by their academic advisor.

MSA 8391 Analytics Field Study
Credit Hours 1.0 - 3.0
Prerequisites Consent of the adviser, good academic standing
Description

open to MSA majors only. The field study is a supervised practical application experience, an internship, or consulting experience, culminating in a term paper or thesis. It provides students the opportunity to learn and apply analytics project skills in a complex and professional setting. Students are responsible for choosing their field study topic and presenting a plan of study to be approved by their academic advisor.