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MSA 7001 Basic Math for Analytics
Credit Hours 1.5.0
Prerequisites None
Description

This is an introductory and review course on Calculus I, which provides the mathematical preparations for MSA students as well as others who are interested in sharpening their math skills. The course covers a variety of topics including functions, derivatives, integrals, differential equations and, infinite sequences and series. Content will be linked to various topics in courses such as statistics, machine learning, and econometrics.

MSA 7003 Foundations for Programming
Credit Hours 1.5.0
Description

Prerquisites: None. This is an introductory and review course on data structures and algorithms, which provide the programming preparations for MSA students as well as others who are interested in sharpening their programming skills. The course covers a variety of topics including algorithmic complexity, object oriented programming, lists, hash tables, recursion, binary trees, heaps, sorting algorithms, and graphs. Content will be linked to various topics in MSA courses.

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

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 8020 Data Visualization
Credit Hours 1.5.0
Prerequisites None
Description

This course introduces students to basic visualization tools as well as data exploration and data presentation skills. The course mainly covers 3 parts: visualization in R using ggplot2; visualization in Tableau; and advanced visualization tools including interactive visualization, spatial visualization and dimension reduction.

MSA 8040 Data Management for Analytics
Credit Hours 3.0
Prerequisites None
Description

It covers a variety of topics including relational data modeling, logical and physical database design, structured query language, capturing, cleaning and merging unstructured data, and analysis techniques, such as classification, sentiment analysis, clustering and information retrieval. The methods and techniques discussed will be linked to other topics, such as machine learning, and applied to practical analytics problems.

MSA 8050 Scalable Data Analytics
Credit Hours 3.0
Description

This course covers essential concepts and tools for large scale data analytics. Topics include 1) functional and parallel programming paradigms and languages, 2) core components of large scale platforms, 3) scalable machine learning algorithms, and 4) real-time data analysis. Programming projects demonstrate design and implementation of large scale analytics pipelines for structured and un-structured data.

MSA 8100 Optimization Methods in Analytics
Credit Hours 1.5.0
Prerequisites None
Description

This course introduces students to the theory, algorithms, and applications of optimization in analytics. The optimization methodologies include linear programming, nonlinear programming and advanced optimization. Examples and applications in analytics, statistics and machine learning will be discussed.

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 Predictive Analytics
Credit Hours 3.0
Prerequisites MSA 8190 or consent of the instructor
Description

This course introduces students to different predictive models with a focus on real-world applications and datasets. The course covers three primary topics: the analysis of time series data, including estimation and inference for ARIMA models; the set of skills required to analyze real world data, including data pre-processing, data type identification, and different types of models for panel and cross-sectional data; the students will also have hands on experience of working with real world data.

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 8350 Legal Analytics
Credit Hours 3.0
Prerequisites None
Description

Cross listed with LAW 7675. This course introduces students to the emerging field of
legal analytics, which employs computational and
statistical modeling, analysis, and visualization of legal
data to accomplish both descriptive and predictive goals. For analytics students, the course provides an introduction
to the U.S. legal system and legal reasoning, legal
materials, and the problems and questions present in the
law. For law students, the course offers an introduction to
basic coding, as well as to the theory and applications of
text mining, natural language processing, machine learning
and other methods for managing and analyzing
unstructured data such as that found in legal documents.

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

This course allows for in-depth study of topics of significance in analytics. Examples of topics that could be covered include applications of machine learning to business research, text analytics in law research, research between FinTech and analytics.

MSA 8391 Analytics Field Study
Credit Hours 1.0 - 4.0
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.

MSA 8395 Special Topics in Analytics
Credit Hours 1.0 - 3.0
Prerequisites Instructor Approval
Description

Examples of topics that could be covered include new applications of analytics in areas like financial technology, new analytics technology, and experimental techniques and methodologies. The topic of each offering will be announced in advance, and students may take this course multiple times for course credit as different topics are offered.

MSA 8500 Image Analytics for Operations
Credit Hours 3.0
Prerequisites None
Description

The course covers topics such as image formation, processing, feature detection and matching, image segmentation, feature-based alignment, image stitching, and recognition. Machine learning methods such as convoluted neural networks will be discussed for classification and optical characteristic recognition. Various applications of using images in business context will be discussed.

MSA 8600 Deep learning analytics
Credit Hours 1.5.0
Prerequisites None
Description

This is an introductory and review course on historical development of neural networks and state-of-the-art approaches to deep learning. Students will learn the various deep learning methods and will also learn how to design neural network architectures and training procedures through hands-on assignments. The course covers a variety of topics including neutral network basics, deep learning strategies such as GPU training and regulation, convolutional networks, recurrent neutral networks, the long short-term memory and other gated RNNs and unsupervised deep learning. Applications of using deep learning into natural language processing and image recognition will be discussed throughout the course.

MSA 8650 Advanced Deep Learning with Business Applications
Credit Hours 3.0
Prerequisites MSA 8100, MSA 8600, or instructor approval
Description

This course uses advanced deep learning methods to explore how to make better business decisions from various data with a focus on texts and images. Text documents and images have proven to be useful complements to structured data in different research fields such as marketing, information management, real estate, accounting, finance, operations management etc. This course studies how to use deep neural network to solve business related problems.

MSA 8770 Text Analytics
Credit Hours 3.0
Prerequisites MSA 8010 or an equivalent Python course
Description

The course will build on established concepts in data mining, machine learning and natural language processing, and on newer developments in the applicability and usability of text data analytics. The course introduces students to the process of formulating business objectives, implementing rigorous text processing techniques, and lastly training, testing, implementing and evaluating various models.