Wayne State University
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College of Engineering

Statistics major

The Master of Science in Data Science and Business Analytics program is designed to give graduates a core of computing, business, statistics, and operations research skills to identify, analyze, and solve analytics problems; to integrate those skills in an interdisciplinary way that other, single-discipline-oriented analytics degree might not; and to provide in-depth training in an analytics area of specialization. The Statistics major is designed to meet demand in industry for talent with solid statistical foundations.

Module 1: Core courses (9 credits)

Course DSB 6000: Data Science Strategy & Leadership (3 credits) - Provides an understanding of how organizations can leverage data science and analytics to gain competitive advantage and how to use the data to align with a company's mission and goals. Students will learn how organizations derive business value/impact, and return on investment, and the importance of interpreting and communicating the business case.

Course DSE 6000: Computing Platforms for Data Science (3 credits) - Covers an overview of various computing platforms for developing, deploying, configuring a wide range of data science applications for different domains. The programming models, characteristics of supported workload, and management of performance, cost and scalability will be compared side by side.

Course STA 5030 : Statistical Computing and Data Analysis (3 credits) - Computational aspect of statistics and data analysis for advanced undergraduate and beginning graduate students. Topics include descriptive statistics, probability distributions, hypothesis testing, ANOVA, linear regression and logistic regression. Data analysis by use of statistical packages such as R, SAS, Python, SPSS or Minitab.

Module 2: Major courses

Students have to finish following courses (11 credits) if they have not completed the courses before admission.

Course MAT 5700: Introduction to Probability Theory (4 credits) - Probability spaces; combinatorial analysis; independence and conditional probability; discrete and continuous random variables including binomial, Poisson, exponential and normal distributions; expectations; joint, marginal and conditional distribution functions; law of large numbers; central limit theorems.

Course STA 5800: Introduction to Mathematical Statistics (4 credits) - A one-semester course for senior undergraduate and master's degree students. Introduction to basic mathematical theory of statistics. Topics include survey sampling, estimation theory, data analysis and sample statistics, testing hypothesis, two sample cases, analysis of variance, regression analysis, Bayesian inference. 

Course STA 5820: Introduction to Data Science (3 credits) - An applied statistical learning course designed for upper level undergraduate students and graduate students in mathematics and other quantitative fields. Topics include: bias-variance trade-off, regression, classification, cross-validation, bootstrap, model selection, regularization, splines, generalized additive models, tree-based methods, support vector machines, principal component analysis and clustering. Computer implementation will be discussed for each of the methods, and students will run their own data analysis projects. 

OR:

Course CSC 5825: Introduction to Machine Learning and Applications (3 credits) - Through algorithmic investigation, brainstorming, and case analysis, students develop the skills and strategies that are necessary for effective leaning from data, including Big Data emerging from science and engineering.

If any of the above courses were completed before admission to the program, students must complete three courses (9 credits) in Module II. Students can choose from any of the following courses:

Course  STA 7810: Advanced Statistics Theory I  (3 credits) - First of two basic courses for Ph.D. students in the Mathematics Department who are interested in statistics. Topics include sample distribution theory, point and interval estimations, optimal estimates, theory of hypothesis testing, and most powerful tests. 

Course STA 7820: Advanced Statistics Theory II (3 credits) - Continuation of STA 7810. Topics include regression analysis, linear models, analysis of categorical data, nonparametric statistics, decision theory, and Bayesian inference.

Course DSA 6100: Statistical Learning for Data Science and Analytics (3 credits) - A fundamental course covering statistical learning techniques required for data science and analytics applications through methods, case studies, and a semester project that cuts across all course modules. This course focuses on both statistical learning methods and the life-cycle of a statistics-driven data science and analytics project. Students will be exposed to a variety of tools and technologies.

Course DSA 6200: Operations Research (3 credits)- Mathematical optimization models that come into play in data science and analytics applications covered through case studies and a semester project. Heuristic solution approaches will also be addressed along with sensitivity analysis techniques.

OR:

Course MAT 5770: Mathematical Models in Operations Research (3 credits) - Deterministic and probabilistic mathematical modeling of real-world problems. Linear and nonlinear programming; Markov chains; queuing theory; inventory models; Markov decision processes.

Course DSE 6200: Modern Databases (3 credits) - Covers an overview of databases, tools, and computing platforms. One focus is basic SQL, NoSQL, and NewSQL programming skills and a comparison of their cons and pros. In particular, the students will learn the criteria to choose a database system, either SQL or NoSQL, based on the requirements of an application domain. 

Course CSC 7825: Machine Learning (3 credits) - Supervised learning including regression, kernel-based, tree-based, probability model based and ensemble learning; unsupervised learning including distance based and model based; Markov Chain Monte Carlo (MCMC) methods; graphical models; current topics from literature.

Module 3: Electives

Students are required to select 4-6 credits from the following list. The number of credits will be based on coursework completed for Module 2.

Statistics courses

Probability courses

Mathematics courses

Computer Science courses

Industrial Engineering courses

Economics courses

Information Systems Management courses

Data Science courses

Module IV: Practicum

Select 6 credits from the following:

Course STA 5830: Applied Time Series (3 credits) -  Time series models, moving average models, autoregressive models, non-stationary models, and more general models; point estimators, confidence intervals, and forecast in the time domain. Statistical analysis in the frequency domain; spectral density and periodogram.

Course STA 6830: Design of Experiments (3 credits) - Randomized blocks; Latin and Graeco-Latin squares; factorial designs; confounding; split plot; fractional replication; balanced incomplete blocks.

Course STA 6840: Applied Regression Analysis (3 credits) - Multiple linear regression; generalized linear models; random effect models; repeated measurements; mixed effect models; non-parametric additive models. Computer implementation using statistical software R; student project on real data analysis.

Course STA 7800: Practicum (6 credits) - Apply theoretical knowledge acquired throughout the Big Data and Business Analytics MS program to a challenging project involving real-world business problems/opportunities and data analytics in a reliable, scalable, distributed computing environment.

Course MAT 7999: Master's Essay Direction (1-3 credits)

IE 7860 Intelligent Analytics (3 credits) - Computational intelligence methods used to solve complex analytics problems and develop decision support systems. Project-centric approach with the goal of developing several analytics solutions for real-world problems.

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