Online Graduate Certificate in Data Analytics

Data Analysis Methods 

This course covers the fundamental concepts of applied data analysis methods. Various aspects of linear and logistic regression models are introduced, with emphasis on real data applications. Students are required to analyze data using major statistical software packages. BANA 7038 should not be taken for credit by MS-Business Analytics students.

Data Visualization 

This course provides an introduction as well as hands-on experience in data visualization. It introduces students to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making.

Statistical Computing

This is a course on the use of computer tools for data management and analysis. The focus is on a few popular data management and statistical software packages such as SQL, SAS, SPSS, S Plus, R, and JMP although others may be considered. Data management and manipulation techniques including queries in SQL will be covered. Elementary analyses may include measures of location and spread, correlation, detection of outliers, table creation, graphical displays, comparison of groups, as well as specialized analyses.

Advanced Health Care Data Analytics, Business Intelligence, and Reporting

This course teaches the use of healthcare data to make decisions and transform healthcare delivery and the health of individuals and populations. The course concentrates on big and small data, and structured and unstructured data. Tools, applications and approaches for health data analytics are taught. This course covers topics such as statistical approaches; data, web and textmining; data visualization, simulation, modeling and forecasting. Key regulatory health and healthcare reporting requirements are taught.

Human Resource Analytics

This course will serve as an introduction to Human Resource Analytics. We will explore the use of analytics within the Human Resource functions of employee benefits, compensation, employee and labor relations and workforce development through guest speakers and class case studies. We will also explore the importance of technology to the overall analytic effort and how the right tools and talent help the effort to be successful. 

Optimization

An introduction to modeling, solving with state-of-the-art software, and interpreting the results for real-world linear, integer, and  optimization under uncertainty applications. Solution techniques and analyses covered include graphical approaches, the simplex method, and sensitivity for linear optimization; branch-and-bound and cutting plane techniques for integer optimization; and two-stage stochastic programming and robust optimization for optimization under uncertainty. Upon completion of this course, students will be able to formulate real applications as mathematical problems, understanding the underlying assumptions, and the scalability/difficulty of the proposed models. 

Data Wrangling

This course provides an intensive, hands-on introduction to data management and data manipulation. You will learn the fundamental skills required to acquire, munge, transform, manipulate, and visualize data in a computing environment that fosters reproducibility.

Simulation Modeling and Methods

Building and using simulation models of complex static and dynamic, stochastic systems using both spreadsheets and high-level simulation software. Topics include generating random numbers, random variates, and random processes, modeling systems, simulating static models in spreadsheets, modeling complex dynamic stochastic systems with high-level commercial simulation software, basic input modeling and statistical analysis of terminating and steady-state simulation output, and managing simulation projects. Applications in complex queueing and inventory models representing real systems such as manufacturing, supply chains, healthcare, and service operations.

Data Mining I 

This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include data preprocessing, k-nearest neighbors, generalized linear regression, subset and LASSO variable selection, model evaluation, cross validation, classification and regression trees.

Data Mining II 

This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include advanced trees: bagging, random forests, boosting; nonparametric smoothing methods; generalized additive models; data preprocessing/scaling; neural networks; deep learning; cluster analysis; association rules.

Forecasting and Time Series Methods 

This is a course in the analysis of time series data with emphasis on appropriate choice of models for estimation, testing, and forecasting. Topics or methodologies covered include Univariate Box-Jenkins for fitting and forecasting time series; ARIMA models, stationarity and nonstationarity; diagnosing time series models; transformations; forecasting: point and interval forecasts; seasonal time series models; modeling volatility with ARCH, GARCH; modeling time series with trends; and other methods.

Applied Statistical Methods 

This course covers applied statistical methods, including topics of frequency distributions, estimation, hypothesis testing, point and interval estimation for mean and proportion; comparison of two populations; goodness of fit tests, one factor ANOVA. Major statistical software is used.

Applied Linear Regression

This course covers applied linear regression, including topics of fitting and drawing inferences from simple and multiple linear regression models; residual diagnostics; model correction procedure for linear regression; variable selection. Major statistical software is used. 

Machine Learning Design for Business 

This course provides a framework for developing real-world machine learning systems that are deployable, reliable, and scalable. Designing machine learning systems is the process of defining the software architecture, infrastructure, algorithms, and data for a machine learning system to satisfy specified business requirements. Without deliberate design, machine learning systems can get outdated quickly because (1) the tools continue to evolve, (2) business requirements change, and (3) data distributions constantly shift.
Students will learn about data management, data engineering, feature engineering, approaches to model selection, training, scaling, and how to continually monitor and deploy changes to ML systems for successful business applications. They will also be exposed to managing the human side of ML projects such as team structure and business metrics.

Artificial Intelligence and Machine Learning Applications in Decision Modeling 

This course helps develop knowledge of how to integrate Machine Learning (ML) and Artificial Intelligence (AI) within data-driven decision modeling pipelines. The class presents a managerial perspective on existing traditional techniques in data-driven decision modeling and contrast them with emerging approaches in which ML and AI are integrated into the decision-making pipeline. Students are introduced to state-of-the-art software tools and packages and will have the opportunity to implement basic data-driven ML/AI-based optimization models. 

Special Topics in Business Analytics 

This course is used to explore topics of current interest in the BANA domain, that do not fall within the scope of any of the regularly scheduled courses. By the nature of the course, specific topics covered will vary with each offering.

Data Management

This course provides an introduction to the use and design of databases to store, manipulate and query data. The course introduces the structured query language (SQL) used to manage data. Students who complete this course should understand how to use SQL for basic data manipulation and queries. This course is intended for users of existing databases to extract needed information and should not be taken by MSIS students or those students who wish to learn detailed database design techniques.

Database Systems

This course provides a comprehensive introduction to data modeling and database design, covering the full process from conceptual modeling to database implementation. Students will learn to analyze user requirements and develop Entity-Relationship (ER) models, map them to relational models, and apply normalization techniques to refine database structures. The course also covers Structured Query Language (SQL) for data definition (DDL), data manipulation (DML), and data control (DCL). Through hands-on workshops and lab sessions, students will gain practical experience in database normalization, optimization, and query development to design and implement efficient, scalable database solutions for real-world applications

Data Warehousing and Business Intelligence 

This course is designed for the comprehensive learning of data warehousing technology for business intelligence. Data warehouses are used to store (archive) data from operational information systems. Data warehouses are useful in generating valuable control and decision-support business intelligence for many organizations in adjusting to their competitive business environment. This course will introduce students to the design, development and operation of data warehouses. Students will apply and integrate the data warehousing and business intelligence knowledge learned in this course in leading software packages.

Big Data Integration 

This course presents an overview of the principles of data integration, the fundamental basis for developing useful and flexible business intelligence platforms. Modern data integration needs differ from traditional approaches in four main dimensions that parallel differences between big data and traditional data: volume, velocity, variety, and veracity.

Survey of Machine Learning and Artificial Intelligence

This course is a survey of Machine Learning (ML) and Artificial Intelligence (AI) from the Data Scientist’s perspective.  It explores ML and AI topics, current and emerging technologies, and applications for students to gain understanding of the successful implementation of ML and AI to address key business and industry problems. 

Special Topics in Information Systems

This course is used to explore topics of current interest in the IS domain, that do not fall within the scope of any of the regularly scheduled courses. By the very nature of the course, specific topics covered will vary with each offering.