Courses and Descriptions

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Required courses

Note: BAIS courses were listed previously as MSCI.

Data and Decisions (BAIS:9100 or MBA:8150) formerly Business Analytics 
Introduction to analytical techniques for making business decisions; utilizing Excel for application of descriptive and predictive analytical tools to solve practical business problems using real world data; dealing with uncertainty in decision making; formal probability concepts and statistical methods for describing variability (decision trees, random variables, hypothesis testing); application of techniques (linear regression, Monte Carlo simulation, linear optimization) to model, explain, and predict for operational, tactical, and strategic decisions. (3 s.h.)

Data Management and Visual Analytics (BAIS:6050)
Understanding how data is stored in databases and learning the tools used to access the data is key to creating data sets used to answer many business questions; how to manage and access data in relational databases using Structured Query Language (SQL); basic principles of visual analytics using Tableau; techniques for presenting data retrieved from databases. Requirements: enrollment in graduate business analytics program. (3 s.h.)

Data Programming in R (BAIS:6060)
Introduction to principles and practices of handling, cleaning, processing, and visualizing data using R programming language; basic programming skills that can be applied to software development in any programming language; variables and data types, control structures, functions and subroutines, arrays and other simple data structures. (3 s.h.)

Data Programming in Python (BAIS:6040)
Introduction to principles and practices of handling, cleaning, processing, and visualizing data using the Python programming language; basic data programming skills that can be applied to software development in any high-level programming language; data types, control structures, functions and modules, and other useful libraries for data manipulation and machine learning applications in Python. (3 s.h.)

Data Science (BAIS:6070)
Underlying concepts and practical computational skills of data-mining tools including penalty-based variable selection (LASSO), logistic regression, regression and classification trees, clustering methods, principal components and partial least squares; analysis of text and network data; theory behind most useful data mining tools and how to use these tools in real-world situations; software for analysis, exploration, and simplification of large high-dimensional data sets. Prerequisite: Data and Decisions (BAIS:9100 or MBA:8150). (3 s.h.)

Advanced Analytics (BAIS:9110)
Development of data-driven, problem-solving skills for prediction of uncertain outcomes and prescription of business solutions; linear and nonlinear regression, Monte Carlo simulation, forecasting, data mining, and optimization utilizing spreadsheets and dedicated software packages. Prerequisite: Data and Decisions (BAIS:9100 or MBA:8150). (3 s.h.)

Master's degree elective courses

Note: BAIS courses were listed previously as MSCI.

Data Programming in Python (BAIS:6040)
Introduction to principles and practices of handling, cleaning, processing, and visualizing data using the Python programming language; basic data programming skills that can be applied to software development in any high-level programming language; data types, control structures, functions and modules, and other useful libraries for data manipulation and machine learning applications in Python. (3 s.h.)

Social Analytics (BAIS:6105)
Exploration of collection, management, and analysis of social data (interactions among actors); actors as individuals, organizations, or other collectives; sources for social data including social media, websites, annual reports, press releases, articles, and other traditional media. Prerequisite: (BAIS:6060 or BAIS:9060) or (BAIS:6040). (3 s.h.)

Text Analytics (BAIS:6100)
Concepts and techniques of text mining; practice of using statistical tools to automatically extract meaning and patterns from collections of text documents; topics include document representation, text classification and clustering, sentiment analysis and topic modeling. Prerequisite: (BAIS:6060 or BAIS:9060) or BAIS:6040 and BAIS:6070 or BAIS:9110. (3 s.h.)

Big Data Management and Analytics (BAIS:6110)
Introduction to advanced techniques for managing and analyzing "big" data; non-relational data models, such as semi-structured (e.g., XML) and unstructured (e.g., key-value) data; state-of-the-art big data tools for non-relational data management, such as noSQL databases and distributed databases (e.g., Hadoop); query languages such as HIVE; design and implementation of data analysis methods on these platforms; through exercises and course projects, students will be trained to use the tools introduced to implement analysis tasks on big data sets. Prerequisites: (BAIS:6050 or BAIS:9050) and (BAIS:6060 or BAIS:9060) or BAIS:6040. (3 s.h.)

Applied Optimization (BAIS:6130)
Use of optimization (also called prescriptive analytics or mathematical programming) to make tactical and strategic decisions; advanced optimization skills including data collection and preparation, logical modeling, and solution interpretation and implementation within a software environment; applications in the various functional areas of business are discussed throughout. Prerequisites: (BAIS:9100 or MBA:8150) and (BAIS:6060 or BAIS:9060) or BAIS:6040. (3 s.h.)

Information Visualization (BAIS:6140)
Exposure to problems and challenges of effectively interpreting and communicating the pervasive data that surround us; students cover the area of information visualization, grounded in theoretical foundations of visual perception, cognition, information design, human-computer interaction, and analysis of quantitative, unstructured, and relational data; lecture/seminar format with discussion of assigned readings, critiquing visualization examples, hands-on experience with a commercial information visualization application, and exploration of select open-source information visualization tools and toolkits.

Financial Analytics (BAIS:6150)
Businesses as well as investors are affected by fluctuating treasury bond rates, equity prices, and foreign exchange rates, and the risk must be measured; students focus on gaining knowledge of the classic financial models and statistical and risk metrics and scaling them up with analytics techniques (sorting with thresholds, portfolio optimization, decision trees, and database programming) to find the best investments based on historical data sets; beginning with descriptive analytics and pushing into predictive and prescriptive analytics, students build a software simulation laboratory using R. Prerequisites: (BAIS:9100 or MBA:8150) and (BAIS:6060 or BAIS:9060) or BAIS:6040. (3 s.h.)

Healthcare Analytics (BAIS:6180)
Clinical data management is essential for evaluating evidence-based practice/performance-improvement projects; a high quality data management plan provides key stakeholders with information necessary to make decisions; plan components include identified processes and outcomes linked to variables and data sources, adequate statistical power, data cleaning and manipulation techniques, statistical methods, and meaningful presentation of variables that address stakeholder concerns and questions; students gain knowledge and skills necessary to develop and execute a data management plan within a final project. Prerequisites: (BAIS:9100 or MBA:8150) and BAIS:6050. (3 s.h.)

Forecasting (BAIS:6190)
Forecasting plays a central role in business decision making. Accurate forecasts are needed when making decisions about investments, resource allocations, schedules and inventory levels. The course discusses quantitative forecasting tools; the extrapolation of time series data such as daily, weekly or monthly sales; and tools include exponential smoothing methods and time series extrapolations from autoregressive and ARIMA Box-Jenkins models. Regression models that predict a variable of interest from its own history, as well as any other available information such as sales promotions and price reductions, and methods for assessing the performance of forecasting methods are discussed. Prerequisite: BAIS:9100 or MBA:8150. (3 s.h.)

Data Leadership and Management (BAIS:6210)
Core chief information officer (CIO) basics; focus on how to keep technology, systems, and procedures supporting business goal outcomes including management of information technology (IT)) teams, systems selection, vendor negotiation, change, information risk, data integrity, ethics, information system (IS) policies, strategies, cloud computing, and budget. (3 s.h.)

Supply Chain Analytics (BAIS:9160)
Supply chain analytics applications for decision making, including demand forecasting, inventory management, capacity planning, and supply chain coordination. Prerequisite: BAIS:9100 or MBA:8150. (3 s.h.)

Digital Marketing Analytics (MKTG:9165)
Examine applications for product forecasting, product development, promotional strategy, online marketing, and customer databases. Prerequisite: BAIS:9100 or MBA:8150. (3 s.h.)

Master's degree required experience project

Analytics Experience (BAIS:6120)
Students work in groups to complete semester-long projects pertaining to business analytics; all project stages are addressed including problem definition, data cleaning, analysis, and final presentation; appropriate tools from required courses used throughout. Groups may partner with an area business. Prerequisites: All certificate courses plus at least one Master's elective. (3 s.h.)