MS in Business Analytics Electives
Advanced Marketing Analytics
In this course, students gain an overview of web, social media, and consumer text analytics; analyze consumer data streams from the Internet, mobile devices, and sensors; and handle very large volumes of data. General data analysis software operation for various marketing problems, marketing platform software for general and specific tasks, and learning machines in marketing are also covered.
Data Driven Marketing Strategy
This course is designed to help students understand how analyzing marketplace data can improve business decision-making. The course focuses on the use of quantitative analysis to support marketing mix and resource allocation decisions, and introduces rigorous tools for measuring the effectiveness of marketing expenditures. Traditional marketing measures, such as awareness, preference, loyalty, customer satisfaction, distribution levels, and market share, are also considered.
This course is designed to give undergraduate and master’s level economics students an introduction to basic econometric methods, including economic model estimation and analyses of economic data. Hypothesis formulation and testing, economic prediction and problems in analyzing economic cross-section and time series data are considered.
Advanced Descriptive Analytics
Descriptive analytics focuses on descriptions and reports of what has happened. This course demonstrates advanced descriptive analytics tools, and covers algorithmic methods such as cluster analysis, text mining, and link analysis. Issues related to Big Data and Internet of Things (IoT) are also discussed.
Prescriptive analytics focuses on the use of data from the other business analytics domains, such as descriptive and predictive analytics, to achieve strategic and operational objectives. This course introduces analytical methods and software commonly used in optimization modeling in businesses.
Project Management and Implementation
Project management involves efficiently and effectively managing people and resources to accomplish a new activity, including budgeting, scheduling, and risk management. This course equips students with the tools and techniques used for planning and managing projects, from initiation through implementation.
Advanced Supply Chain Technology
Technological advancements related to supply chain management include sensor systems, Internet of Things, radio frequency identification systems, automated storage and retrieval systems, and distribution routing systems. This course guides you in learning the characteristics of these systems and how they can support and improve supply chain management.
Decision and Risk Analysis
This course explores theory and practice of decision-making under uncertainty, graphical modeling techniques including influence diagram and decisions trees, the value of information, utility theory foundations, risk preference, multi-attribute decision modes, and the economic justification of projects.
Applied Statistics and Quality Control
In this course, students learn the systematic analysis of processes through the use of statistical analysis, methods, and procedures as well as statistical process control, sampling, regression, analysis of variance (ANOVA), quality control, and design of experiments.
A single project can have various moving parts. This course prioritizes project development, selection, planning, budgeting and cost estimation, scheduling, and termination, as well as the project manager’s role.
Survey Analysis and Design
This course provides students with techniques and context in survey creation and analysis. It emphasizes basic issues related to the design and analysis of sample surveys, the basics of questionnaire construction, sampling, data collection, analysis and data presentation.
Principles of Survey Analysis
Serving as an introduction to the basic principles of causality and inductive logic in contemporary social and behavioral science, this course explores one, two, and multi-way layouts in analysis of variance, fixed effects models, and linear regression in several variables. It also overviews the Gauss-Markov-Theorem, multiple regression analysis, and basic principles of experimental and quasi-experimental designs.
Accurately gathering statistics is vital to factual analytics. This course investigates design of probability samples, sampling populations of humans and unique challenges posed by such populations, simple random sampling, stratification, cluster sampling, systematic sampling, multistage sampling, probability proportional to size sampling, area probability sampling, and telephone samples.