Dhillon School of Business
Overview of Business Analytics
Credit hours: 1.50
Contact hours per week: 3-0-0
This course will examine key components of the business analytics process, including: business analytics frameworks; framing of organizational problems to determine their suitability for an analytics solution; identification and realization of organizational benefits from analytics; common sources of data relevant to organizations; ethical and privacy issues; and identification of key methods used in business analytics.
Prerequisite(s):Admission to a graduate program
Introduction to Business Analytics Methods
Credit hours: 1.50
Contact hours per week: 3-0-0
This course will provide an overview of common analytics methods used in business analytics, as well as selected applications from a variety of business disciplines. The course will emphasize how to choose among methods, interpretation, and linking outcomes to organizational value.
Prerequisite(s):Admission to a graduate program
Predictive Business Analytics I
Credit hours: 1.50
Contact hours per week: 3-1.5-0
This course will explore linear and logistic regression as predictive methods, and examine their use and interpretation in various business contexts. The emphasis will be on the application of the methods and interpretation of results through scenarios and cases, rather than on the underlying theory. Dimensionality reduction methods such as factor analysis will also be introduced. Students will be asked to complete at least one assessment piece that requires them to apply the concepts learnt in the course to their major.
Prerequisite(s):Admission to a graduate program
Predictive Business Analytics II
Credit hours: 1.50
Contact hours per week: 3-1.5-0
This course will explore cluster analysis, decision trees, ensemble methods, and other classification methods such as naïve Bayes, including their use and interpretation in various business contexts. The emphasis will be on the application of the methods and interpretation of results through scenarios and cases, rather than the underlying theory. Students will be asked to complete at least one assessment piece that requires them to apply the concepts learnt in the course to their major.
Prerequisite(s):Admission to a graduate program
Business Decision Analytics
Credit hours: 1.50
Contact hours per week: 3-0-0
This course examines prescriptive analytics such as optimization models and modeling of uncertainty and their role in addressing a variety of business decisions, including pricing, scheduling, budgeting, and logistics. The emphasis will be on the application of the methods and interpretation of results through scenarios and cases, rather than the underlying theory. Students will be asked to complete at least one assessment piece that requires them to apply the concepts learnt in the course to their major.
Prerequisite(s):Admission to a graduate program
Time Series Models and Forecasting for Business
Credit hours: 1.50
Contact hours per week: 3-0-0
This course will examine the key features of time series data and the models that can be used to forecast time series data, including ARIMA models and error, trends, and seasonality models. Business applications of time series models will be examined. The emphasis will be on the application of the methods and interpretation of results through scenarios and cases, rather than on the underlying theory. Students will be asked to complete at least one assessment piece that requires them to apply the concepts learnt in the course to their major.
Prerequisite(s):Admission to a graduate program
Text Analytics for Business
Credit hours: 1.50
Contact hours per week: 3-0-0
This course will introduce students to the principles and methods of text mining to analyze unstructured text. Topics such as sentiment analysis will be examined as well as other business applications. The emphasis will be on the application of the methods and interpretation of results through scenarios and cases, rather than on the underlying theory. Students will be asked to complete at least one assessment piece that requires them to apply the concepts learnt in the course to their major.
Prerequisite(s):Admission to a graduate program
Communicating Analytics Findings
Credit hours: 1.50
Contact hours per week: 3-0-0
This course will help students develop visualizations to share analytics findings with a variety of audiences. Storytelling as a means of communicating findings will be introduced, along with basic principles of dashboard design, and communicating uncertainty. Students will be asked to complete at least one assessment piece that requires them to apply the concepts learnt in the course to their major.
Prerequisite(s):Admission to a graduate program
Big Data Analytics
Credit hours: 1.50
Contact hours per week: 3-0-0
This course will introduce students to the principles, technologies and methods involved in working with big data. Common business applications will be explored. The emphasis will be on the application of the methods and interpretation of results through scenarios and cases, rather than on the underlying theory. Students will be asked to complete at least one assessment piece that requires them to apply the concepts learnt in the course to their major.
Prerequisite(s):Admission to a graduate program
Introduction to Consulting
Credit hours: 1.50
Contact hours per week: 3-0-0
Students will gain an understanding of the consulting process, issue and problem diagnosis, consulting approaches and styles, client-stakeholder relationships, and management of change. Tools for organizing project work will be introduced. Students will be asked to complete at least one assessment piece that requires them to apply the concepts learnt in the course to their major.
Prerequisite(s):Admission to a graduate program
Business Analytics 5010/Data Science 5010
Introduction to Data Science and Analytics in Python I
Credit hours: 3.00
Contact hours per week: 0-3-0
The basics of data science and data science workflow. Data science process models, framing of problems, preparation of data, visualization of data, predictive models, clustering algorithms, and interpretation and communication of findings. Statistical topics such as summary statistics, distributions, correlation, and regression will also be introduced. This course is based on Python, which is a leading programming language for data science. No prior knowledge of Python or statistics is assumed.
Prerequisite(s):Admission to a graduate program
Equivalent:Statistics 5850 (Introduction to Data Science with Python) (prior to 2022/2023)
Business Analytics 5020/Data Science 5020
Data Visualization
Credit hours: 1.50
Contact hours per week: 0-1.5-0
Sophisticated visual representations appropriate for industry and academic applications which serve to motivate analyses, detect flaws, and communicate data-driven findings. Topics include choosing a visualization approach, best practices for data visualization, and generation and interpretation of various visual representations in Python and other applications.
Prerequisite(s):Business Analytics 5010/ Data Science 5010
Business Analytics 5050/Data Science 5050
Data Wrangling
Credit hours: 1.50
Contact hours per week: 0-1.5-0
Preparation of data for analysis. Topics include understanding problem requirements, data profiling and structuring, data imputation, integration of data from multiple sources, data duplication, reformatting, dimensionality, metric construction, loading, metadata, and dimensions of data quality.
Corequisite(s):Business Analytics 5010/ Data Science 5010
Business Analytics 5110/Data Science 5110
Introduction to Data Science and Analytics in Python II
Credit hours: 3.00
Contact hours per week: 0-3-0
Introduction to machine learning tools in Python, including deep learning tools for unstructured datasets. Students will learn to set up these analytical tools, choose tools to solve specific problems, use them to inform decisions, and communicate their results to non-specialists.
Prerequisite(s):Business Analytics 5010/ Data Science 5010
Business Analytics 5140/Data Science 5140
Data Management
Credit hours: 1.50
Contact hours per week: 0-1.5-0
Introduction to common forms of data storage, such as relational databases, file structures, and data warehouses. Overview of finding and retrieving data from data management systems (e.g., SQL) and web sources.
Corequisite(s):Business Analytics 5010/Data Science 5010
Master of Science (Management) Placement
Credit hours: 0.00
Used to maintain continuous registration for students not otherwise taking courses in a particular term.
Prerequisite(s):Admission to the Master of Science (Management) program
Grading:'X' grade
Business Analytics Experiential Project
Credit hours: 6.00
Other hours per term: 20-0-0
Students will apply data analytics methods to an organizational problem or opportunity and develop a paper documenting all aspects of the project, including organizational problem or opportunity, framing as a data analytics problem, data retrieval, data preparation, model selection and rationale, evaluation, refinements, and plans to realize organizational benefits. Students who cannot identify an organizational partner can work on an industry problem or other issue with permission of the course instructor.
Prerequisite(s):Admission to the Master of Science (Management) program and successful completion of coursework