Mendoza School of Business

MSBA-Chicago Course Descriptions

Introduction to Business Analytics

  • Business and technology trends driving interest in analytics and big data
  • Understanding the role of analytics professionals in business organizations
  • Data, analytics, and business problem framing

Data Management 

  • Relational database concepts, entity-relationship diagrams, metadata & business rules
  • Query formulation using structured query language (SQL)
  • Data cleaning and transformation skills using R

Data Visualization

  • Building core skills for visual analysis
  • Visualization techniques and tools
  • Promising trends in visualization

Statistics for Managerial Decision Making I

  • Probability distributions
  • Inference and estimation from samples
  • Correlation and regression
  • Categorical data analysis

Advanced R

  • Develop advanced aptitude in R coding for analytics applications
  • Create R Markdown documents that integrate narrative and code to communicate an analytics message
  • Use application programming interfaces (APIs) to interact with web applications in R
  • Perform web scraping to retrieve data from websites for analysis in R code

Ethics in Business Analytics

  • Elements of big data ethics: Identity, privacy, ownership and reputation
  • Overview of current practices, issues and concerns

Statistics for Managerial Decision Making II

  • Advanced regression analysis and general linear models
  • Methods for categorical and limited dependent variables
  • Models for hierarchically structured and longitudinal data
  • Planning and designing studies and surveys

Machine Learning

  • Supervised and unsupervised machine learning techniques
  • Ensemble methods and advanced machine learning algorithms
  • Emerging trends and issues

Data Storytelling

  • Data presentation methods
  • Translating data insights for a lay audience
  • Transforming raw data into compelling messages

Time Series Analysis

  • Time series data methods and issues
  • Other techniques and approaches
  • Evaluating and implementing forecasting models

Python for Analytics 

  • Foundations of Python programming
  • Data wrangling and analysis in Python
  • Building and applying models to business problems

Cloud Analytics

  • Cloud computing solutions for common business analytics problems
  • Full-stack cloud solutions: Infrastructure as a Service (IaaS), Platform as a Service (PaaS, and Software as a Service (SaaS)

Unstructured Data Analytics

  • Methods for unstructured data collection, exploration and visualization
  • Sentiment analysis, pattern recognition, tagging and natural language processing
  • Approaches to image processing with neural nets

Marketing and Customer Analytics

  • Estimating return on marketing investments
  • Pricing and new product introduction decisions
  • Customer Lifetime Value (CLV) analysis
  • Optimal level of advertising and advertising allocation analysis

Emerging Issues in Analytics and Big Data

  • Building organizational analytics maturity
  • Emerging technology trends and directions
  • Practical challenges in implementing analytics

Analytics Capstone Project

  • Intensive integrative analysis of problems and data provided by an industry partner
  • Effective communication of analytics results
  • Understanding analytics solution deployment and lifecycle issues

All course listings and descriptions are subject to change.