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 I

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

Decision & Risk Analysis

  • Decision analysis models and the value of information
  • Using data to assess uncertainty
  • Monte Carlo simulation models

Predictive Analytics

  • Overview of big data challenges and the data mining process
  • Dimension reduction and principal component analysis
  • Association rules and cluster analysis
  • Evaluation, classification, and predictive performance

Statistics for Managerial Decision 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

Data Storytelling

  • A principal challenge for anyone working with ubiquitous data is communicating results of an analysis to stakeholders. This course teaches students the art of clear, effective, and engaging data presentation with attention to the business necessity of translating complex technical subjects into actionable insights for a lay audience. Students will harness the power of storytelling for the strategic benefit of an organization by turning a raw set of data into a compelling message that resonates with an intended audience.

Time Series Analysis

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

Ethics in Business Analytics

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

Machine Learning

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

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.