Business Analytics

Learning outcomes

After successfully completing this digital badge program, participants will:

  • Define and explain the business analytics process (problem definition; data preparation; technical analysis and modeling; evaluation of results; implementation and deployment).
  • Understand and describe the functionality and role of analytic techniques in data mining and predictive analytics.
  • Perform data exploration to evaluate variables for data mining and to suggest and implement approaches to handle data problems such as missing values, outliers, and skewed distributions.
  • Construct, validate, and interpret data mining and predictive analytics models with multivariate data sets using various software tools.
  • Compute and interpret key predictive accuracy measures and methods including lift charts, gain charts, and ROC curves.
  • Understand and utilize supervised models such as multiple regression, logistic regression, classification trees, and neutral networks.
  • Understand and utilize unsupervised models including principal components analysis and cluster analysis.
  • Apply data mining and predictive analytic techniques to problems in areas such as fund raising, retailing, direct marketing, market segmentation, bankruptcy prediction, credit scoring, and fraud detection.

Format

For more information about the next digital badge program, please contact Kai Westerfield, Program Manager, at kmwester@iu.edu.