Indiana University – Kelley School of Business

Business Analytics:

Define and explain the business Analytics process - Problem Definition , Data Preparation, Technical Analysis and Modeling, Evaluation, Implementation and Deployment

Perform Analytics Tasks with SAS - JMP Pro and Excel Construct, Validate, and Interpret Data mining and Predictive Analytics

models using large multivariate data sets. Apply data mining and predictive analytic techniques in the areas of Fund

raising, Retailing, Direct marketing, Market segmentation, Bankruptcy prediction, Credit scoring, and Fraud detection.

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.

Compute and interpret key predictive accuracy measures and methods including lift charts, gain charts, and ROC curves.

Data Warehousing, Mining and Visualization:

Understand and utilize unsupervised models -Principal components analysis, Cluster analysis, and Market basket analysis

Develop Dashboards and Balanced Scorecards to support business decisions Data Visualization using IBM Watson Analytics and Tableau Managing BI Business risks Designing for Data Quality to achieve business success Data Profiling, Master Data Management and ETL Techniques. Dimensional Modeling, Data Governance and OLAP