1. 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 supportbusiness 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