About This Course
Predictive Analytics brings together advanced analytics capabilities spanning ad-hoc statistical analysis, predictive modeling, data mining, text analytics, entity analytics, optimization, real-time scoring, machine learning and more. IBM SPSS Modeler puts these capabilities into the hands of business users, data scientists, and developers.
In this course in the Big Data University you will learn the basics to get started with Predictive Modeling.
After completing this course, you should be able to:
- Describe what Predictive Modeling is all about and know why you would want to use it
- Understand the CRISP-DM methodology and the IBM SPSS Modeler Workbench
- Understand Common Modeling Techniques
- Use IBM SPSS Modeler to solve a Kaggle competition
- Explore, Prepare, Model and Evaluate your data using IBM SPSS Modeler
About your instructors
Mikhail Lakirovich is an Advisory Data Scientist, Strategy Consulting at IBM. He joined IBM in 2014 and worked as a Technical Product Marketing Manager. Prior to his work at IBM, Mikhail worked as a marketing manager at Baxter International Inc.
Armand Ruiz is the product manager of Advanced Analytics at IBM. He joined IBM in 2011 and has worked as a Product Engineer, Research Developer for Smarter Cities, Developer Advocated for IBM Data Science, and Technical Product Manager for SPSS Programmability and Extensibility. Prior to IBM, Armand worked as a Data Scientist at Vodafone.
Greg Filla is a Product manager intern - SPSS at IBM. He is a Masters Candidate - Predictive Analytics at DePaul University. Prior to this work, Greg was a ACA HR Analyst at LaSalle Network, and a Retirement Plan Specialist at RPS Benefits.