About This Course
In this course, we will be focusing on predictive modeling fundamentals. These are the mathematical algorithms, which are used to "learn" the patterns hidden in data.
Learn the crucial step in the Big Data Lifecycle: using big data to make decisions!
- Possess the modeling skills needed by companies all over the world to go beyond storing big data to understanding big data
- Learn how to use these skills to make decisions such as cancer detection, fraud detection, customer segmentation and predicting machine downtime.
- Get introduced to the data mining process and modeling techniques using one of the most popular software, IBM's SPSS Modeler.
- Learn how to build models on trained data, test the model with historical data, and use qualifying models on live data or other historical untested data.
- Save or earn companies millions of dollars with your decisions!
- Module 1 - Introduction to Data Mining
- CRISP-DM Methodology
- Introduction to SPSS Modeler - predictive data mining workbench
- SPSS Modeler Interface
- Module 2 - The Data Mining Process
- Business Understanding
- Data Understanding
- Data Preparation
- Module 3 - Modeling Techniques
- Introduction to Common Modeling Techniques
- Cluster Analysis (Unsupervised Learning)
- Classification & Prediction (Supervised Learning)
- Classification - Training & Testing
- Sampling Data in Classification
- Predictive Modeling Algorithms in SPSS Modeler
- Automated Selection of Algorithms
- Module 4 - Model Evaluation
- Metrics for Performance Evaluation
- Accuracy as Performance Evaluation tool
- Overcoming Limitations of Accuracy Measure
- ROC Curves
- Module 5 - Deployment on IBM Bluemix
- Scoring new data
- Deployment of the Predictive Model
- What is IBM Bluemix?
- Predictive Modeling service: Deployment in the Cloud
- SPSS Collaboration and Deployment Services
- This course is free.
- It is self-paced.
- It can be taken at any time.
- It can be audited as many times as you wish.
Recommended skills prior to taking this course
- Basic knowledge of business statistics
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.
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.
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.