Predictive analytics has moved from a niche technology used in a few industries, to one of the most important technologies any data-driven business needs. Because of the demand, there has been rapid growth in university programs in machine learning and data science. These teach the science well, but do not describe the tradeoffs and the “art” of predictive analytics.

This workshop will cover the practical considerations for using predictive analytics in your organization through the six stages in the predictive modeling process:

1. Business Understanding – how to define problems to solve using predictive analytics
2. Data Understanding – how to describe the data
3. Data Preparation – how and why to create derived variables and sample data
4. Modeling – the most important supervised and unsupervised modeling techniques
5. Evaluation – how to match modeling accuracy with business objectives to select the best model
6. Deployment – how to use models in production

Practical tips are given throughout the workshop including:

• Which transformations of data should be used for which algorithms?
• Which algorithms match what kinds of problems?
• How does one measure model accuracy in a way that makes sense for the business?
• How does one avoid being fooled with predictive models, thinking they are behaving well when in reality they are brittle and doomed to fail?

Case studies that illustrate principles will be used throughout the workshop, drawn from Mr. Abbott’s more than 20 years of consulting experience in data mining and predictive analytics. The techniques are software independent, but Mr. Abbott will illustrate them using several commercial and open source software packages.

Every registered attendee will receive a copy of Mr. Abbott’s book “Applied Predictive Analytics”

This workshop will benefit anyone who has worked with data, whether in spreadsheets, statistics programs, or commercial predictive modeling software, and would like to learn the practical side of predictive analytics.

Attendees receive a course materials book and an official certificate of completion at the conclusion of the workshop.

Workshop Schedule:

• 09:00
  • Software installation
• 09:15
  • Workshop program starts
• 10:30 – 11:00
  • Morning Coffee Break
• 12:30 – 13:30
  • Lunch
• 15:00 – 15:30
  • Afternoon Coffee Break
• 17:00
  • End of the Workshop

Intended Audience:

• Practitioners: Analysts who would like a tangible introduction to predictive analytics or who would like to experience analytics using a state-of-the-art data mining software tool.
• Technical Managers: Project leaders, and managers who are responsible for developing predictive analytics solutions, who want to understand the process.

Knowledge Level: Familiar with the basics of predictive modeling.

About the instructor

Dean Abbott is Co-Founder and Chief Data Scientist of SmarterHQ, and President of Abbott Analytics, Inc. in San Diego, California. Mr. Abbott is an internationally recognized data mining and predictive analytics expert with over two decades of experience applying advanced data mining algorithms, data preparation techniques, and data visualization methods to real-world problems, including fraud detection, risk modeling, text mining, personality assessment, response modeling, survey analysis, planned giving, and predictive toxicology.

Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and co-author of IBM SPSS Modeler Cookbook (Packt Publishing, 2013). He is a highly-regarded and popular speaker at Predictive Analytics and Data Mining conferences and meetups, and is on the Advisory Boards for the UC/Irvine Predictive Analytics Certificate as well as the UCSD Data Mining Certificate programs.

He has a B.S. in Mathematics of Computation from Rensselaer (1985) and a Master of Applied Mathematics from the University of Virginia (1987).