Workshop
Thursday, October 15, 2015 in Washington, DC
Full-day: 9:00am - 4:30pm
Room: Farragut SquareThe Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes
SOLD OUT
A free copy of John Elder's book Statistical Analysis and Data Mining Applications is included.
Intended Audience: Interested in the true nuts and bolts
Program Level: Intermediate
Recommend Field of Study:
- Recommend Field of Study:
- Computer Science
- Analytics
- Statistics
- Mathematics
- Finance and Marketing
Instructional Method: Group – Live CPE Credit's received for completion:
- 1.5 CPE Credits for Management Adv Service
- 1.5 Statistics
- 1.5 Specialized Knowledge and Apps
- 1.5 Business mgmt. and org
Knowledge Level: Familiar with the basics of predictive modeling.
Attendees will receive an electronic copy of the course notes via USB drive.
More statements of testimony:
"You don't know what you don't know ... after this course, I now know what I don't know and what I should develop greater understanding in for both myself, my company and our clients"
– Sean Liddle, Deloitte
"Some very complex topics were explained very comprehensively and clearly, and put into a wider context of how to use these in real-life situations."
– Colin Styles, Information Architect, Sussex Health Informatics Service
"I can't remember when I walked away from a one-day training course so filled with insights and new ideas. Thank you for your real work experiences and great examples about analytics and modeling for decision making. Because of your training on modeling techniques, you helped me rethink my approach to data mining and predictive analytics. You were generous with your time and with the Q&A. I would like to learn more than just the basics you covered, but what I did learn gave me a foundation for better management techniques and using data analytics."
–Manager Data Analytics, US Postal Service Office of Inspector General
Workshop Description
Predictive analytics has proven capable of enormous returns across industries – but, with so many core methods for predictive modeling, there are some tough questions that need answering:
- How do you pick the right one to deliver the greatest impact for your business, as applied over your data?
- What are the best practices along the way?
- And how do you avoid the most treacherous pitfalls?
This one-day session surveys standard and advanced methods for predictive modeling.
Dr. Elder will describe the key inner workings of leading algorithms, demonstrate their performance with business case studies, compare their merits, and show you how to pick the method and tool best suited to each predictive analytics project. Methods covered include classical regression, decision trees, neural networks, ensemble methods, resampling and more.
The key to successfully leveraging these methods is to avoid “worst practices”. It's all too easy to go too far in one's analysis and “torture the data until it confesses” or otherwise doom predictive models to fail where they really matter: on new situations.
Dr. Elder will share his (often humorous) stories from real-world applications, highlighting the Top 10 common, but deadly, mistakes. Come learn how to avoid these pitfalls by laughing (or gasping) at stories of barely averted disaster.
If you'd like to become a practitioner of predictive analytics – or if you already are, and would like to hone your knowledge across methods and best practices, this workshop is for you!
What you will learn:
- The tremendous value of learning from data
- How to create valuable predictive models for your business
- Best Practices by seeing their flip side: Worst Practices
The workshop is filled with real-world stories and explanations of methods that are visual and analogy-based, rather than mathematical. Each section is designed to make clear the gist of its concept to a complete novice, and to conclude with intriguing ideas for advanced researchers. Experience has shown that attendees who get the very most out of the course:
- Have some experience with programming, or algorithmic approaches to problem-solving
- Have taken an introductory course in probability and statistics … but most importantly
- Have a problem to solve that inspires and anchors their learning as techniques are introduced
Participants will learn how to:
- Run a decision tree, regression, and neural network on a new problem.
- Split data and use resampling to best assess a model's accuracy.
- Use visualization to discover outliers and feature transformations.
- Combine competing models to improve accuracy.
- Recognize examples of the top analytics mistakes, to avoid them!
Why Attend?
View Dr. Elder describing the course in this brief video:
Schedule
- Workshop starts at 9:00am
- First AM Break from 10:00 - 10:15am
- Second AM Break from 11:15 - 11:30am
- Lunch from 12:30 - 1:15pm
- First PM Break: 2:00 - 2:15pm
- Second PM Break: 3:15 - 3:30pm
- Workshops ends at 4:30pm
Attendees receive a free copy of John Elder's book Statistical Analysis and Data Mining Applications, an electronic copy of the course notes via USB drive, and an official certificate of completion at the conclusion of the workshop.
Instructor
Dr. John Elder, CEO & Founder, Elder Research, Inc.
Dr. John Elder heads a data mining consulting team with offices in Charlottesville Virginia, San Francisco, Mountain View California, and Manhasset New York (www.datamininglab.com). Founded in 1995, Elder Research, Inc. focuses on investment, commercial and security applications of advanced analytics, including text mining, forecasting, stock selection, image recognition, process optimization, cross-selling, biometrics, drug efficacy, credit scoring, market timing, and fraud detection.
John obtained a BS and MEE in Electrical Engineering from Rice University, and a PhD in Systems Engineering from the University of Virginia, where he's an adjunct professor teaching Optimization or Data Mining. Prior to 15 years at ERI, he spent 5 years in aerospace defense consulting, 4 heading research at an investment management firm, and 2 in Rice University's Computational & Applied Mathematics department. Dr. Elder has authored innovative data mining tools, is a frequent keynote speaker, and was co-chair of the 2009 Knowledge Discovery and Data Mining conference, in Paris.
John's courses on analysis techniques -- taught at dozens of universities, companies, and government labs -- are noted for their clarity and effectiveness. Dr. Elder was honored to serve for 5 years on a panel appointed by the President to guide technology for National Security. His book with Bob Nisbet and Gary Miner, Handbook of Statistical Analysis & Data Mining Applications, won the PROSE award for Mathematics in 2009. His book with Giovanni Seni, Ensemble Methods in Data Mining: Improving Accuracy through Combining Predictions, was published in February 2011. John is a follower of Christ and the proud father of 5.
