Workshop

Monday, October 24, 2016 in New York
Full-day: 8:30am - 4:30pm

Room: 1A24

Advanced Methods Hands-on:
Predictive Modeling Techniques

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.

Workshop Description
Once you know the basics of predictive analytics and have prepared data for modeling, which algorithms should you use? What are the similarities "best practices" e e attention will be paid to learning and experiencing the influence various options have on predictive models so that attendees will gain a deeper understanding of how the algorithms work qualitatively.

Participant background
Participants are expected to know the principles of predictive analytics. This hands-on workshop requires all participants to be involved actively in the model building process, and therefore must be prepared to work independently or in a small team throughout the day. The instructor will help participants understand the application of predictive analytics principles, and will help participants overcome software issues throughout the day.

Course Notes and Free Textbook:
All data and Statistica files needed for the workshop will be provided on a USB drive and will also be made available via an internet link. Paper copies of the workshop notebook will be distributed to attendees upon arrival. All attendees will also receive a paperback copy of Dean's book, Applied Predictive Analytics.

Software
While the majority of concepts covered during this workshop apply to all predictive analytics projects - regardless of the particular software employed - this workshop's hands-on experience is achieved using Statistica Data Miner. A license will be made available to participants for use on that day (included with workshop registration).

Hardware:
Attendees will be able to try the techniques with Statistica Data Miner during the workshop using their own laptops. Your laptop must be running a Windows operating system (XP, Vista, 7, or 8). If you are bringing a Macintosh computer, you will need to have a Windows virtual machine running on the laptop. If your company has strict security policies, such as requiring administrator privileges before installing software or disabling USB ports, please make arrangements before the workshop to install the software and download the workshop data files. Internet at the workshop site may not be adequate to download all the materials in a timely manner.

A more detailed description of software and hardware requirements are available here (PDF file):

Attendees may receive an official certificate of completion upon request at the completion of the workshop.

Schedule

Software installation at 8:30am
Workshop program starts at 9:00am
Morning Coffee Break at 10:30 - 11:00am
Lunch provided at 12:30 - 1:15pm
Afternoon Coffee Break at 2:30 - 3:00pm
End of the Workshop: 4:30pm

Instructor

Dean Abbott, President, Abbott Analytics, Inc.

Dean Abbott is Co-Founder and Chief Data Scientist of Smarter Remarketer, Inc., 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 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).