Workshop

Thursday, April 7, 2016 in San Francisco
Full-day: 9:00am - 4:30pm

Room: Nob Hill A

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.

More statements of testimony:

"Outstanding instruction."
– Robert Lake, Cisco

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 and differences? Which options affect the models most? This workshop dives into the key predictive analytics algorithms for supervised learning,including decision trees, linear and logistic regression, neural networks, k-nearest neighbor, support vector machines, and model ensembles. Attendees will learn "best practices" and 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.

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.

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

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

Schedule

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

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 has taught full-day data mining and predictive analytics training courses and hands-on workshops to thousands . He teaches predictive analytics and text mining courses through UC Irvine extension and UC San Diego extension programs.