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Dean Abbott Instructor:
Dean Abbott
Abbott Analytics, Inc.


Thursday, May 18, 2017 in San Francisco
Full-day: 8:30am - 4:30pm

Advanced Methods:
Data Preparation and 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.

What prior attendees have exclaimed

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" 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.

Applied Predictive Analytics Course Notes and Free Textbook:
Course notes and all data needed for the workshop will be provided on a USB drive at the workshop, 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 (Optional):
While the majority of concepts covered during this workshop apply to all predictive analytics projects - regardless of the particular software employed - attendees of this workshop can gain additional insight by following along in the demonstrations by using analytics software. Mr. Abbott will be conducting demos using the open source software KNIME.

Hardware (Optional):
Attendees will be able to try the techniques using KNIME during the workshop using their own laptops. Your laptop may run KNIME using Windows, Macintosh, or Linux operating systems (please consult for minimum requirements). We recommend you download and install KNIME prior to the workshop because internet bandwidth at the workshop site is not guaranteed to be fast enough for a timely download of the software.

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


Price and Registration Info:
  • Software installation assistance, if needed 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


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).

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