Workshop sponsored by:
Workshop
Toronto, Monday, May 12, 2014
Full-day: 8:30am - 4:30pm
Room: Room: 202D 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
and differences? Which options effect 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" 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.
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 a leading predictive analytics software tool. A license will be made available to participants for use on that day (included with workshop registration).
Hardware: Bring Your Own Laptop
Each workshop participant is required to bring their own laptop running Windows. Instructions will be provided to install a trial license for the analytics software used during this training program.
Attendees receive a course materials book and an official certificate of completion at the conclusion 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 President of Abbott Analytics in San Diego, California. Mr. Abbott has over 21 years of experience applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, risk modeling, text mining, response modeling, survey analysis, planned giving, and predictive toxicology. In addition, Mr. Abbott serves as chief technology officer and mentor for start-up companies focused on applying advanced analytics in their consulting practices.
Mr. Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade to audiences of up to 400, including PAW, KDD, AAAI, IEEE and several data mining software users conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. Mr. Abbott also has taught applied data mining courses for major software vendors, including SPSS-IBM Modeler (formerly Clementine), Unica PredictiveInsight (formerly Affinium Model), Enterprise Miner (SAS), Model 1 (Group1 Software), and hands-on courses using Statistica (Statsoft), Tibco Spotfire Miner (formerly Insightful Miner), and CART (Salford Systems).
