Dean Abbott Instructor:
Dean Abbott
President
Abbott Analytics

Workshop sponsored by:

Workshop

Hands-On Predictive Analytics

Monday, October 19, 2009

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, there's no better way to dive in than operating real predictive modeling software yourself - hands-on. "Get your hands dirty" by trying out state-of-the art modeling methods on real data. Working to solve a specific business problem, you will design and execute on a core analytical approach. Prep the data, set up the modeling, push "go" and check out the results.

"Hands-on Predictive Analytics" puts predictive analytics into action. This one-day workshop leads participants through the industry standard data mining process, from Business Understanding through Model Deployment, approaching each stage of this process by driving a state-of-the-art data mining software product. In this way, attendees gain direct experience applying this "best practices" process, and ramp up on an industry-leading tool to boot.

Key process stages covered during the workshop include:

  • Business Understanding
    Participants will review a problem description from a business perspective, and design one or more solutions to that problem using predictive analytics. The solution will include one or more predictive models as determined by the participants. These models will be assessed according to the business objective(s) already defined.
  • Data Understanding
    From a given data set (supplied), participants will examine characteristics of the data (a "data audit") and identify potentially problematic issues. Fields with insufficient information will be discarded.
  • Data Preparation
    Participants will clean fields in the data as necessary, and will derive new attributes as candidate inputs to predictive models
  • Modeling
    Participants will determine which predictive modeling methods to use, and will build several models and assess them as prescribed in the Business Understanding phase. The best model from the competing candidates will be selected to evaluate and deploy.
  • Evaluation
    A final evaluation of the model(s) will be made, and the expected financial benefit of the model(s) will be forecast and graphed. As time permits, an "ensemble" model composed of all the models built by participants will be created to compare with the best individual models - we'll often find that the big "uber-model" is the best model of all.
  • Deployment
    Strategies for real-world model deployment will be assessed, including the application of the predictive model for its intended purpose - to produce scores that predict "tomorrow" across today's customer data.

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 vast 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 via SAS Enterprise Miner. A license will be made available to participants for use on that day (included with workshop registration).

Hardware: Training Computers Are Included
Each workshop participant will have access to a computer with SAS Enterprise Miner installed for the duration of the workshop.

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


Schedule

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

Instructor

Dean Abbott, President, Abbott Analytics

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 Clementine (SPSS), Affinium Model (Unica Corporation), Enterprise Miner (SAS), Model 1 (Group1 Software), and hands-on courses using Tibco Spotfire Miner (formerly Insightful Miner), and CART (Salford Systems).


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