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Intermediate Data Mining and Predictive Analytics

RapidMiner

Thursday, September 18, 2014 at 8:30 AM - Friday, September 19, 2014 at 5:00 PM (EDT)

Washington, DC

Intermediate Data Mining and Predictive Analytics

Ticket Information

Ticket Type Sales End Price Fee Quantity
Early Bird – SAVE $300!
Early Bird pricing! Save $300 by registering in advance prior to 10 days before the class starts.
Ended $1,525.00 $0.00
DUO - 2-course pricing (must purchase each course at this rate)
Buy both of these consecutive courses at the same time and save $800! Note: You must purchase the DUO ticket for each class on the same day for this rate to be honored.
Ended $1,425.00 $0.00
Regular
Regular price ticket for training course.
Ended $1,825.00 $0.00
Group of 2 - SAVE $200 each
SAVE $200 each! Receive a discount if buying 2 regular tickets at the same time. Pricing is per ticket.
Ended $1,625.00 $0.00
Group of 3 - SAVE $325 each
SAVE $325 each! Receive a discount if buying 3 regular tickets at the same time. Pricing is per ticket.
Ended $1,500.00 $0.00
Group of 4 - SAVE $500 each
SAVE $500 each! Receive a discount if buying 4 or more regular tickets at the same time. Pricing is per ticket.
Ended $1,325.00 $0.00

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Event Details

Intermediate Data Mining and Predictive Analytics with RapidMiner

Thursday, September 18th and Friday, September 19th from 8:30 am to 5 pm

Light snacks in the morning and afternoon will be provided, as well as lunch.  

Be sure to check out our Introduction to Data Mining and Predictive Analytics course being held in the same location on September 16 and 17.  Register for both at the same time to get a special discount.

Class size for this event is limited to 12 students.  If the class is sold out and you wish to be added to a waiting list, please contact the event organizer.

This training is a second two-day course, exploring additional possibilities of performing data mining and predictive analytics with RapidMiner Studio and RapidMiner Server.  Where the Introductory course takes a clean, simplified business example to build a strong foundation, this Intermediate course explores a similar business case with some of the messiness of the real world added in.

With knowledge of the Intro class assumed as a prerequisite, this class changes from a teacher-student classroom format to a mentor-mentee relationship with the entire group performing as members of a data science team.

After successfully completing this course, participants will have an increased understanding of how RapidMiner software works and is used. The participants will be able to prepare data and create predictive models in standard data environments typically found within most analyst positions, as well as in many more uncommon environments.  

Practical exercises during class prepare the participants to transfer the knowledge gained and apply it to their own data mining problems, solving them more quickly and easily.  Since the class labs are hands-on, performed on the students' own laptops, the students will be taking their actual classwork home with them to jumpstart their application to the real world.

After the training, students will have the ability to:

  • perform the most necessary and common data preparations
  • build sophisticated predictive models
  • evaluate model quality with respect to different criteria
  • deploy data mining models


Details


Participants may introduce their own work and project specific questions in order to find particular solutions together with the trainer and other participants.  The training course addresses beginners and intermediate learners.

 

Topics

  • Overview
    • Business case changes
    • Intro course recap
    • Loading new data
  • EDA
    • Multiple sources
    • Understanding new attributes
    • Schema relationships
  • Data Preparation
    • Joins
    • Aggregation
    • Multi-level Aggregation
    • Pivot
    • Set Theory
    • Calculated values
    • Regular Expressions
    • Changing value types
    • Balancing data
    • Outlier detection
    • Feature selection
    • Dimensionality reduction
  • Predictive Models (sample varies)
    • SVM
    • Random Forest
    • k-Means Clustering
    • Neural Networks
    • Logistic Regression
    • Meta Learning
  • Model Evaluation
    • Advanced performance criteria
    • ROC plots
    • Comparison between models
    • Lift Chart
    • Significance tests
    • Validation of preprocessing and preprocessing models
    • Logging results
  • Deployment
    • Sharing data, models, and processes
    • Exporting processes as web service
    • Basics of report creation
    • Managing processes and services


Prerequisites

You must bring a laptop to class (Windows, Mac or Linux OS).  For Windows, Java Runtime Environment (JRE) version 7 is required.  For Mac and Linux, Java Development Kit (JDK) version 7 is needed.  Students will be provided with links to install RapidMiner Studio 6 prior to the class.

 

Instructor

Todd Cioffi

Todd is the Director of RapidMiner University at RapidMiner, a leader in Predictive Analytics providing an easy-to-use desktop-to-cloud solution designed for data scientists and business leaders. As a strong advocate for training and certification he combines his experience in technology and education to impart real-world use cases to students and users of analytics solutions across multiple industries.

For more than 20 years, Todd has been highly respected as both a technologist and a trainer. As a tech, he has seen that world from many perspectives: “data guy” and developer; architect, analyst, and consultant. As a trainer, he has designed and covered subject matter from operating systems to end-user applications, with an emphasis on data and programming. He is a regular contributor to the community of analytics and technology user groups in the Boston area, writes and teaches on many topics, and looks forward to the next time he can strap on a dive mask and get wet.


Training Facility Logistics

The training will be held at the MicroTek - Washington, D.C. training facility.

There is public parking next to the building.  Map and directions.  

Public transportation options cane be found here.

For lodging options, MicroTek has arranged rates at a number of nearby hotels.

For local contact information, contact the facility at 202-289-3811

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Classes require a minimum of 3 students by September 2 to be held.  If there are insufficient registrants, the class may be cancelled and all students will be refunded the full registration fee. Students should organize their travel arrangements accordingly and with this proviso.

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Can't make it? Sign up for our newletter to stay in the loop on future events and classes by clicking on the Subscribe button at the top of any page on www.rapidminer.com.

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Our Refund Policy: Plans change? We get it. But if you can't make it to the class, please email us at training@rapidminer.com no later than September 2.  No refunds will be given after this date.

Have questions about Intermediate Data Mining and Predictive Analytics? Contact RapidMiner

When & Where


MicroTek - Washington, DC
1110 Vermont Ave NW
Suite 700
Washington, DC 20005

Thursday, September 18, 2014 at 8:30 AM - Friday, September 19, 2014 at 5:00 PM (EDT)


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Organizer

RapidMiner

Pioneering advanced analytics vendor RapidMiner is redefining how business analysts use data to predict the future. With an open source heritage, RapidMiner is one of today’s most widely known and used predictive analytics platforms, providing powerful solutions for a wide variety of industries.

RapidMiner focuses on the fields of predictive analytics, data mining, and text mining. The discovery and leverage of unused business intelligence from existing data enables better informed decisions and allows for process optimization.

RapidMiner serves customers globally from offices in the United States, Germany, Hungary and the United Kingdom. Furthermore, our network of partners can support your data analysis projects using RapidMiner software products.

 

  Contact the Organizer

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