Skip Main Navigation
Page Content
This event has ended

Introduction to Data Mining and Predictive Analytics


Tuesday, May 13, 2014 at 9:00 AM - Wednesday, May 14, 2014 at 5:00 PM (EDT)

Introduction to Data Mining and Predictive Analytics

Ticket Information

Use promotional code to access tickets.

Share Introduction to Data Mining and Predictive Analytics

Event Details

Introduction to Data Mining and Predictive Analytics with RapidMiner

Tuesday, May 13th and Wednesday, May 14th from 9 am to 5 pm

A light breakfast, lunch, and afternoon snacks will be provided.

Be sure to check out our Intermediate Data Mining and Predictive Analytics course being held in the same location on May 14 and 15. Register for both at the same time to get a special discount.

Class size for this event is limited to 10 students. If the class is sold out and you wish to be added to a waiting list, please contact the event organizer.

This course is a two-day introduction to the foundations of data mining, predicitve analytics, and RapidMiner software. After successfully completing this training, participants will have an understanding of how RapidMiner Studio and RapidMiner Server work and are used. They will be able to create predictive models in the standard data environments found within most analyst positions.

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 this course, participants will be able to:

  • perform basic data preparations
  • build initial predictive models
  • evaluate model quality
  • score new data sets


  • Target audience: newcomers, analysts, developers, administrators
  • Previous knowledge: basic knowledge of computer programs and mathematics
  • Methods: lectures, discussions, individual and group work, exercises on realistic data.

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.


  • Basic Usage
    • User Interface
    • Creating and handling RapidMiner repositories
    • Starting a new RapidMiner project
    • Operators and processes
    • Loading data from flat files
    • Loading data from databases
    • Storing data, processes and results
  • Data Preparation
    • Normalization and standardization
    • Basic transformations of value types
    • Handling missing values
    • Sampling
    • Filtering examples and attributes
    • Handling attribute roles
  • Predictive Models
    • Explorative data analysis including data visualization
    • Correlations
    • Linear Regression
    • Naïve Bayes
    • Decision Trees
    • Rules
    • Importance of attributes
  • Model Evaluation
    • Splitting data
    • Evaluation methods
    • Performance criteria
    • Lift charts
    • ROC plots
  • Scoring
    • Applying models             


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.


Sebastian Land

Sebastian Land blends his theoretical and practical knowledge of Advanced Analytics as the Head of Customer Technical Services for RapidMiner.  In this role, he combines his experience as one of the programmers of the software itself with his years working with clients to maximize their results using RapidMiner.


Training Facility Logistics

The training will be held in Camberley, Surrey, UK at the RapidMiner UK offices outside central London. For air, auto, and public transport directions, please click here and scroll to the UK Office. 


Classes require a minimum of 3 students by May 5 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.  RapidMiner will promptly notify registered users as soon as the 3 students quota will be met.  (Note: this class is near capacity and will definitely run.)


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


Our Refund Policy: Plans change? We get it. But if you can't make it to the class, please email us at no later than May 5.  No refunds will be given after this timeframe.

Have questions about Introduction to Data Mining and Predictive Analytics? Contact RapidMiner

When & Where

RapidMiner UK
Quatro House
Frimley Road
GU16 7ER Camberley, Surrey
United Kingdom

Tuesday, May 13, 2014 at 9:00 AM - Wednesday, May 14, 2014 at 5:00 PM (EDT)

  Add to my calendar



RapidMiner is the industry's easiest-to-use Modern Analytics platform that significantly accelerates productivity – from data prep to predictive action – with prebuilt models and one click deployments. Leveraging its open source heritage, RapidMiner was built by data scientists for data scientists, business analysts and developers. Unlike traditional analytics providers, RapidMiner enables anyone to make the most of all data in all environments, by providing a powerful code free advantage and the wisdom of over 250,000 users around the world.

RapidMiner offers training courses for business analytics, data mining, predictive analytics, predictive reporting, text and web mining, and related topics. Below are upcoming courses in the USA. We also offer courses in the UK and Germany.

  Contact the Organizer
Introduction to Data Mining and Predictive Analytics
Camberley, Surrey, United Kingdom Events Class Business

Please log in or sign up

In order to purchase these tickets in installments, you'll need an Eventbrite account. Log in or sign up for a free account to continue.