RapidMiner Basics Pt. 2
Thursday, February 12, 2015 at 8:30 AM - Friday, February 13, 2015 at 5:00 PM (EST)
San Francisco, California
London, United Kingdom
RapidMiner Basics Pt. 2
Thursday, February 12th and Friday, February 13th from 8:30 am to 5 pm
A light breakfast, lunch and afternoon snacks will be provided.
Be sure to check out our RapidMiner Basics Pt. 1 course being held in the same location on February 10 and 11. Register on the same day for each 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 Basics Pt. 1 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
- Target audience: users, analysts, developers, administrators
- Previous knowledge: Introduction to Data Mining and Predictive Analytics or equivalent
- 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.
- Business case changes
- Intro course recap
- Loading new data
- Multiple sources
- Understanding new attributes
- Schema relationships
- Data Preparation
- Multi-level Aggregation
- Set Theory
- Calculated values
- Regular Expressions
- Changing value types
- Balancing data
- Outlier detection
- Feature selection
- Dimensionality reduction
- Predictive Models (sample varies)
- 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
- Sharing data, models, and processes
- Exporting processes as web service
- Basics of report creation
- Managing processes and services
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: Thomas Ott
Training Facility Logistics
The training will be held in Cambridge, MA at the RapidMiner Headquarters training center. The facility is near the Alewife T (metro) stop and other public transportation. Map and directions.
There are a variety of lodging options locally and in other parts of Cambridge and Boston that are accessible to the facility either on foot, bike, or by public transportation.
The closest reliable public parking is at the Alewife T station. From Alewife to RapidMiner HQ, there is a free public shuttle, but it is only a short walk away (perhaps ten minutes).
Classes require a minimum of 3 students by February 4 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.
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.
Our Refund Policy: Plans change? We get it. But if you can't make it to the class, please email us at email@example.com no later than February 4. No refunds will be given after this date.
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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.