San Francisco, California
London, United Kingdom
Intermediate Data Mining and Predictive Analytics with RapidMiner
Thursday, August 14th and Friday, August 15th from 8:30 am to 5 pm
A light breakfast, lunch and afternoon snacks will be provided.
Be sure to check out our Introduction to Data Mining and Predictive Analytics course being held in the same location on August 12 and 13. 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
- 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.
David Weisman, PhD
David Weisman is a data scientist consultant with over 35 years of experience in the software field. In addition to consulting, he is a researcher at the University of Massachusetts Boston, working at the intersection of molecular biology and data mining. David is searching for cancer biomarkers in enormous volumes of DNA sequence data, identifying biosensors of environmental pollutants in bacterial and plant transcriptomic data, and teaching bioinformatics courses. Prior to obtaining his recent Ph.D. in molecular biology, David ran a long-term successful software consulting firm, specializing in distributed system development, compiler design, operating system development, quantitative finance, network security, and health care informatics.
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 August 6 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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Our Refund Policy: Plans change? We get it. But if you can't make it to the class, please email us at firstname.lastname@example.org no later than August 6. No refunds will be given after this date.
When & Where
RapidMiner offers a variety of ways to learn and develop your skills with the RapidMiner product suite. Our training courses are the most efficient and effective way for data analysts, data scientists, and administrators to get started with RapidMiner. They are also the perfect preparation for our , which can qualify you as a Certified RapidMiner Analyst and Certified RapidMiner Expert.