Introduction to Data Mining and Predictive Analytics with RapidMiner
Tuesday, August 12th and Wednesday, August 13th from 8:30 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 August 14 and 15. 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 course is a two-day introduction to the foundations of data mining, predicitve analytics, and RapidMiner software. To support a business context for these topics, we will develop a specific business scenario as a through line during the course. The class follows a learn-do model, allowing students time to focus on the new material as it is explained, then apply that understanding in a lab exercize on their own.
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.
- Business Scenario
- Data Mining in the Enterprise
- Basic Usage
- User Interface
- Creating and handling RapidMiner repositories
- Starting a new RapidMiner project
- Operators and processes
- Loading data
- Storing data, processes, and results
- EDA: Exploratory Data Analysis
- Data Types
- Data Hierarchy
- Quick Summary Statistics
- Visualizing Data
- Data Preparation
- Normalization and standardization
- Basic transformations of value types
- Handling missing values
- Filtering examples and attributes
- Handling attribute roles
- Building Better Processes
- Relative Path
- Flow Control
- Building Blocks
- Predictive Models
- k-Nearest Neighbor
- Naïve Bayes
- Linear Regression
- Decision Trees
- Importance of attributes
- Model Evaluation
- Applying models
- Splitting data
- Evaluation methods
- Performance criteria
- Sharing and Collaboration
- Exporting images
- RapidMiner Server
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 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.
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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 email@example.com no later than August 4. 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.