RapidMiner Basics Part 1
Two-day class from 9:00 a.m. to 5 p.m.
Lunch and afternoon snacks will be provided.
Be sure to check out our RapidMiner Basics Part 2 course being held in the same location on directly following this class.
RapidMiner Basics Part 1 is a two day course focusing on data mining and predictive analytics with RapidMiner Studio. Over the course of two days students will explore a clean, simplified business use case and build a strong analytical model while becoming familiar with the graphical interface and all of the products features and functionality.
The course is structured in a mentoring fashion where the entire group performs as members of a data science team. After successfully completing this course, participants will have a solid understanding of how RapidMiner Studio functions. Participants will be able to prepare data, create and validate predictive models, and will be ready to extend their knowledge to advanced topics such as RapidMiner Basics Part 2 and RapidMiner Server: Web Apps and Deployment.
Practical exercises during the course prepare students to take the knowledge gained and apply to their own respective data mining problems, solving them quickly and easily. Since the class labs are hands-on and performed on the participants’ personal laptops, students will take actual classwork home with them, which will provide a jumpstart to the real world.
Analysts, Developers, and Administrators
Basic knowledge of computer programs and mathematics
After the training, students will have the ability to:
- Perform all common data preparations
- Build strong analytical predictive models
- Evaluate model quality with respect to different criteria
- Share analytical models and collaborate with team members
- Business scenario
- Data mining in the Enterprise
- Getting Started with RapidMiner Studio
- User Interface
- Creating and Managing RapidMiner Repositories
- Starting a new RapidMiner Project
- Operators and Processes
- Loading Data
- Storing data, processes, and Result Sets
- 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
What to Bring
You must bring a laptop to class (Windows, Mac or Linux OS) with the latest RapidMiner Studio installed.
System requirements: http://docs.rapidminer.com/studio/installation/system-requirements.html
Please email us at email@example.com no later than 10 days prior to the event to notify us that you cannot attend. No refunds will be given after this date.
NOTE: Classes require a minimum of 3 students seven days before the commencement of the course. If there are insufficient registrants, the class will be cancelled and all students will be refunded the full registration fee. Students should organize their travel arrangements accordingly and with this proviso.
Zeit und Ort
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 certification exams which can qualify you as a Certified RapidMiner Analyst and Certified RapidMiner Expert.