Academy | Syllabus
Data Science with RapidMiner
RapidMiner is a popular choice for most data scientists, owing to its ease of use and versatile nature. The Data Science with RapidMiner training program offered by Multimatics is designed to help participants understand how RapidMiner can be used for various data-science applications. Participants will also learn how to create reproducible data processing pipelines, visualizations, and prediction models. The training material is prepared based on the latest version of RapidMiner, accompanied by discussions and exercises to work on the questions.
Trusted by OrganizationsProven by Professionals
+21
Years of Experiences
+6.000
Delivered Training Programs
+50.000
Professionals
90%
Exam Pass Rate
Program Details
Durations
The program is a 5-day intensive training class.
Method of Delivery
The program provided by Multimatics will be delivered through interactive presentation by professional instructor(s), group debriefs, individual and team exercises, behavior modelling and roleplays, one-to-one and group discussion, case studies, and projects.
Who Should Attend?
This program is designed for Database Administrator, Data Analyst, Business Analyst, Researcher, Programmer, and everyone who is interested in learning about data.
Program Objectives
By the end of the program, participants will be able to:
- Understand how RapidMiner can be used for various data-science applications
- Create reproducible data analyses using RapidMiner
- Perform data exploration using RapidMiner
- Understand and implement data modelling concepts
- Understand and implement prediction analytics
- Understand key concepts and roles in data science
- Understand and implement the stages of the data science process
- Solve data science case studies according to the standard cross industry processes
Examination Details
Duration
5 Days
Format
- Open BookClose Book
Level
-
Passing Grade
65%
Program Modules
INTRODUCTION DATA SCIENCE
Topic Covered
- What and Why Data Science
- Main Role and Data Science Method
- History and Data Science Implementation