Type of course: classroom training, duration: 2 days
Basics 1 is a two-day training course that teaches the basics for understanding the new possibilities created by artificial intelligence and predictive modelling using the RapidMiner platform. First, an overview of data science and related areas is given, establishing a common basic vocabulary and bringing the broad variety of terms into some kind of order. Building on this, participants learn about the challenges that await them in a data science project and how to handle these efficiently by means of a practical example. For this purpose, a simple but realistic use case is looked at, which increases in complexity as the training goes on.
The course is structured to provide constant alternation between the study of theoretical basics and proven best practices and the practical application of knowledge acquired. Participants will form a data science team that completes the tasks set by the course instructor together.
The course lays the foundation for project activity in the area of data science. After the course, participants will have a thorough knowledge of the basic concepts of artificial intelligence and predictive modelling, allowing them to realize and manage projects. They will also be able to apply proven concepts using RapidMiner Studio, so as to independently perform simple data science tasks and gain their own experience. By attending this course you will have the prerequisite for attending the further training course Basics 2 - Profiling and Complex Models.
The skills acquired by participants of the training course include:
- Basic methods of data preparation
- Creating analytical predictive models
- Using analytical predictive models
- Evaluating models against different quality criteria
- Business scenario
- Analytics taxonomy & hierarchy
- Standards: CRISP DM & Collaborative CRISP
- Data science in companies
Getting Started with RapidMiner Studio
- User interface
- Creating and managing RapidMiner repositories
- Operators and processes
- Storing data, processes and results
Exploratory Data Analysis
- Loading data
- Producing quick statistics
- Visualizing data & creating charts
- Basics of data ETL (extract, transform, load)
- Data types & conversions of types
- Dealing with missing values
- Dealing with attribute roles
- Filtering rows and columns
- Normalizing and standardizing
Building Better Processes
- Organisation, renaming & relative paths
- Using sub-processes
- Building blocks
Predictive Modelling Algorithms
- k-nearest neighbor
- Naive Bayes
- Linear regression
- Decision trees and rules
Creating and Evaluating Models
- Machine learning theory: Bias, variance, overfitting & underfitting
- Split and cross validation
- Evaluation methods and quality criteria
- Optimisation and parameter tuning
- Deploying models
- Outlier detection
- Random forest
- Ensemble methods
Previous Knowledge Required
For Basics 1 you only need a basic understanding of mathematics and experience in dealing with computer programs.
Aspiring data scientists, engineers, experts and analysts
The training courses are specifically aimed at all those wishing to get involved with data science. They teach basic knowledge that is necessary to successfully realize and manage data science projects in the area of artificial intelligence. As such, they are directed at engineers, business, financial and technology analysts and other experts. The courses are also suitable for training new data scientists or experienced data scientists in order to ease their transition to the RapidMiner platform.
After you have attended the training courses Basics 1 & 2, you can take an exam to acquire the “RapidMiner Analyst” certificate and show off your new qualification.
Basics 1 € 1400 per participant (plus VAT)
Certificate free of charge for participants; the exam can be taken on a voluntary basis at the end of the Basics 2 training*
*A later examination can be taken online at any time for an additional € 200.
Courses can only be offered where there are at least two participants. We will inform you whether the course can go ahead as soon as possible following your registration.