You want to use the R programming language to transform data into strategic knowledge? You are looking for the optimal introduction to working with R? You would like to participate in a training that really helps you despite your home office and limited travel possibilities?
Then register for our online R trainings. In our two most popular courses „Introduction to R“ and „Machine learning with R“ we provide you with the knowledge you need for the productive use of R. How does our offer differ from other online trainings? The presence of our experienced data science trainers. Your individual questions will be answered in direct exchange – this ensures the greatest possible learning success for you.
Location: In the home office, office or from the balcony: Our webinars offer you the flexibility you need in the current situation.
Price: Per course: € 299,- | In a bundle: € 499,-
Course language: German
Introduction to R | Courses in Jun, Jul, Aug & Sep 2020 | 2 days | 09:00 am to 13:00 pm
The course is intended as an introduction to R and its basic functionalities and facilitates your entry into R with practical tips and exercises. This basic course serves as a starting point for R beginners without in-depth previous knowledge for the further use of R in individual application scenarios.
Table of contents:
- First steps into R
- Concept and philosophy of R
- Data structures and their properties
- Importing data
- Data management
- Data analysis with R
- For loops and control elements
- Visualizations with R
Machine learning with R | Courses in Jul, Aug & Sep 2020 | 2 days| 09:00 am to 13:00 pm
In our course „Machine learning with R“ we give you an insight into algorithms of machine learning and show you how to develop your own models, which challenges you face and how to master them.
Table of contents:
- Introduction to the basic terms of machine learning
- Introduction to machine learning algorithms such as decision trees, random forest, gradient boosting machine
- Introduction to a methodical approach in the development of machine learning models
- Typical steps in data preparation such as feature selection or data transformation
- Creation of training and test data sets
- Introduction of validation techniques such as cross-validation or bootstrapping
- Introduction and interpretation of different metrics for measuring success such as:
- For classifications: Accuracy, sensitivity, specificity
- For regression: RMSE, MAE, MAPE, …
- Interpretation of ROC curves
- Tuning of parameters
- Introduction of the data mining framework caret