Training: Data science with R

The open source language R is one of the best solutions for data analysis, data visualization, data mining and predictive analytics. With R, a unique standard of functionality, quality and actuality can be achieved.

The course is intended as an introduction to R and its basic functionalities. It makes it easier for participants to get started with R by getting practical tips and exercises. This basic course serves R beginners without previous knowledge as a starting point in order to use R in further individual application scenarios.

The objective of the course is to teach the participants the logic and terminology of R and to lay the foundation for working with R independently.

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

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Recommended course length: Two days

Use machine learning and data mining algorithms to develop artificial intelligence applications based on your data.

In our course „Introduction to machine learning with R“, we give you an insight into machine learning algorithms and show you how to develop your own models, what challenges you might face and how to master them.

Based on practical examples and exercises, we teach you the skills to implement machine learning methods in R independently. The preparation of data, the development and training of algorithms and the validation of analysis models: In our course you will get to know the central steps of machine learning.

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

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Recommended course length: Two days

The basis of every data analysis is a good data management, where a large part of the effort for the analysis process is spend on the preparation of the raw data.

Based on practical examples, the “Data Management with R” course imparts efficient methods for the preparation of differently structured data.

The focus of this course lies on the handling of the packages dpylr, tidyr and data.table.

Table of contents:

• data.table: Memory-efficient editing and reading of large data sets
• dplyr: Simple and performant syntax for manipulating data frames
• tidyr: Transformation of data sets – from long to wide table and vice versa
• Dealing with special data types: Editing date and string variables

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Recommended course length: One day

The goal of a time series analysis is to identify structures and patterns in time series data, to describe these patterns and to derive forecasts from this knowledge. The questions arise from different disciplines such as econometrics, finance or meteorology and are related to the development of foreign exchange, stocks, sales or weather.

A further goal is to ensure the safe use of terminology and methods by introducing the participants to the field of time series analysis theoretically. Afterwards, the participants will not only learn about statistical test procedures for the characterization of time series but will also get to know the central smoothing and forecasting procedures. Based on exemplary data sets from economics (e.g. stock and currency data) the methods are applied in R and deepened through small exercises. At the end of the training, the participants should be able to conduct time series analyses with R for their own projects.

Table of contents:

• Introduction to the basic concepts of time series analysis
• Time series objects in R
• Characterization of time series
• Exponential smoothing of time series
• ARIMA models

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Recommended course length: One day

In this course, you will learn analysis techniques that allow you to uncover statistical relationships and patterns in your data. It focuses on three classical methods of multivariate statistics which are regression, cluster and factor analysis.

Linear regression analysis allows you the modelling of relationships and the influencing of various factors on a specific target value. What influence does the weather have on my sales numbers? Which distribution channels are most successful?

A cluster analysis can reveal hidden similarities between observations. The goal is to identify groups within the sample that are homogeneous in themselves and at the same time can be easily distinguished from the other groups. A classic application in this field is a customer segmentation.

Factor analysis is always interesting when information from different measured values must be condensed. This form of statistical information compression is used in various areas of application: in psychological and socio-scientific analyses for measuring abstract constructs such as „civil courage“ and in the technical area for image or signal processing.

“Multivariate statistics with R” is regarded as an application-oriented introduction to the three methods mentioned above. Their focus lies in the application in R. Furthermore, this course is designed to provide a comprehensive introduction to the three methods mentioned above. It is aimed at interested parties who already have a basic knowledge of R and statistics.

Table of contents:

• Cluster analysis
• • Basic concepts of cluster analysis
  • Similarity and distance measures
  • Comparison and application of different algorithms
• Regression analysis
• • Introduction to linear regression analysis
  • Model coefficients, significance tests, model quality
  • Graphical and statistical verification of model prerequisites
  • Consideration of non-linear effects and interaction effects
  • Automated modeling using Stepwise Regression
• Factor and principal component analysis (PCA)
• • Introduction to procedures
  • Process of factor analysis
  • Data inspection, determination of the number of factors, rotation
  • Factor loadings, communalities and reproduced variance

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Recommended course length: Two days

The statistical programming language R is perfectly suited for the visualization of data. By using application-oriented examples, you will learn about the standard graphic system of R and its underlying concepts. Moreover, the course deals with the graphic package ggplot2, which is a popular alternative to the standard graphic system. With ggplot2, even complex graphics can be implemented quickly and easily.

In addition to the basic graphics functions, the course focuses on various adjustments that can be used to influence the appearance of a graphic. The goal is to enable you to visualize data and to adapt them to your individual requirements.

Table of contents:

• Base graphic system
• • Simple one- and two-dimensional graphics
  • Adjustment of graphics with individual elements
  • Adjustment of appearance
  • Export of graphics
• ggplot2
• • Introduction to the grammar of graphics
  • Basics of ggplot2
  • Different types of graphics with ggplot2
  • Adjustment of ggplot2 graphics
  • Complex graphics

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Recommended course length: One day