Data is the motor of our world, and you can define the direction. We’ll provide you with the right tools
Your data analysis should achieve results with direction and sense. Part of that are conscientious processing, sound choices of methods and reproducibility of all single working steps. To make an impact, results must be validated and visualised, for example by regular reports or web applications.
Learn in our academy how to create and communicate reliable analyses.
Basic programming with R
4 – 12 participants | 2 Days
Start learning R as a programming language. In this course we’ll teach you the basic concepts and techniques of this modern, functional and object-oriented language.
Dealing with numbers
- R as a scientific calculator
- Understanding objects
- Exotic numeric values: NA, NaN, Inf
- Understanding vectors
- Working with vector functions
Logic & text
- Working with logical vectors
- Understanding and applying logical indexing
- Formating numbers
- Handling factors
- Formating and analysing texts
- Understanding and applying date concepts
- Creating and applying time series
- Working with matrizes
- Handling arrays
- Working with data frames
- reading and writing objects
- Handling text and excel files
- Understanding and applying lists
- Writing functions
- Using arguments correctly
- Designing output objects
- Understanding Methods
- Simple branching with if & switch
- Applying branches on vectors
- Programming loops
- Avoiding loops: Apply & Co.
Professional programming in R
4 – 12 participants | 2 days
Advance your R skills. In this course we’ll teach you substancial functions for your daily work.
- Functions, scoping
- Understanding assign and replace
- Functional programming
- Function operators
- Lambda calculus and Currying with R
- Priciples of object orientation
- Capsuled vs. functional object orientation
- Overview over possible paradigms in R
- Working with S3 and S4 classes
- Working with RC and R6 classes
Performance and memory
- Runtime optimization
- Memory usage
- Memory optimization
R Packages and shiny apps
- Why should you use packages?
- The strukture of R packages
- Phases of R packages
- Documentation, testing
- Understanding the architecture of shiny apps
- Developing a shiny app
Descriptive data analysis with R
4 – 12 paticipants | 2 days
In this course we’ll teach you how to use R as a tool for interactive data analysis.
Create and import data
- Learning about types of attributes
- Unterstanding the data structure in R
- Inserting data manually and querying attributes
- Importing data of different formats
- Using webscraping
- Connecting data banks
- Learning about the graphic systems in R
- Working with base functions
- Getting a graphic ready for publication
- Bringing your data to life: The package lattice
- Speaking the language of your images: The package ggplot2
- Editing attributes: Changing the type, creating new attributes, recognizing missing values
- Editing contents: Applying mathematical functions and text operations
- Sort, choose and bind: The packages data.table and sqldf
- Transform and compress data sets: The package reshape2 and the apply-family
- The gate to big data: dplyr, magrittr, xdf, dplyXdf & co.
Analyzing one-dimensional attributes
- Understanding frequency distributions
- Median & co: Understanding and applying measures of dispersion
- Varianz & co: Streuungsmaße verstehen und anwenden
- Verteilungen charakterisieren: Form und Boxplots
- Paretoprinzip oder nicht? Konzentrationsmaße
- Mit fehlenden Daten umgehen
Analyzing two or more characteristics
- Visualizing the joint distribution
- The relationship between nominal characteristics
- The relationship between ordinal characteristics
- The relationship between continous characteristics
- Visualizing and analysing several variables
- Handling missing data
Analysing time series and indices
- Visualising time series
- Decompose and aggregate time series
- Harazd rate & co: Analyzing events
- Creating and using indices
- Handling missing data
Karl-Kuno Kunze earned diplomas in physics and econophysics, a DEA de Physique des Liquides at Paris University and a MSc in Mathematical Finance at Oxford University.
He did his doctorate first in theoretical physics, then in economic science.
After more than fifteen years of practical experience in applying quanititative models in the financial industry, today he manages daqana and teaches as professor of business mathematics at Ostfalia University in Wolfsburg.
Mirjam Rehr earned her diploma in statistics at LMU Munich and afterwards worked as scientific assistant at WWU Münster, Institute for Geoinformatics, as well as UKM/WWU Münster, Institute of Medical Informatics.
For the Joint Research Center of the EU (Ispra) she was part of the conception and the management of the course: “Statistical modelling in R”.
Ralf Stubner earned a diploma in physics and afterwards did his doctorate in theoretical physics.
He has over ten years of experience in engineering and developing data driven systems as well as using data for quantitative models in the financial industry.
Furthermore he conceived and managed several technical trainings.