You have experience with programming from your daily work with data or from your education and have a basic Mathematical understanding. Basic experience with programming in R will be helpful.
- Introduction to the RStudio IDE, the most popular IDE for R programming.
- Importing data from flat files and databases. Data comes from a variety of sources, it is therefore important to know how to get data into R.
- Manipulating data in R with a focus on the dplyr package. The dplyr package contains a grammar of data manipulation, this will help you solve the most common data manipulation challenges.
- Plotting data in R with a focus on the ggplot2 package. The ggplot2 package contain similar grammar for plotting in R. To be able to plot is essential when new knowledge about data is to be shared with colleagues.
- Program a prediction model in R, also called supervised learning. Learn how to implement models in R through exercises, with a focus on the most popular Machine Learning models.
- Clustering Basics in R, also called unsupervised learning. Learn how to group similar observations together.
- Interactive reporting of results to colleagues and non-technicals with R Markdown. New knowledge about data is not worth much if you aren't able to communicate the findings with your colleagues. R Markdown is a strong tool for reporting.
- And more....
Troels is a Data Scientist, where he uses SQL, Python, Scala, Spark and R for Big Data analysis. He has a masters in Mathematical Finance from Aarhus University where he studied and taught for several years in statistical and mathematical courses. He is very interested in Data Science both from a theoretical and practical perspective.
Do you have any course related questions, please contact
- Charlotte Heimann
- +45 72203147