Applied Data Science with RTeknologisk Institut
Learn how to solve many commonly faced problems when applying R for Data Science and become an even more important part of your company. This course will introduce you to the most important Data Science tools in R to effectively solve many real life problems.
Data Science is an exciting field and the demand for people with skills herein only seems to increase as companies tend their focus to digitalization and data exploration. The goal of this three day course is to step through some basic tools for tackling a wide variety of Data Science challenges.
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.
- With more.
Troels Lægsgaard 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.
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