Statistical Methods for Data Analysis | Research Techniques & Applications
“The sexy job in the next 10 years will be statisticians!” – Hal Varian, chief economist at Google, could not have been more correct when he said this sentence in the early 2000s.
The knowledge about statistical methods for the analysis of large data sets is becoming more and more important for a modern curriculum vitae.
On statistical-programming.com, you can learn how to use the techniques that are currently up to date in the research fields of statistics and data science – and even more important – how to apply these methods with modern statistical software such as R or Python.
List of Statistics Tutorials
In the following, you can find a list of statistics and data science tutorials that I have published on statistical-programming.com. At the moment, the tutorials are mainly covering the handling of missing data and related topics. However, in the near future I will add further topics to the list. In the tutorials, I am explaining the theoretical concepts and show some practical applications for the different methods.
The Most Important Methods in Statistics & Data Science
Admittedly, the list of available statistical methods is huge. As a beginner, it therefore makes sense to learn some of the most important techniques first and the move on from there.
If you want to get a first overview about some of the most important statistical concepts, I can recommend the following video tutorial of the YouTube channel The Doctoral Journey. The speaker, Dr. Amanda J. Rockinson-Szapkiw, is explaining some basic descriptive and inferential methods.