Data has always been around us. Nowadays it becomes more and more accessible in a digital format. It contains valuable information that we may extract from it and use for the benefit of us, those around us, for businesses, non-profit organizations and governments.

Not using the advantages of data analysis in business means leaving profitable opportunities to the competitors. At the same time, when the amount of data exceeds a normal spreadsheet size in terms of rows, columns or both, it becomes increasingly difficult to make sense of data. Not knowing how to operate with large amounts of data as simply as with small tables, paralyzes the analysis.

In this course students learn a structured and practical approach to data analysis using R, a programming language for statistical computing (https://www.r-project.org/), and RStudio (https://www.rstudio.com). R is one of the most popular tools across employed data scientists (https://www.kaggle.com/surveys/2017). Unlike many other programming languages, R is designed specifically for data analysis and contains tools that were created to make each step of data analysis accessible to those without degrees in statistics and computer science. We will extensively use the “tidyverse” collection of R packages (https://www.tidyverse.org). Students don’t need prior experience in programming to learn how to use R.