In applied econometric research and business analytics alike, many steps in handling digital data are involved before running regression estimations and training machine learning algorithms. While often neglected in statistics and econometrics curricula, the steps of gathering, cleaning, storing, and filtering data for research/analytics purposes are of utmost importance to ensure accurate and reproducible analytic insights. This pocket reference first introduces and summarizes foundational and practically relevant data and data processing concepts, and then guides the reader through each step of how to get from the raw data to the final data analysis output.
The book presupposes a basic knowledge of undergraduate economics and statistics and relates several case studies to practical questions in these fields. Finally, the aim is to give you firsthand practical insights into each part of the data science pipeline in the context of business and economics research.
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` and `+`) to R source code in this book, and we comment out the text output with two hashes `##` by default, as you can see from the R session information above. This is for your convenience when you want to copy and run the code (the text output will be ignored since it is commented out). Package names are in bold text (e.g., **rmarkdown**), and inline code and filenames are formatted in a typewriter font (e.g., `knitr::knit('foo.Rmd')`). Function names are followed by parentheses (e.g., `bookdown::render_book()`). The double-colon operator `::` means accessing an object from a package. -->
Ulrich Matter St. Gallen, Switzerland
