“…The quality of our decisions in an industrial environment depends strongly on the quality of our analyses of data. Excel, a tool designed for simple financial analyses, is often used for data analysis simply because it’s the tool at hand, provided by corporate IT departments who are not trained in data science.
Unfortunately, Excel is a very poor tool for data analysis and its use results in incomplete and inaccurate analyses, which in turn result in incorrect or, at best, suboptimal business decisions. In a highly competitive, global business environment, using the right tools can make the difference between a business’ survival and failure. Alternatives to Excel exist that lead to clearer thinking and better decisions. The free software R is one of the best of these…”
Sourced through Scoop.it from: www.r-bloggers.com
Kudos to Thomas Hopper for writing this guide and for making the complete 87-page PDF file available for download. For over two decades, I limited the analyses offered to my consulting clients to what I could do with Excel, because it was the only tool they had, and I wanted to reproduce my results.
For the past three years, however, I have been teaching myself R and fully agree with Hopper that it is a much more powerful and reliable tool for analytics. I also agree that it takes time and effort to learn, but it is useful even at a beginner’s level of proficiency.
Many, including Hopper, refer to this gradual learning process as “steep learning curve,” which, strictly speaking, means the opposite: the steeper the learning curve of a skill, the faster you learn it…
The main challenge I see for the manufacturing engineers and managers I know is the switch from a spreadsheet to a coding mindset.
Excel is still preferable for expense reports or project cost justification, and R does not obviate the need for a database management system (DBMS).