Nina and I have been sending out drafts of our book Practical Data Science with R 2nd Edition for technical review. A few of the reviews came back from reviewers that described themselves with variations of:
Senior Business Analyst for COMPANYNAME. I have been involved in presenting graphs of data for many years.
To us this reads as somebody with deep experience, confidence, and bit of humility. They do something technical and valuable, but because they understand it they do not consider it to be arcane magic.
In this note we describe might can happen if such a person (or if a junior version of such a person) acquires 1 or 2 technical books.
I don’t mean fancy graduate texts, but the black and white paperbacks that one used to find shelved by the yard in Borders or Barnes and Noble. Such a person can quickly acquire the ability to do a lot more.
For instance suppose they pick up the following (which we recommend!):
This book, Beyond Spreadsheets with R, teaches how to use the open source software called
R to transform, tabulate, and graph data. This person then learns how to program by performing example tasks they are already expert in.
Reviews of Beyond Spreadsheets with R show how useful this book can be to the right reader.
An excellent book to help you understand how stored data can be used.
A great introduction to a data science programming language. Makes you want to learn more!
This is an incredible way to learn. Even if one stops here, they will have learned how to write programs that help with their job. This opens up so many opportunities.
These potential readers don’t have to quit their job, take out loans for a graduate degree, slog through videos, join a data science boot camp, or switch jobs. If they are already doing a job that involves data and graphs (and many people are!) they can quickly see if
R (which is free!) can help with their work. If this does not work for them they are out only the cost of one reasonably priced book ($49.99 is a substantial amount of money, but it is about 1/3rd of the cost of a typical academic text). And they don’t have to read the whole book to try things out, they should be able to tell if it is working for them by working through the examples as they go.
If the above works well for them they can perhaps use the time
R is saving them on their tasks to look into our book: Practical Data Science with R 2nd Edition.
In the words of one of the mentioned Practical Data Science with R reviewers:
I think the approach is great, as it is largely just going through the process of how to do data science, from requirements gathering to deployment, and there are various nuggets to help people coming from other languages to understand how they should use R.
If all of this works out, and the reader wishes to see some cutting edge technology, I would then suggest they pick up Deep Learning with R. This will give them a hands-on taste of one of the important research directions in data science.
It may not be obvious to a new reader what “deep learning” is, but this book uses free deep learning software to work with additional types of data such as text and images.
We started with Beyond Spreadsheets with R as an introduction to
R appropriate for those already working with data in spreadsheets. An introduction to
R that will appeal to those already using other statistical software (such as SAS) is R in Action, 2nd Edition.
In all cases, start with one book at a level you are familiar with.