Every Data Scientist must only pay taxes and die, rest is just optional...

Picture from cacm.acm.org

I am just wondering. There are many articles about what every Data Scientist MUST know and do to be real Data Scientist. There are many articles about MUST not do as Data Scientist. What real Data Scientist MUST read and so on and so on. And honestly I don't care. Why is that so?

Research

Let's take the last one must: "What Data Scientist must read" list. I just briefly took several results from Google, here is the list of 10 of them:

• https://www.quora.com/What-is-a-list-of-books-that-every-data-scientist-should-read
• http://bigdata-madesimple.com/20-must-read-books-for-budding-data-scientists
• http://www.analyticsvidhya.com/blog/2015/06/infographic-read-books-analytics-data-science
• http://www.datasciencecentral.com/profiles/blogs/15-books-every-data-scientist-should-read
• http://www.mastersindatascience.org/blog/7-must-read-books-for-the-budding-data-scientist
• http://www.forbes.com/sites/gilpress/2015/03/31/3-recent-books-for-budding-data-scientists-and-business-executives-involved-with-big-data-analytics
• https://www.umbel.com/blog/big-data/must-read-big-data-books
• http://analyticstraining.com/2014/five-must-reads-for-data-scientists
• https://www.quora.com/What-are-the-must-reads-for-data-science-and-machine-learning
• http://www.analyticsvidhya.com/blog/2015/06/infographic-read-books-analytics-data-science

And what is this quick research good for? From this circa 80 books and list of several articles you get list of subjective chosen resources which lead practically to nowhere. Just several books repeating like famous Nate Silvers Signal and Noise and of course some R or other Cookbooks. So, what is conclusion?

Conclusion

This lists of books which someone else read leads me always to Vincent Granville's article Fake data science. And what you need to take from it? Pick any book you need for your field of expertise. And what should be your field of expertise? Choose some project, doesn't matter if your personal one, school or for instance from Kaggle.com. And follow up approaches which you need to for goal achievement, then pick book, course to support your path towards this goal. And by real work, life experience you will sooner or later become Data Scientist.

Too much, too little

Interesting, I was waiting when it happens. It happen, after 3 months of intensive study. Combination of daily routine work, care about family and study, brought me to the intensive and continuous tired mood. I just realized that study of the Data Science field finishes to be fun and starts to be obstacle.

I know what I am doing wrong and I need to get rid of it. First at all it is study of many different MOOC courses in parallel and taking new one into account which leads to two things. I am overloaded and I am not able to finalize anything in term and/or in quality. So I need to strictly reduce it in maximum two in parallel, sometimes only one, when it is too difficult (who should know, Data Analysis and Statistical Inference gave me a lesson). And starts to enjoy it again. And also sleep over night, at least sometimes ;-).

At this time I would like to finalize all which I have in the middle, but don't go for new ones till my queue is not empty or with just one running course. I will maybe keep one and in parallel will return to read some book.

Anyway, it doesn't mean, I am stop writing these notes to my blog or even close it, not at all. I just need to slow down and keep my track which I have planned. It's interesting how little is enough to realizes that it is too much :-).