This is a supremely ridiculous set of suggestions that has no merit whatsoever.
Companies aren't libraries. They aren't paying you to sit and read books. There is an assigned dayjob, a set of tasks you have on your jira that you have to resolve by your deadlines, and that occupies the 8 hour workday ID you are doing any justice to it. So any reading you do is on the side, on your own time.
Furthermore, people have these roles precisely because of their talents and their choices. As a Data Scientist, most of what I do is read ML literature, build ML models and write technical reports in Tex on what worked and what didn't. The skills to do this were acquired over many painful years of graduate work in math, statistics, ML. To suggest somebody can just read their way through that material is quite laudable, but you are underestimating the difficulty by orders of magnitude. Essentially, you are suggesting that all of the graduate study and mentoring and homeworks and assignments and all that went into the learning process be condensed into a book which one can just plow through and become a DS. Well, good luck with that.
By the same token, expecting me to have the same level of efficiency and passion as a data engineer when faced with a Hadoop/Oozie/Presto/Pig/kafka or what have you is silly. I don't care for these technologies and how to work them. I know it takes a really long time to get good at them - that's why the engineers get paid a lot of money and also get yelled at when the ETL job fails. Because it's a set of seriously valuable skills that were no doubt acquired over lots of time and practice. It's not like I can buy a book on these things, just read through them and suddenly I am a DE! I neither have the interest nor the time to do that.
>>the distinction between data scientists and data engineers is bogus
Not at all! Both DS and DE professionals do distinctly different work and conflating everything under 1 umbrella buys you nothing.
>> Companies aren't libraries. They aren't paying you to sit and read books.
That stroke you've brushed is too wide. Smart employers will have some of the money they're paying an employee going towards learning... and if they're really smart, they can even measure their ROI. Leads to less turnover, and better long-term vision for their projects.
I get your point, but give someone passionate enough 6 months in a new work environment, and with a decent mentor, and you might find they become surprisingly adept at it. The hard part is hiring for the capability to learn (fast).
Furthermore, people have these roles precisely because of their talents and their choices. As a Data Scientist, most of what I do is read ML literature, build ML models and write technical reports in Tex on what worked and what didn't. The skills to do this were acquired over many painful years of graduate work in math, statistics, ML. To suggest somebody can just read their way through that material is quite laudable, but you are underestimating the difficulty by orders of magnitude. Essentially, you are suggesting that all of the graduate study and mentoring and homeworks and assignments and all that went into the learning process be condensed into a book which one can just plow through and become a DS. Well, good luck with that. By the same token, expecting me to have the same level of efficiency and passion as a data engineer when faced with a Hadoop/Oozie/Presto/Pig/kafka or what have you is silly. I don't care for these technologies and how to work them. I know it takes a really long time to get good at them - that's why the engineers get paid a lot of money and also get yelled at when the ETL job fails. Because it's a set of seriously valuable skills that were no doubt acquired over lots of time and practice. It's not like I can buy a book on these things, just read through them and suddenly I am a DE! I neither have the interest nor the time to do that.
>>the distinction between data scientists and data engineers is bogus
Not at all! Both DS and DE professionals do distinctly different work and conflating everything under 1 umbrella buys you nothing.