Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed
Sebastian Raschka weighs in on how to battle stress as a beginner in the data science world. His insight is to-the-point, so reading it should be a stress-free endeavour.
I think that having a so much great resources available can sometimes be both a blessing and a curse. It's great that we have so many tools and sources of information to choose from, but to make best use of it -- and our time -- it is really important to actually "choose" and keep "focused."
I don't want to say that many resources are "redundant," since "redundant" has a somewhat negative ring to it. However, there are many different books, tools, and courses that cover essentially the same thing, although, the scope and style may be a bit different.
So, instead of adding everything that we stumble upon to our reading lists, I'd say that it makes more sense to be absolutely clear about personal goals first ("What skills do I need to learn to solve problem X?," "Do I really to learn this new, shiny tool X instead of Y?"). Since there's so much material out there, it's become necessary to be a bit more selective when choosing learning material and exploring different tools. Of course, it sometimes feels like we are missing out on something, but I think that getting used to this feeling really helps to stay focused and to make steady progress.
For example, I think that one "introduction to machine learning" book should really be enough, there's no point in reading multiple "introductions to ML," unless you really feel like that the particular resource was not comprehensible or sufficient.
As Cathy O'Neil & Rachel Schutt explained, there is no "perfect" data scientist -- there's just not enough time to learn everything, rather, everyone develops a certain skill set and is better in a certain areas:
I think that not knowing everything is not necessarily a bad thing. Since (as depicted in the figure above), we can compensate each other by working in a team.
Original. Reposted with permission.
Related:
- Top Data Scientist Claudia Perlich on Biggest Issues in Data Science
- What is the Difference Between Deep Learning and “Regular” Machine Learning?
- Top Data Scientist Daniel Tunkelang on Data Science Project Scope… and Reducing It
