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5 Ways Data Scientists Keep Learning After College

Taken from the answers experts gave, here is a compiled list of 5 essential actions and attitudes that keep data scientists learning after their degrees.

4. Take online courses

Google “learn data science” and you’ll see just how popular online courses in data science are.
Prospective data scientists, or those just looking to stay ahead, are able to learn at their own pace, on their own time, and are able to focus on exactly what they want to understand.

Dolatshahi explained the influx of online courses available:

The classes offered now are extremely valuable because you don’t need to have a masters or any qualification if you’re interested in learning the material. The nice thing about taking a class is that it gives you a supportive learning environment with instructors, TAs, opportunities to ask questions, and a community of fellow students.

Hyndman also advocated the use of these programs:

Coursera has a fantastic data science program. I think there are four separate Coursera courses that run out of Johns Hopkins which are really good, which I recommend regardless of what your background is.

Interested? There are over 150 data science programs you can take on Coursera right now.

5. Keep reading books, blogs, and articles

It’s hard to overstate the value of a good book. The same can be said of a well-constructed article written by a thought leader. Dolatshahi spoke about how important reading continues to be for his own education, “The most important parts of my experience have been reading articles and books, experimenting with technology, and talking to people.”

Bartlett also spoke about how important it was for his own education to read applied books with examples, as well as books about software:

You can’t really do anything without software, so the books that teach you in the context of a particular software package are some of the best. Frank Harrell wrote a book on using R for survival analysis and basic regression. What you want are books written by practitioners, people who’ve actually done things in the field.

Next steps

Data science is evolving quickly. To keep up, you need to continually self-educate, but also, as Heineike stresses, how you choose to focus that learning is also essential. This is the thought process she looks for when determining whether a data scientist is right for her team.

I want people who will bring something to the table, so maybe they have some expertise in some area of statistics or mathematics or computer science that’s kind of novel to the team and broadens what we can think about. You need to have a ferocious appetite for learning and know how to cope with continually not knowing what you’re doing — know how to continually be in a position where you have to learn a lot.

Original Post

Author Bio:

Ever since daniel_levine Daniel can remember -- perhaps right after his first steps -- he’s been making spreadsheets, breaking down data, and looking to find the connecting threads hidden behind numbers. Daniel works with RJMetrics analysts, and anyone else who can teach him something new, to unearth from the 1’s and 0’s the takeaways that truly make a difference.


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