The Data Science Handbook, 2nd Edition

26Parting Words: Your Future as a Data Scientist

So, you’ve read this book, and let’s assume for the sake of argument that you thought the subject matter was pretty cool (and that the writing was brilliant, of course). What now?

My most important advice is to get out there and start tackling some real problems. I’ve done work as a software engineer and an academic, and I’m constantly impressed by how much more intellectually dynamic data science is than anything else I’ve done. In a single day, I will flit between low‐level debugging, designing software architecture, helping clients translate a business problem into math, and brushing up on my linear algebra. In data science there is always something new that you can learn, and usually something new that you should learn, and no book can substitute for real experience in that kind of environment.

As far as broadening your knowledge base, there are several directions (not mutually exclusive) that you might consider growing:

• Really the best, if you have a particular area of application in mind, is to become more of a domain expert in whatever it is you want to apply data science to. Remember that the key to doing great data science is to ask the right questions, and the only way to do this is to have a deep understanding of the domain you’re studying.
• A lot of data scientists almost double as software engineers. I would probably put myself in this category. They know a number of additional programming languages, they’ve written ...