Database Reference
In-Depth Information
Be Creative
The second, related piece of advice is to be creative. The command line is very flexi‐
ble. By combining the command-line tools, you can accomplish more than you might
think.
We encourage you to not immediately fall back onto your programming language.
And when you do have to use a programming language, think about whether the
code can be generalized or reused in some way. If so, consider creating your own
command-line tool with that code using the steps we discussed in
Chapter 4
. If you
believe your command-line tool may be beneficial for others, you could even go one
step further by making it open source.
Be Practical
The third piece of advice is to be practical. Being practical is related to being creative,
but deserves a separate explanation. In the previous subsection, we mentioned that
you should not immediately fall back to a programming language. Of course, the
command line has its limits. Throughout the topic, we have emphasized that the
command line should be regarded as a companion approach to doing data science.
We've discussed four steps for doing data science at the command line. In practice,
the applicability of the command line is higher for step 1 than it is for step 4. You
should use whatever approach works best for the task at hand. And it's perfectly fine
to mix and match approaches at any point in your workflow. The command line is
wonderful at being integrated with other approaches, programming languages, and
statistical environments. There's a certain trade-off with each approach, and part of
becoming proficient at the command line is to learn when to use which.
In conclusion, when you're patient, creative, and practical, the command line will
make you a more efficient and productive data scientist.
Where to Go from Here?
As this topic is on the intersection of the command line and data science, many
related topics have only been touched upon. Now, it's up to you to further explore
these topics. The following subsections provide a list of topics and suggested resour‐
ces to consult.
APIs
• Russell, M. (2013).
Mining the Social Web (2nd Ed.)
. O'Reilly Media.
• Warden, P. (2011).
Data Source Handbook
.
O'Reilly Media.
