Database Reference
In-Depth Information
CHAPTER 10
Conclusion
In this final chapter, the topic comes to a close. We'll first recap what we have dis‐
cussed in the previous nine chapters, and will then offer you three pieces of advice
and provide some resources to further explore the related topics we touched upon.
Finally, in case you have any questions, comments, or new command-line tools to
share, we provide a few ways to get in touch.
Let's Recap
This topic explored the power of employing the command line to perform data sci‐
ence tasks. It is an interesting observation that the challenges posed by this relatively
young field can be tackled by such a time-tested technology. It is our hope that you
now see what the command line is capable of. The many command-line tools offer all
sorts of possibilities that are well suited to the variety of tasks encompassing data
science.
There are many definitions for data science available. In
Chapter 1
, we introduced the
OSEMN model as defined by Mason and Wiggens, because it is a very practical one
that translates to very specific tasks. The acronym OSEMN stands for obtaining,
scrubbing, exploring, modeling, and interpreting data.
Chapter 1
also explained why
the command line is very suitable for doing these data science tasks.
In
Chapter 2
, we explained how you can set up your own Data Science Toolbox and
install the bundle that is associated with this topic.
Chapter 2
also provided an intro‐
duction to the essential tools and concepts of the command line.
The OSEMN model chapters—Chapters
3
(obtaining),
5
(scrubbing),
7
(exploring),
and
9
(modeling)—focused on performing those practical tasks using the command
line. We haven't deveoted a chapter to the fifth step, interpreting data, because, quite
