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But at this point, we'd like to go a bit further than that, to strive for
something a bit more profound.
Let's define data science beyond a set of best practices used in tech
companies. That's the definition we started with at the start of the topic.
But after going through this exploration, now consider data science to
be beyond tech companies to include all other domains: neuroscience,
health analytics, eDiscovery, computational social sciences, digital hu‐
manities, genomics, policy…to encompass the space of all problems
that could possibly be solved with data
using
a set of best practices
discussed in this topic, some of which were initially established in tech
companies. Data science happens both in industry and in academia,
i.e.,
where
or
what domain
data science happens in is not the issue—
rather, defining it as a “problem space” with a corresponding “solution
space” in algorithms and code and data is the key.
In that vein, start with this: data science is a set of best practices used
in tech companies, working within a broad space of problems that
could be solved with data, possibly even at times deserving the name
science. Even so, it's sometimes nothing more than pure hype, which
we need to guard against and avoid adding to.
What Are Next-Gen Data Scientists?
The best minds of my generation are thinking about how to make
people click ads… That sucks.
— Jeff Hammerbacher
Ideally the generation of data scientists-in-training are seeking to do
more than become technically proficient and land a comfy salary in a
nice city—although those things would be nice. We'd like to encourage
the next-gen data scientists to become problem solvers and question
askers, to think deeply about appropriate design and process, and to
use data responsibly and make the world better, not worse. Let's ex‐
plore those concepts in more detail in the next sections.
Being Problem Solvers
First, let's discuss the technical skills. Next-gen data scientists should
strive to have a variety of hard skills including coding, statistics, ma‐
chine learning, visualization, communication, and math. Also, a solid
foundation in writing code, and coding practices such as paired
