Databases Reference
In-Depth Information
Instead, let's ask a related question: who in academia plans to
become
a data scientist? There were 60 students in the Intro to Data Science
class at Columbia. When Rachel proposed the course, she assumed
the makeup of the students would mainly be statisticians, applied
mathematicians, and computer scientists. Actually, though, it ended
up being those people plus sociologists, journalists, political scientists,
biomedical informatics students, students from NYC government
agencies and nonprofits related to social welfare, someone from the
architecture school, others from environmental engineering, pure
mathematicians, business marketing students, and students who al‐
ready worked as data scientists. They were all interested in figuring
out ways to solve important problems, often of social value, with data.
For the term “data science” to catch on in academia at the level of the
faculty, and as a primary title, the research area needs to be more for‐
mally defined. Note there is already a rich set of problems that could
translate into many PhD theses.
Here's a stab at what this could look like: an academic data scientist is
a scientist, trained in anything from social science to biology, who
works with large amounts of data, and must grapple with computa‐
tional problems posed by the structure, size, messiness, and the
complexity and nature of the data, while simultaneously solving a real-
world problem.
The case for articulating it like this is as follows: across academic dis‐
ciplines, the computational and deep data problems have major com‐
monalities. If researchers across departments join forces, they can
solve multiple real-world problems from different domains.
In Industry
What do data scientists look like in industry? It depends on the level
of seniority and whether you're talking about the Internet/online in‐
dustry in particular. The role of data scientist need not be exclusive to
the tech world, but that's where the term originated; so for the purposes
of the conversation, let us say what it means there.
A chief data scientist should be setting the data strategy of the com‐
pany, which involves a variety of things: setting everything up from
the engineering and infrastructure for collecting data and logging, to
privacy concerns, to deciding what data will be user-facing, how data
is going to be used to make decisions, and how it's going to be built
back into the product. She should manage a team of engineers,
