Summary: What do you need to do to get an entry level job in data
science?
This article is written for anyone who is considering becoming a data
scientist. That includes young people just starting their bachelor’s
degrees and folks in the first two or three years of their careers who want to
make the switch.
It’s not for
folks who know they are going to pursue one of the new Master’s in Data Science
or Ph.D. candidates. It’s for folks looking for entry level jobs that are
specifically on the data science career ladder.
Is There a Data Science Career Progression That Doesn’t Require an
Advanced Degree?
Yes there
is. Like many high skill professions that’s not to say that an advanced
degree won’t make it easier but there are definitely ways to enter this market
with only a bachelor’s degree.
If you’ve
been practicing data science for more than five or ten years you also know that
the majority of us over 35 don’t have specific data science degrees. We
came to data science via a variety of related disciplines and gained our cred
largely based on performance and experience. It’s only the cohort under
35 working in data science that’s likely to have a DS-specific degree, advanced
or bachelor’s.
The flack
this article is likely to draw is not over the level of degree required or the
types of experience but the just-below-boiling controversy about who gets to
call themselves a data scientist. The problem in our profession, and I’m
not going to solve it here, is there is not an accepted nomenclature that differentiates
the various skill levels of data scientists or who gets to wear that title at
all.
Employers
aren’t helping since actual data science jobs may be called engineer, analyst,
developer, team lead or many other less exciting sounding titles. Other
employers are giving data science titles to folks who are not really doing data
science, but more descriptive analytics and straight EDW work.
So for simplicity’s sake I’m going to call our target audience folks
who are seeking positions as Junior or Associate Data
Scientists. Specifically that means doing work that involves
detecting signals in the data that can be used to make predictions about future
behavior. Not simple descriptive historical analysis of what’s happened
in the past.
For Beginners What Does the Market Look Like and What Type of Work Will
You Do?
There are two key points to understand here. The first is that
the data science market has divided into two distinctly different segments,
Production and Development.
Production: This
is by far the largest and most mature segment where predictive analytics has
been used for longest and where it is best integrated to create truly
data-driven businesses. Large B2C service businesses dominate this group,
specifically insurance, financial services, cable and telecos, healthcare, plus
retail, ecommerce, and some manufacturing. These companies are widely
distributed geographically so you can work pretty much anywhere. The
primary data science activities are predictive analytics and recommenders.
Development: This
is the new and sexy world of data science that gets all the press
coverage. In these enterprises the data science and the code are the
product. Think Google, Facebook, eHarmony, Apple, and the thousands of
start-ups that are either developing new analytic and big data platforms, or
products with embedded analytics. This is also where you find the newest
developments in data science including deep learning for image, text, and
speech recognition, much of IoT (some crossover here to the production world),
and all the flavors of AI.
The
Development world is geographically concentrated in a few areas that we all
know: the Bay area, Silicon Beach, New York, Boston, and maybe Austin.
This is exciting and heady stuff where you will probably devote upwards of 60%
to 70% of your substantial starting salary to rent.
As a new
Associate Data Scientist you are much more likely to find your first career
step in the Production world.
The Four Paths of Data Science
The second
main point is that your career progression in DS will probably take you down
one of four paths represented by different types of data scientists.
These four types are ultimately differentiated by what they spend their time
doing.
In short,
they conclude there are four types of Data Scientists differentiated not so
much by the breadth of knowledge, which is similar, but their depth in specific
areas and how each type prefers to interact with data science problems.
1.
Data
Businesspeople are those that are
most focused on the organization and how data projects yield profit. At the
entry level you’ll be performing the junior duties of blending and cleaning
data and preparing basic predictive models.
2.
Data
Developer. Focused on the technical problem of
managing data — how to get it, store it, and learn from it. At the entry level
you’ll be working with Hadoop as well as structured data. If you are more
interested in the data science infrastructure side this may be for you and is a
particularly good path for a current analyst and IT staff to move up into the
data science career path.
3. Data Creatives. Often
tackle the entire soup-to-nuts analytics process on their own: from extracting
and blending data, to performing advanced analyses and building models, to
creating visualizations and interpretations. This is a more senior role
innovating new types of predictive analytic use cases, data products, and
services. This may also be you if you find yourself in a company with
little or no experience with advanced analytics but you’re unlikely to get this
job fresh out of college with no experience. Data Creatives are
heavily present in the Development world.
4.
Data
Researchers. Nearly 75% of Data
Researchers have published in peer-reviewed journals and over half have a PhD.
These are folks who are innovating data science at its most fundamental
level.
According to
Harris, Murphy, and Vaisman it’s not the skills that are different but the way
we choose to emphasize them in our approach to Data Science problems. Here’s their
chart.
This is an
important decision since you need to do activities within data science that you
like. This may lead you toward an advanced degree or simply to develop
you skills through experience. It’s not something you have to decide from
day one but one that you’ll want to consider early in your career.
The Skills You’ll Need to Enter the Data Science Market
If you were
shopping for a two-year Master’s Degree in Data Science you’d have lots to pick
from. If you search for Bachelor’s degrees in Data Science you’ll find a
good selection but at many institutions the undergraduate degree is more likely
to be titled ‘Computer Science’ leaving you to wonder if you’re actually getting
the knowledge that you need.
If you have
a choice, pick a college that specifically offers a Data Science degree.
If you don’t have that choice you’ll have to analyze and select the blocks of
learning that you’ll need.
Yes you need to be grounded in the broad aspects of computer science
but in addition there are specific skills and knowledge you’ll need to
master. The best description I’ve seen for this incremental learning is
also an excellent guide for those of you who have recently finished your bachelors.
It’s from an article by Amy Gershkoff, the Chief Data Officer at Zynga and
describes their in-house program for growing their own data
scientists.
Zynga’s
in-house program is 12 to 18 months. To be considered there are a variety
of performance requirements and academically the candidate needs a minimum of
two previous semesters of coursework in statistics, economics, computer
science, or similar. At Zynga, some of this is in an on-line academic
environment and some is mentored by their in-house data scientists. This
could easily be the course list for your undergraduate program. I have
added some observations of my own.
Phase I: Foundational Statistical Theory
Participants learn the basics of probability theory and statistical
analysis including sampling theory, hypothesis testing, and statistical
distributions. For statistical analysis, topics include correlation,
standard deviations, and basic regression analysis, among others. Usually
one to two semesters of an online statistics course (such as Princeton University’s online course) covers this material.
Phase II: Foundational Programming Skills
To be an
effective data scientist, knowledge of scripting languages is a
requirement. Selecting which ones is a matter of discussion. My
take is this:
SQL: Not really a hard
data science language but reflects the fact that you’re likely to have to
extract data yourself from relational databases. Also, SQL is now almost
universally available as a query language on Hadoop (it’s really no longer accurate
to call it NoSQL).
Python: The big discussion
over the last five or so years has been around R versus Python. Python is
my pick as a production language with a very generous data science
library. More importantly, as SPARK has come on so quickly as the preferred
tool on Hadoop, Python works easily here while R does not. In the most
recent surveys you’ll see Python pulling away from R.
SAS: Yes SAS. SAS
was practically the original DS scripting language before R and Python.
Although it’s included here under programming skills you can learn to use the
SAS packages via drag-and-drop UI just as easily. Depending on what
survey you’re reading you
may or may not see SAS on each list, but in the Production world SAS is
extremely common and having this skill is a definite competitive
advantage. IBM SPSS is an option but SAS has a huge lead in
adoption. You will rarely encounter SAS in the Development world.
Phase III: Machine Learning
Participants
learn both supervised and unsupervised learning techniques. Supervised
learning techniques include decision trees, Random Forrest, logistic
regression, Neural Networks, and SVMs. Unsupervised learning techniques
include clustering, principal components analysis, and factor analysis.
Only a matter of a year or two ago you could not be an effective data
scientist without knowing the inner workings of these algorithms including how
to manipulate their tuning parameters to optimize results. The late
breaking news however is the new availability of completely automated
predictive analytic platformswhere selection and operation of the ML algorithms is handled by AI.
The
likelihood that your new employer will have any of these new platforms on hand
is still fairly slim but growing by the day. Perhaps you will be the one
to suggest they utilize them. They can really speed up the modeling
process. Until then, you need to know what’s going on under the hood of
all the major ML algorithms.
Phase IV: Big Data Toolbox
It is
important for data scientists to not only learn the necessary algorithms, but
also to learn how those algorithms need to be adapted for large datasets.
For this reason, basic knowledge of tools such as Hadoop, Spark, and an
analytics platform for large data sets constitutes a dedicated module.
It’s here
that you’ll learn how those models you built in the last section are put into
operation to assist business decisions. Until they’re operationalized,
they’re of no value.
It’s also
here that you’ll learn the basics of streaming versus batch both in model
development and implementation. Spark has come on very fast with
extremely high adoption rates and is the basic tool now for both batch and
streaming.
Should You Specialize Early?
In the
Development world you will increasingly only be selected if you have a
specialty. In the Production world you are likely to have more
opportunities if you don’t specialize. Having said that there are two
areas you may want to examine which can be picked up fairly rapidly and are
considered specializations within the Production world.
IoT for Manufacturing: This
is the use of predictive models on streaming data from SCADA systems and the
like to predict the quality of output during a production run or the imminent
failure of a piece of capital equipment.
If you
wanted to make your living in an area dominated by manufacturing you would
consider adding these to your portfolio early in your career.
For the most
part however, if you’re in the Production world, predictive modeling and
recommenders will be a complete toolset for several years.
Remember
also that our profession is changing fast. It is already well past the
time that a single data scientist could master the entire field.
Employers may still be looking for unicorns but very rapidly there will be
emerging specialty fields you may consider as your career progresses.
Deep learning, natural language processing, image processing, and AI are all
examples that will take either additional education or serious OJT.
What about
the rumors of those outsized salaries even for beginners? Well they are
at least partly true in that you will earn a well above average salary compared
to other analyst or IT staff positions. You’re not going get a Silicon
Valley salary if you’re working in Milwaukee.
The best salary and skills studies come from O’Reilly. Their most
recent survey for example says that a Master’s degree will only add about
$3,500 per year to your earnings. This is a well done
survey that
evaluates not only salary but time spent in different tasks, tools used, and
other factors. Be sure to carefully evaluate who filled out the surveys
and whether you think they are representative. There are no purely
objective bias-free surveys in our profession.
As Your Career Progresses
Data science has been and continues to be a field in which knowledge of
tools as well as business in paramount. We utilize a complex toolbox to
extract, blend, clean, transform, engineer, model, and implement models that
can create business value from data that only a few years ago was not
considered valuable.
It should
come as no surprise that innovation is simplifying and automating the toolbox
of existing tools even as new tools are arising. In the past if we were
expert carpenters with great skill with our tools, in the future we will be
more like architects bringing a broad range of tools and design skills to bear
to build value.
In
management consulting where I spent many years we used to say that a consultant
needs three legs to stand on, domain knowledge (knowledge of a particular
industry), process knowledge (deep understanding a particular process such as
planning, manufacturing, or accounting), and methodology (in management consulting
this means process improvement, reengineering, strategy development, or package
implementation among others). As your career progresses you should build
your own foundation on these three principles where methodology becomes the
skills of data science that you’ve mastered. The other two legs, deep
knowledge of one or more industries and one or more business processes will be
why future employers seek you out.
