The must-have skills

Today data science is at the heart of nearly every
business and organization. The growing need to not only gather data
but sift through it and analyze it to direct decisions has prompted
a huge demand for qualified data scientists. 

A data scientist career holds great appeal for those who
to not only find a position in demand but one that offers high
earning potential and high job satisfaction. It ranks
as the best job for 2019 in America on Glassdoor with a
median base salary of $108,000 and a  rank of
4.3 out of 5 for job satisfaction. ^[1]

To get clarity on the differences between a data scientist
and a data analyst, see the following video:

What does it take to be a data scientist? Obviously,
strong technical skills are essential. But the
question is which specific skills does on have to master to set on
this particular career path?

RELATED: THE EVOLUTION OF COMPUTER LANGUAGES
OVER 136 YEARS^[2]

The must-have skills

The answer to the question of essential skills for data
scientists continues to change and to evolve as evidenced by a
widely-quoted article on the subject by KD Nuggets,
9 Must-have skills you need to become a Data
Scientist, updated^[3]. The
“updated” was added to the title because the number of skills on
the list grew over the years. 

As it stands now the 13 skills on the KD Nuggets list are
the following:

1. Education
2. R Programming
3. Python Coding
4. Hadoop Platform
5. SQL Database/Coding
6. Apache Spark
7. Machine Learning and AI
8. Data Visualization
9. Unstructured Data
10. Intellectual curiosity 
11. Business Acumen
12. Communication Skills
13. Teamwork

While some of the skills come as no surprise, for you’d
expect a data scientist to master the languages and technical
skills used in data science, some of the items are a bit more
general. And that is because data science is not a matter of mere
rote extraction of numbers but of making sense of it all in context
of business goals. 

Not just a science but an art

That is why several years
ago Venture Beat^[4] 
suggested that “data artist” may be a more accurate job title:
“Perhaps these scientists are not the Einsteins and Edisons but the
Van Goghs and Picassos of the big data revolution.” The point is to
recognize that data scientists don’t merely observe and quantify
but come up with creative approaches to extracting insight and
value from data.

A successful data scientist is not just someone who has
checked off the list of hard skills; he or she has to have the
ability to think about how to approach a problem in a new way that
opens the way to a solution and then effectively communicate what
worked and why. Far more than a mere quant, the successful data
scientist is a creative thinker and problem solver with domain
understanding.

The interview proof of hard and soft
skills

This mix of skills is what emerges from the list Roger
Huang presents in Every Data Science Interview Boiled Down To
Five Basic Questions^[5]. Those five
questions work out to 60% hard skills,
20% soft skills, and 20% ability
to apply knowledge to the situation. 

The hard skills makes up three of the questions: one on
math, one on coding, and one on statistics. Soft skills come into
play in providing the answer for what Huang calls “behavioral
questions” that assess the applicant’s fitness for the company
culture. Then there is what he calls the “scenario question,” the
one that challenges applicants to demonstrate their ability to
apply what they’ve learned to a particular situation and outline an
approach that could work.     

Seeing the bigger picture

As one of the distinguishing features of the data
scientist is the intellectual curiosity that prompts a person to
pursue real understanding, it is expected that the person will do
more than merely crunch numbers. As a Wall Street Journal
article, What Is a Data Scientist,
Anyway?  declared, “an effective data
scientist … has an ability to see how particular subsets of data
may be more useful than others, and what conclusions can be drawn
from them.”^[6]

It’s also important to take an interest in the big picture
of the organization and what outcomes are pertinent to its goals.
That’s consistent with what Dr. John Maiden, a data scientist with
JP Morgan Chase’s Digital Intelligence, described in a
NYC Data Science Academy
blog.^[7]

One of the key things they look for at the financial firm
is the ability to “apply solutions to large, messy real world
problems.” He explains that is because the job entails less
involvement with “straightforward data analysis” than with
“wrangling messy datasets to provide actionable insights.^[8]”

The Cs are key 

In the video below, Bernard Ong, AVP, Lead Data Scientist,
Advanced Analytics at Lincoln Financial Group, talks about his own
career path and what he looks for in candidates when hiring for his
team. In addition to the coding and math skills, he says, he wants
candidates that possess what he calls the “3 Cs.” These stand for
curiosity, creativity, and critical thinking. 

Ong explained why a good data scientist has to have those
capabilities in order to “not just understand modeling and
predictive analysis but also what kind of business challenges we
are trying to address.” This is where thinking about how things fit
together is important.

“It begins by asking the right questions, which stems from
curiosity. It continues with critical thinking to assess the
problem and progresses with creativity to come up with innovative
solutions and in communicating the vision to the business end in
terms they understand,” added Ong.

Telling the data story that drives
decisions

When it comes to communicating this vision, “technical
terms” just don’t cut it. Rather, you “have to be able to tell the
story behind the data,” Ong points out. 

Working out such movements within a firm certainly call
for capitalizing on soft skills, but they also are crucial even for
those who stay within the data scientist role. Maiden emphasizes
the importance of being able to communicate well “to provide
actionable advice to drive decision making.” That calls not just
for oral and written communication but for data visualization,
finding the right chart charts and graphs to tell the data story in
a way that makes it understandable even for those who are not
schooled in data analytics.

As people respond strongly to visual proof, graphically
representing the correlations and causation surfaced by the data
analysis conveys the relationships in a much more compelling way
than mere text.  Data visualization is really where
mathematical quantification and creative artistry come together
toward the same end of promoting data-driven decisions.

KD Nuggets touches on that same point in emphasizing how
important it is to develop “a solid understanding of the
fundamentals of the industry and the goals of the firm” to enable
the data scientist to harness “technical abilities to make a
difference in the long run.” It’s of even more vital interest for
data scientists whose career aspirations include a shift into a
role within the C-Suite.

Creative approaches solve data problems

In the same vein, Ong says that you have to have an
understanding of the larger context to be sure you’re working with
the data required to solve the problem: 

“One of the challenges is getting the right
data to find the answers needed. You can be curating large amounts
of data and still find that it doesn’t provide the information you
seek.”

That’s where creative thinking comes into play in working
out “data fusion.” That approach is to combine “different sources
of data into new combinations that could provide the right kind of
data.”

“This is where creativity helps the data scientist make
new discoveries and work out solutions,” Ong declares.

Ultimately, working with Big Data effectively calls for
using both the creativity  and methodical processes in an
ideal combination that Einstein described as
the ideal of science: ^[9]

“The mere formulation of a problem is far more essential
than its solution, which may be merely a matter of mathematical or
experimental skills. To raise new questions, new possibilities, to
regard old problems from a new angle requires creative imagination
and marks real advances in science.”