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.
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?
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. 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:
- R Programming
- Python Coding
- Hadoop Platform
- SQL Database/Coding
- Apache Spark
- Machine Learning and AI
- Data Visualization
- Unstructured Data
- Intellectual curiosity
- Business Acumen
- Communication Skills
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 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. 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.”
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 aNYC Data Science Academy blog.
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.”
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 thatEinsteindescribed as the ideal of science:
“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.”