Newsflash: Top notch Data Scientist skills are in shortage.
No wonder. Burning Glass identified more than 300 core analytical skills relevant to the Data Science industry.
Whether you are looking to hire a data scientist or want to become one of the best in the field, you need to know what Data Science skills are the most critical.
Are coding skills critical and if so, what languages should a Data Scientist master? Are soft skills really important or is that what unemployed English majors aspiring to be Data Scientists say?
QuantHub offers Data Science testing and profiling. This is our area of expertise.
Based on our experience working with top companies on data strategy and developing proprietary analytical tools, we recommend upping your data literacy skills and honing these aspects becoming an ace in the following Data Science skills areas to get noticed.
Hard Data Science Skills
By “hard” Data Science skills, we mean, well yeah, difficult. Sorry!
You would think this one is obvious, but many employers and Data Scientists in-the-making overlook this basic skill because automated tools can disguise math and statistics talent, or lack thereof. Statistics, Linear Algebra and Calculus are critical competencies.
Aspiring Data Scientists need these in order to understand what these tools are assuming, to select appropriate algorithms for a job, and to grasp what is actually happening in the black box of complex equations.
While most Data Scientists have basic statistical backgrounds, a Data Scientist legend will have deep expertise in Statistics.
This is, after all, the core discipline from which Data Science has evolved! And this skill applies to all industries and companies.
Or is it “coding”? Regardless of which term you prefer, a stellar Data Scientist doesn’t just dabble in Scratch – they know what languages are in high demand and master them.
Being able to decode big data requires an understanding of several languages.
Which programming languages are most critical? R is widely used to solve any problem in Statistics and Data Science.
It has a steep learning curve so if you don’t know it well, it’s time to find a MOOC.
That said, we believe that Python will soon surpass R as the language of choice for Data Scientists. It is just as commonly used as R.
As if that weren’t enough on your Data Scientist resume, did we mention that SQL proficiency is pretty much assumed if you call yourself a Data Scientist?
You need to be well versed in relational databases at a minimum to boost your data skills profile.
Knowledge and experience with big data platforms is a definite plus and can set you apart from the field.
3. Data Exploration
You need a strong dose of patience to be good at this – and an addiction to caffeine won’t hurt. Exploration is a complex undertaking if done correctly.
A Data Scientist needs to be adept at finding his/her way through large amounts of data to understand its context.
You need to be able to use a variety of exploration techniques in a logical, organized way and couple those techniques with a key data science skill called curiosity (more on this skill later).
Critical technical skills include variable identification, univariate analysis, bivariate analysis, and visualization methods.
You can become even more legendary in your Data Scientist role if you discover new data relationships that warrant further investigation or bring new business insights.
4. Data Wrangling
You have to cowboy (or cowgirl) up if you want to be really good at dealing with lots of disparate data.
The best Data Scientists possess the true grit to take data in its wild and messy original form and “tame” it until it behaves in line with an analytical project.
There’s a perception among newly minted Data Scientists and others on the periphery of this field that the majority of a Data Scientist’s work is the sexy predictive modeling work they always read about. That’s almost never the case.
Datasets are notoriously chaotic, with database fields ill-defined, used for different purposes (literally the same field), full of outliers that no-one can explain, etc.
In most cases, that data must be transformed, standardized, normalized, cleansed, etc., before any real modeling work can provide value.
As with Data Exploration, patience is a virtue. No amount of technical know-how will overcome a lack of time and attention to this step in the Data Science process.
5. Machine Learning
A large number of Data Scientists are not truly proficient in machine learning techniques. If you want to stand out from other Data Scientists, use your math, programming, and database skills as a base to further develop highly valued machine learning skills.
Clustering, decision trees, factorization machines, optimization, and deep learning are just a few skill areas that you should master on the road to becoming a “Machine Learning Engineer”.
You should also be well versed in applying machine learning algorithms such as neural networks and frameworks such as TensorFlow and Spark.
Ah, deployment, the oft-forgotten final step.
None of the above skills will matter much if you can’t take the value created in exploring, wrangling, and modeling the data and then deploy that value into an operationalized production environment efficiently and with high quality.
Think about the state of software deployments before the DevOps movement: Time Consuming. Rigid. Monolithic. Riddled with Single Points of Failure.
Very few of those traits are as prevalent in today’s software engineering world. This is largely due to the focus that the industry has put on deployment, automation, etc. over the past 5-10 years.
A similar movement is needed in Data Science. Those who solve this challenge could find themselves very valuable in the field.
So, consider immersing yourself in the world of making exploration code and visualizations, wrangling scripts and processes, and machine learning models run efficiently in production.
You’ll need to understand modern deployment frameworks, monitoring frameworks, and dealing with actual production data at scale, in order to be effective in this area.
Somewhere Between “Hard” And “Soft” Data Science Skills is…
7. Business Acumen
We’re not suggesting that you have to go out and get an MBA. However, business skills and knowledge are the bridge between the hard and soft skills of Data Science.
The best Data Scientists build a strong bridge. They:
– have a good understanding of the business across multiple departments and teams
– confer with business professionals and stakeholders for input and feedback
– take their abstract business problems and issues, and they derive analytical insights
– understand business metrics and KPIs
The most valued Data Scientists assist in developing solutions and strategies that are aligned with business goals. This is the whole point of Data Science, so focus here.
Soft Data Science Skills
Even the most gifted of Data Scientists will only get so far without some touchy-feely skills. It is these skills which typically distinguish Data Scientists in interviews.
We suggest you start getting in touch with your inner Data Scientist (it’ll help get you ahead!).
Do you like to tell stories around the campfire? Break out the marshmallows, you’ll make a fab Data Scientist.
The whole point of Data Science is to communicate trends and insights to stakeholders who need to use them in their organizational roles.
If your audience doesn’t appreciate all the science and math that you have done – the technology, problem-solving, data challenges, successes and failures – no amount of programming prowess will overcome this failure in communication skills.
Storytelling and data visualization are two particularly important data science skills that’ll get you on a data science team.
You have to be able to create a compelling narrative from your mathematical findings that others can understand and which instills confidence.
Furthermore, you need to be able to narrow down insights and strategic options and make clear recommendations. This takes practice and focus.
Remember: key business strategies that are based on your data analysis can shift the direction of an entire corporation, so make sure that your story has a happy ending.
Remember that art class you took in high school thinking it was an easy A? Well, even if you got a C it wasn’t for naught.
An understanding of color palettes and the usage of blank space could serve you well.
Visualization is very important for communicating the value of Data Science to non-technical people who don’t care what a p value or correlation is.
The ability to provide a visual representation of data in an organized, clear, and concise manner that does not involve endless charts and graphs is paramount.
9. Problem Solving
Albert Einstein famously said:
“I have no special talent. I am only passionately curious.”
Much of Data Science is about being curious.
That is to say, trying to understand the behavior of complex data sets through experimentation and creative approaches, all in the name of problem-solving.
Expert Data Scientists possess a natural intellectual curiosity and desire to answer questions that people have.
QuantHub believes that this soft data science skill is a key differentiator in job interviews.
Business people are hoping their Data Science colleagues have this trait, so be sure to cultivate and demonstrate it in interviews, testing, and on the job, if you want that promotion.
10. Critical Thinking
In today’s fast-paced society, critical thinking is underrated and underdeveloped.
Ironclad Data Scientists are able to discern which problems are important to solve and then model what is critical to solving the problem.
All while avoiding extraneous information and variables.
The best Data Scientists also understand that their experience is imperfect and is not without risk if they rely on it too much.
A Data Scientist needs to be able to suspend their own beliefs and assumptions momentarily, step back, and evaluate a problem from multiple perspectives.
Let’s not forget: they work in a rigorous, complete manner.
So, becoming a great Data Scientist involves honing contradictory skill sets: technical and math skills, storytelling, business know-how, and intuitive problem-solving.
You only need, what, 8 degrees for that?
These skills are in fact a rare combination. It is surprising how many people who interview for Data Science jobs have built complex models, but when pushed on why they think the model worked or why they chose the approach they did, they don’t have a good answer because they are lacking in some of these skills.
The good news is there is a multitude of inexpensive or free resources available online dedicated to the training and development of these skills.
Even if your employer doesn’t reimburse you for this kind of training, invest in yourself.
This field is showing no signs of slowing down, so positioning yourself well within the bounds of “data science skills that will make you legendary” might just be the best investment you ever make!
About the Author
Nathan Black is the Chief Data Scientist at QuantHub, an AI-driven platform for attracting, vetting, and developing data scientists. QuantHub helps recruiters and corporations vet Data Scientists and related Analytics professionals to truly gauge their level of expertise. QuantHub’s comprehensive evaluation platform covers skill tests (Python, R, Statistics, etc) as well as real-world data challenges to verify that candidates have both the skills to do the job and also have the ability to apply those skills to real-world responsibilities. Visit QuantHub.com today to find out how you can begin to identify legendary Data Science candidates!