Top Skills for Data Scientists in 2022

Data scientists need to integrate a vast array of technical and soft skills to be successful.

As the role of data science engineer evolves, we need to keep our skills up to date. The title of this article may have been a bit overly ambitious, clearly no one article can summarize all that you will need to succeed in datascience. But lists like this can put things into perspective, so that’s what I hope to do today as you are reading this.

Introduction to Data Science Skills

Think about it, two decades ago, data scientists didn’t even exist. Yes albeit, some people cleaned, organized and analyzed information — but the data science professionals we admire today stand at the head of a relatively new and evolving career path.

Today we have data engineers, data scientists, DevOps engineers, ML engineers and the list goes on.

So what remains? While the field of data science continues to evolve with exciting new progress in analytical approaches and machine learning, there remains a core set of skills that are foundational for all general practitioners and specialists, especially those who want to be employable with full-stack capabilities.

So what are the skills that really gets you more easily employed? So what is a datascient in today’s changing world anyways? They’re part mathematician, part computer scientist, and part trend-spotter, people like to say.

Increasingly businesses understand they need to leverage data, Big Data and A.I to remain competitive. According to research conducted by the multinational professional services company Accenture, 79 percent of enterprise executives agree that any organization that does not incorporate big data into their growth strategy would lose their competitive edge and potentially go out of business.

In this big-data-centric (and small-data) environment, data scientists are more than useful — they’re crucial to business success.

This list is not an elaborate primer, but just a context driven list to get orientated. I hope to give some emphasis of real-world skills and business soft-skills that are also relevant to be a successful data scientist in the field.

1. Get Educated

Data published by IT Career Finder reveals that roughly 40 percent of data scientist positions require an advanced degree such as a master’s or Ph.D.

If you are an aspiring data scientist, do keep in mind that technical and non-technical skills are both important as well as practical applied skills such as:

• Data Wrangling / Feature Engineering

• Writing SQL Queries & Building Data Pipelines

• Storytelling (i.e. Communication) to complement Data visualization

• Regression/Classification

2. Strategic Contextual Business Application

It’s create to have a theoretical foundation and academic skills-set but how do you apply it to the business environment of a company? Think about how to communicate it to management, leadership and the business context at large? Think backwards here:

• Communicate data insights in a simple story to empower the decision-makers to act on.

• Collaborate with business stakeholders to identify their business needs and convey the expected results.

• Showcase your Data Scientist skills on Big Data platforms and analytics tools to gain business insights.

• Identify/create appropriate algorithms to solve business problems.

• Craft experiments to corroborate your assumptions  and make available scenario models as and when necessary.

• Support data collection, integration, and retention requirements based on the collected data.

3. Have a Technical Background as a Bachelor’s Preferably

Many datascience candidates can learn the technical skills while only having a bachelor’s degree in Math, Statistics, Economics, Engineering or Computer Science. If aspiring data scientists really want to home in on a specialty and boost their resume above their competitors’, they might also opt into targeted training programs or boot camps in analytical disciplines like predictive analytics, data mining or database management. 

Having a technical Bachelor Degree is usually the way to go if not having a Masters or PhD level education.

4. Choosing a Data Science Specialization

For simplicity’s sake knowing what type of specialization you wish to pursue in datascience is also useful.

According to one analysis written for the Harvard Business Review, these experts tend to fall into one of three categories in their later-stage careers: 

• Business Intelligence: This category involves organizing company data into easy-to-understand dashboards, reports and emails.

• Decision Science: These specialists focus on using data to help companies make smarter, well-supported business strategy decisions. 

• Machine Learning: Data scientists in this vertical build and apply data science models to perpetually gather information and further business operations. 

So think about which you find more interesting, ML, BI or DS. Get the skills that best lead you to your end-goal. Then reverse engineer the steps required both educational, occupational and career-wise to get there.

5. Be Mathematically, Statistically and Critical Thinking Inclined

A future in datascience implies that you genuinely like maths, stats and solving problems that require critical thinking. Think about it, will you enjoy working with numbers, data and business problems for years?

Critical thinking and problem solving takes years to refine. You need a keen problem-solving mind to figure out what goes where and why, and how it fits into the big picture. Data science is all about being data-centric, finding a needle in a haystack and working with data to optimize business value.

Optimizing critical thinking and problem solving are skills that are creative, not dry and boring. All this said, aspiring data scientists must build a foundation of necessary technical skills before truly mastering a field.

6. Learn Foundational Skills

The foundational skills of datascience are things like programming, data visualization, teamwork, collaboration, python, social media mining, fundamental statistics and things you might be using every day or every week.

• Model deployment

• Machine learning and AI

• Storytelling with Data visualization

• Writing code in Python, R or SQL

• Communication skills that translate technical knowledge

• NLP

• High level Maths

• Business Savvy

• Regression/Classification

• Data Wrangling

• Data Intuition

6. Be a Good Programmer

Data scientists need to be able to program their data science tools. This means that you should have a strong foundation in a few of the below. A data scientist is a jack-of-all trades, and would be able to learn whatever language was most aligned with a project.

Some of the top programming languages generally associated with datascience are:

• Python

• R

• Julia

• C/C++

• Java

• Scala, etc…

7. Putting it All Together

When you become a Data Scientist, the organization will want you to be a problem solver and find the best possible solution for a problem statement. In such a case, you need to consider what is important, what doesn’t matter, and how to interact with engineers, stakeholders, and sometimes even end-users.

Depending on the type of role you have in an organization, what you do the most will depend on your job description, level of seniors and so forth:

8. The Good to Have

There are many other things that we don’t necessarily consider skills but are obviously things you may run into, such as:

• A/B Testing (Experimentation)

• Big Data and Processing large data sets

• Business and Data Intuition

• SQL/NoSQL and Database management

• Explanatory Models

• Microsoft Excel

• Data Visualization Tools (e.g. such as Tableau)

• IDEs such as RStudio and Jupyter Notebook

• Familiarity with Kaggle and Google Colab

• Concepts of hypothesis testing and regression analysis

• High level maths (for pearing “under the hood” not just being “behind the wheel”)

• Recommendation systems

• Basic knowledge of Shells, SSH, and Docker

• Common Data Science tools

• RStudio for data science in R; Python data visualization libraries like Matplotlib and Seaborn.

• Knowledge of Cloud ML Services Azure, AWS, Google Cloud Services

• And tons of others that you will pick up or need to refresh along the way.

10. Follow the Demand

As your career evolves as a Data scientist your skills might be honed in special ways depending on your industry, your company and your specialization. By the time you reach your goals, the environment for data engineers may have been transformed.

According to Indeed, in just three years, the number of Data Scientists’ job posting has increased by 78 percent. According to Glassdoor, Data Scientists ranked first among the 50 best jobs in the United States. Moreover, almost 60 percent of global companies cannot analyze or classify their data.

The average data scientist makes over $74,000 in the United States, and with inflation and a bit of specialization this could be higher. In the Great Resignation and with many early retirements during the pandemic, the demand for data engineers of all kinds is still considered strong.

Becoming a data scientists could onboard your career into working in machine learning and Artificial Intelligence. You might enjoy my A.I. Newsletter called A.I. Supremacy here.

11. The Importance of Soft and Storytelling Sales Skills

Data scientists rarely work in isolation. For companies to be data-centric to improve profits, it really is a team sport. Regardless of your data science role, you’ll need to communicate and collaborate with clients, developers, other data professionals, designers, and even executives.

Being able to share technical knowledge to a variety of audiences with storytelling and sales accum could mean the difference between having a successful and mediocre career. No matter how introverted you may consider yourself, don’t neglect the power of soft skills in the field and to help you reach your goals.

No matter what background you come from, your gender, ethnicity or country or origin, soft skills that are exceptional level the playing field and enable you to break through glass ceilings of career elevations, promotions, successful salary negations and so forth. While we work with data, people use data at the end of the day. Never forget that.

In business as in many aspects of life, an organization is the sum of its parts. The cohesion and collaborative power of a team are usually more important than the intelligence or creativity of any one member. Working well in a team means empowering an organization, colleagues and leadership that comes from experience.

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I’m hoping this Newsletter can help, inform and inspire someone out there.

Thanks for reading!