Main image of article Data Scientist Skills: What You Need to Know to Maximize Salary

Data science is a complex profession. Your employer relies upon you to sort through massive, often messy datasets to find the insights that might save—or destroy—the company. You have to keep up-to-date on the latest tools, programming language updates, and data scientist skills that allow you to do your job. And tools will only take you so far—at a certain point, you have to make logical guesses about the data in front of you.

That being said, data science can prove a lucrative profession for those with the right mix of skills and experience. According to Lightcast, which collects and analyzes millions of job postings from across the country, the median salary for a data scientist currently stands at $112,359. With specialist skills (such as machine learning and artificial intelligence), that number can climb still higher.

But which data scientist skills are important? What do you need to know beyond tools, platforms, and languages? Let’s dive in:

What technical skills do I need to become a data scientist?

A data scientist’s projects and goals can vary wildly depending on their organization and its mission. However, data science rests on a foundation of technical skills common across industries. These include:

  • Statistics (i.e., statistical analysis)
  • Data processing
  • Data visualization
  • Data storage
  • Programming languages (Python, R, and more)
  • Machine learning
  • Artificial intelligence

At its core, the data science profession is all about analyzing massive data sets for insights. Doing so effectively depends on the data scientist’s intuition and analytical skills—which come with education and experience. During the job interview for a data scientist position, you’ll likely face questions about how you’d make decisions or predictions based on incomplete or messy datasets; even if you don’t know the answer, your interviewer will expect you to present good logic, educated guesses, and (unless you’re just starting out) previous experiences.

Given the popularity of data science, many specialists are transitioning into data scientist roles. For example, many economists, mathematicians, research scientists, and statisticians decide to pursue data science; in such cases, their existing analytical skills are easily transferrable to their new jobs.  

Do data scientists need business skills?

Business skills never hurt if you’re analyzing data (and communicating the results) in a corporate context. Detailed knowledge of a particular industry is often a must.

“Let’s say I work in D&A for an airline, I’m a wizard at A.I. tools and create great reports on how many flights people take, average searches before actually purchasing a ticket, average ticket spend, average flights per person per year—the basic data the system delivers,” Kathy Rudy, chief data and analytics officer at IT research and advisory firm ISG, told Dice in 2021. “All great information, but what does the business need to know? Maybe it’s the average number of empty seats on a particular route or number of people on a waitlist per flight to determine if they need to add flights to a route? Just knowing how to work an Xbox does not mean you know how to play the games.”

Learning the needs of a business is often a matter of communicating frequently with business stakeholders throughout the organization, such as managers and executives. Other data scientists can opt to pursue formal educational opportunities—if not a full-on degree such as an MBA, then classes in fundamentals of accounting, digital marketing, or whatever else your organization focuses on.

Data scientists also need “soft skills” such as communication and empathy, as they’ll need to convey their plans and the results of their analysis to other organizational stakeholders in a way the latter can understand.

Are data scientists different from data analysts and data engineers?

The short answer to this question is “yes.” These three roles are definitely not interchangeable, although data analysts and data scientists have some glancing similarities (a focus on analyzing datasets, etc.). Here’s how the roles break down, especially with regard to skills:

Data Scientist: As we’ve discussed, data scientists combine statistics, Big Data crunching, analytics tools, machine learning to transform massive datasets into crucial insights that organizations can use to survive and thrive. It’s a very strategic role that requires numerous skills, including critical thinking and data analysis.

Data Analyst: Like data scientists, data analysts are tasked with analyzing data for insight—but they often do so on a much more “tactical,” smaller scale. A “typical” analyst might work with end users to figure out what they need from the data, then analyze that dataset and communicate the results. Key data analyst skills and tools include Apache Hadoop, data visualization suites like Datawrapper, and Tableau.

Data Engineer: Data engineers construct and maintain (often massive) repositories for data, such as the customer-information databases that large companies use. They also monitor the movement and status of data through these systems, and can assist data scientists and data analysts in locating and cleaning needed datasets. Key skills and tools include Hadoop, Docker, Scala, and Kubernetes.

What certifications do data scientists need?

The data science industry includes a broad array of certifications. Here are three ultra-popular ones:

Keep in mind that many data science certifications are for more advanced data scientists (i.e., those who’ve spent a number of years in the industry). There are also certifications that verify your proficiency with particular tools and platforms such as SAS, Azure, Google’s TensorFlow, and others.  

What data scientist skills will boost your salary?

Knowing “cutting edge” skills such as machine learning and artificial intelligence (A.I.) will make you more valuable to potential employers. But if you want to really show how invaluable you are, try to master as much of the data science workflow as possible.

Why? According a SlashData analysis, most data scientists and machine-learning specialists are knowledgeable about just few parts of the overall data science/machine learning (DS/ML) workflow. The highest percentage is involved in data exploration and analysis, and far fewer participate in model deployment, project management, and model health and lifecycle management. They’re very good at what they do—but learning more of the workflow (and the data scientist skills involved in each) can open up an entire world of opportunities.


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