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What skills are needed to succeed in data science?

What skills are needed to succeed in data science?


Data, data, data.

The world of tech is laser focused on harnessing, organizing, and analyzing data. That should be music to the ears for those with the expert know-how in the industry. But it should also be considered good news for those not as adept with data management since data science jobs are growing fast—and with greater salaries.

In fact, data science and database management skills are one of the top skills managers are increasing salaries for, according to Robert Half’s 2024 Tech Salaries and Hiring Trends guide.

Data science itself is a term used across-the-board to connect areas of AI, machine learning, and statistics and can lead to careers as data scientists, analysts, engineers, and beyond. Many people in the profession consider data skills as paramount to the future.

While not everyone necessarily needs to become a data scientist, everyone needs to be knowledgeable of its data and its pitfalls, says Jignesh Patel, professor in the computer science department at Carnegie Mellon University and co-founder of DataChat—a generative AI platform.

“The study of data science is important because it imparts a systematic way to discover facts and identify falsehoods in a digitized society. Collectively, we are becoming more data-driven, not just in businesses, but in our day-to-day lives,” Patel tells Fortune

So, for both sides of the coin—those who are and are not interested in pursuing a career in data science—what are the top skills in the data science field? 

What skills do you need to succeed in data science?

To be successful in data science, you must have a strong knowledge of math and computer science—in addition to soft skills centered around working constructively as part of a team. 

More specifically, data scientists’ core must center around algorithms, code patterns, and interactions between code modules, Patel says.

“The foundational hard skill for a data scientist is statistics. How we apply statistics in data science is changing in dramatic ways thanks to automation and AI, but a foundation in statistics—and math—is critical for discovering facts,” he notes.

It also remains fact in the data science industry that AI is revolutionizing everyday tasks. In particular, the tech is increasingly able to assist experts in programming activities. But having the knowledge of popular languages remains a critical skill.

Some of the hard skills center to data science include:

  • Subject-matter expertise: Statistics, mathematics, computer science, cloud computing, networking, AI, machine learning, deep learning
  • Programming languages: Python, SQL, R, C++
  • Platform knowledge: AWS, Azure, Hive

Similar to the skills needed in data analytics, data science experts must have many common soft skills as often must across teams to use data toward business goals. These may include:

  • Communication
  • Critical thinking
  • Teamwork
  • Curiosity
  • Desire to learn
  • Business acumen
  • Problem-solving mindset

But, Patel believes the most important soft skill is being able to debate and openly critique work. 

“You must learn to give and take feedback, which is a surprisingly rare skill. In businesses, the data scientists who rise to the top are deeply technical and skilled at navigating human processes—relationships, politics, approval flows, etc.—to create business artifacts of value,” he says.

Where can you gain data science skills?

Luckily, it is becoming more and more difficult to find post-secondary schools across the U.S. who do not offer any data science program or classes. So, if college is on your horizon, that may be the best way to first gain exposure. Relevant courses are likely to be hosted in the computer science, statistics, and/or mathematics departments. 

For those who know data science is truly for them, majoring or minoring in the subject as an undergraduate—or pursuing an advanced degree is very achievable. For master’s degree programs in particular, many options are now available online–which can provide added flexibility and affordability for those who need it. (Fortune ranks the best in-person master’s in data science as well as online master’s in data science). 

Another practical way to upskill and reskill in the data science space is via a bootcamp. They may offer a quick and affordable way to learn the necessary skills, especially the more hard, tech-based ones. For those who don’t know where to start, Fortune has made that process a little easier by ranking the best data science and analytics bootcamps. Some programs are notably tied to universities, which could provide an advantage in the name-recognition category. 

Similarly, some universities and companies offer data science certification programs that range in terms of price, length, and platform. IBM and Harvard are two examples.   

Fortune’s guide on how to get a job in data science as well as details about entry-level data science jobs may also be great resources for more information.

Patel’s overall advice for someone wanting to get started in data science? Learn Python and SQL and take courses in mathematics, statistics, and big data technology like databases and large language models (LLMs), he says. 

“While the middleware in data science will change, those foundational areas will remain critical in the foreseeable future. Fundamentally, data science is a way to get to the truth, evolved for our digital environment,” Patel says. “Machines alone can’t handle that responsibility.”


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