Fortune has dived intensively into the world of data science, including the important skills, how to get a job, and the places hiring entry-level data scientists. And, since it relates closely to other fields of study, we’ve gone into the differences between data science and data analytics and computer science, for example.

However, when it comes down to pulling the trigger on an education program, the decision-making process can remain a challenge, especially considering the vast number of offerings. Fortune is hoping to alleviate some of stress by providing a ranking of the best master’s in data science programs for 2024.

In preparation for the release of our new ranking, Fortune sat down with two experts at top tech companies who have been interacting with data for decades to discuss the entire data science educational ecosystem:

• Jimmy Priestas: Global Managing Director, Data & AI – Cloud Ecosystems Lead, Accenture
• Courtney Totten: Director of Data Skills and Academic Programs, Tableau

By asking centered around the importance of data, the quintessential data science skills, and how they would be evaluated, we hoped to gain a better sense of how to effectively provide guidance to those hoping to pursue a career in data science. Neither expert was involved in the ranking of any programs directly.

‘Data is the driver’

It’s no question that changes in technology are causing businesses to rethink their strategies. But, Priestas says generative AI and its intersection with data is creating situations where the world is trying to address the relationship between humans and machines.

Cloud, data, and AI are the key aspects companies must utilize to reinvent every part of their enterprise.

“At Accenture, we believe Cloud is the enabler, data is the driver, and AI is the differentiator that will help companies unlock entirely new ways of working, optimizing operations, and accelerating growth,” Priestas tells Fortune. 

For these reasons, he adds, the demand for AI has never been greater than before.

In terms of skills, having knowledge of foundational mathematical and statistical concepts is critical, Priestas says, because they underpin the entirety of data science. Moreover, proficiency in Python. One way to learn—and showcase—some of the important skills through a cloud certification offered through Google, Azure, Amazon, and Oracle.

Experience with data wrangling, data visualization, and machine learning are also crucial, he adds.

“One of the most important things that I look for in the programs as well as even applicants is how the students should seek opportunities to apply their skills in real-world problems through internships, apprenticeships, personal projects,” Priestas tells Fortune.

Being able to build the bridge between business and tech is also important for data scientists, he says—which is why it is especially crucial for students and professionals to gain real-world experience.

The heart of data science: ‘storytelling’

Every citizen of the world needs to have some sort of data education, according to Totten. 

“In order to drive business decisions, you need to have not only people that understand data, you have to have the data. And you have to have the platforms that help you look at the data,” Totten tells Fortune. 

Tableau itself is one of the most popular data wrangling platforms (and could be argued that it’s used more by data analysts in particular). But, depending on the business, they may use a different type of software, so it may be beneficial for students to learn a variety of programs.

From a skills standpoint, having effective communication and curiosity skills alongside knowing programming languages like Python and SQL is important, she says.

For those starting out in data science, doing your best to grasp the full cycle—from getting to know the data and truly being able to analyze it to formulate a story— is the true key to the field, Totten says.

“If you’re not interested at the heart of it in storytelling, I think it will be really challenging for somebody to be incredibly successful as a data scientist,” Totten says. 

The field is always more than just numbers and statistical models, she notes.

“It’s about really understanding how to ask the right questions and how to create the right types of visualizations and tell the stories to help drive the decisions,” Totten says. “Because at the end of the day, that is why we bring data scientists into our organizations.”