Data is being discussed in classrooms and boardrooms around the world right now. Although there may be an endless number of ways to use and discuss data, only a limited number of individuals have the skills to collect, organize and analyze it effectively – especially in a business setting.
luck He dives extensively into the world of data science, including what skills are important, how to get a job, and places that hire entry-level data scientists. Since it is closely related to other fields of study, we have covered the differences between data science, data analytics and computer science, for example.
But when it comes to pulling the trigger on an educational program, the decision-making process can still be a challenge, especially given the sheer number of offers. luck It hopes to relieve some of the stress by providing a ranking of the best master's in data science programs for 2024.
In preparation for the release of our new classification, luck I sat down with two experts at top tech companies who have been interacting with data for decades to discuss the entire data science education ecosystem:
- Jimmy Prestas: Global Managing Director, Data & AI – Cloud Ecosystem Leader, Accenture
- Courtney Totten:Director of Data Skills and Academic Programs, Tableau
By asking questions centered around the importance of data, essential data science skills, and how to evaluate them, we hoped to get a better idea of how to effectively provide guidance to those hoping to pursue a career in data science. None of the experts were directly involved in rating any program.
“Data is the engine”
There is no doubt that changes in technology are causing companies to rethink their strategies. But Prestas says generative AI and its intersection with data is creating situations in which the world is trying to address the relationship between humans and machines.
Cloud, data, and AI are the key aspects that companies must leverage to reinvest every part of their organization.
“in AccentureWe believe the cloud is the enabler, data is the driver, and AI is the differentiator that will help companies unlock entirely new ways of working, improve operations, and accelerate growth. luck.
For these reasons, he adds, the demand for artificial intelligence has never been greater.
In terms of skills, knowledge of basic mathematical and statistical concepts is crucial, Prestas says, because they underpin all of data science. Furthermore, proficiency in Python. One way to learn and demonstrate some important skills is through a cloud certification offered through Google, Azure, Amazon, and Oracle.
Experience with data handling, data visualization and machine learning is also crucial, he adds.
“One of the most important things I look for in programs as well as even in applicants is how students should look for opportunities to apply their skills to real-world problems through internships, apprenticeships, and personal projects,” Prestas says. luck.
Being able to build the bridge between business and technology is also important for data scientists, which is why it's especially important for students and professionals to gain real-world experience, he says.
The heart of data science: “storytelling”
Every citizen of the world needs some kind of data education, according to Totten.
“In order to make business decisions, you don't just need people who understand the data, you have to own the data. You have to have platforms that help you look at the data,” Totten says. luck.
coloring It is in itself one of the most popular platforms for data argumentation (and arguably used the most 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 software.
From a skills perspective, having effective communication and curiosity skills along with knowledge of programming languages like Python and SQL is important, she says.
For those starting out in data science, doing your best to understand the full cycle — from getting to know the data and really being able to analyze it to craft a story — is the real key to the field, Totten says.
“If you're not intrinsically interested in telling stories, I think it's going to be very difficult for someone to be incredibly successful as a data scientist,” Totten says.
She points out that this field is always more than just numbers and statistical models.
“It's really about understanding how to ask the right questions and how to create the right kinds of visualizations and storytelling to help make decisions,” Totten says. “Because ultimately, that's why we bring data scientists into our organizations.”
