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Data is now being discussed in classrooms and conference rooms around the world. And while there are seemingly endless ways to use and discuss data, especially in business environments, only a limited number of people have the necessary skills to effectively collect, organize, and analyze data. It is being
luck has taken an intensive dive into the world of data science, including important skills, how to find a job, and where to hire entry-level data scientists. We will also discuss in detail the differences between data science and, for example, data analysis and computer science, as it is closely related to other fields of study.
However, when it comes to triggering educational programs, the decision-making process can still be a challenge, especially given the vast number of services. luck wants to take some of the stress away by providing you with a ranking of the best master’s degrees in data science programs in 2024.
In preparation for the announcement of the new rankings, luck We sat down with two experts from top technology companies who have been interacting with data for decades to discuss the entire data science education ecosystem.
- Jimmy Priestas: Global Managing Director, Data & AI – Cloud Ecosystem Lead, Accenture
- Courtney Totten: Director of Data Skills and Academic Programs, Tableau
How to effectively provide guidance to those who want to pursue a career in data science by asking questions that focus on the importance of data, typical data science skills, and how those skills are evaluated. We wanted to help you understand it better. Neither expert was directly involved in ranking the programs.
“Data is the driving force”
There is no doubt that technological change is forcing companies to rethink their strategies. But Priestas says generative AI and its intersection with data is creating a situation where the world is trying to address the human-machine relationship.
Cloud, data, and AI are key aspects that businesses need to leverage to reinvest in every part of their enterprise.
“At Accenture, we believe the cloud is the enabler, data is the driver, and AI is the differentiator, enabling businesses to realize entirely new ways of working, optimizing operations, and accelerating growth,” Priestas said. luck.
For these reasons, the demand for AI is greater than ever, he added.
In terms of skills, it is important to have knowledge of basic mathematical and statistical concepts. Because they underpin data science as a whole. Additionally, proficiency in Python. One way to learn and showcase some of your key skills through cloud certifications from Google, Azure, Amazon, and Oracle.
Experience in data wrangling, data visualization and machine learning is also important, he adds.
“One of the most important things I look for in this program, as well as in applicants, is how students have the opportunity to apply their skills to real-world problems through internships, practicums, and personal projects. Should I look for it,” Priestas said. luck.
Being able to build bridges between business and technology is also important for data scientists, he says. That’s why it’s especially important for students and professionals to gain real-world experience.
The core of data science: “Storytelling”
According to Totten, every citizen in the world needs some form of data education.
“To drive business decisions, you not only need people who can understand the data, you need to have the data. And you need a platform that helps you see the data.” Totten says Mr. luck.
Tableau itself is one of the most popular data wrangling platforms (especially used by data analysts). However, different industries may use different software, so it may be beneficial for students to learn a variety of programs.
From a skills perspective, she says it’s important to have effective communication and curiosity skills, along with knowledge of programming languages such as Python and SQL.
For those starting out in data science, the real key to the field is to do your best to understand the entire cycle of knowing your data and being able to actually analyze it and formulate a story. says Totten.
“I think it’s very difficult to be incredibly successful as a data scientist if you’re not fundamentally interested in storytelling,” Totten says.
She points out that the field has always been about more than just numbers and statistical models.
“It’s important to really understand how to ask the right questions and how to create the right kinds of visualizations and tell stories to drive decision-making,” says Totten. “After all, that’s why we bring data scientists into our organizations.”
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