What makes a successful Data Scientist?

The demand for quality data scientists is on the rise. If you have been considering this career path, the odds right now are heavily in your favor
It takes a mix of hard and soft skills to make a quality data scientist. These include mathematical and statistical skills, machine learning and artificial intelligence skills, curiosity, creativity, and grit.

“We are living in a time of scientific innovation, unprecedented technology, and unlimited growth prospects. These new technologies such as Artificial Intelligence and the Internet of Things, if used optimally, can spare the world from many disadvantages and can bring to the world enormous benefits.” - HRH Prince Mohammed bin Salman bin Abdulaziz Al Saud

As can be gleaned from the words of his Highness, we are living in times when Saudi Arabia is heavily investing in technology across all sectors. At the forefront of this investment is SDAIA, Saudi Data and AI Authority. Launched in 2019, SDAIA’s purpose is to position the Kingdom as a global leader in the elite league of data-driven economies. The authority aims to unlock the value of data as a national asset to realize Vision 2030's aspirations by setting the national data and AI strategy and overseeing its execution through harmonized data policies, data analytics and insights capabilities, and continuous data and AI innovations.

In light of this growing investment in data, the demand for quality data scientists is on the rise. If you have been considering this career path, the odds right now are heavily in your favor. In a research project by Endeavor mapping the Riyadh tech sector, one of the major findings was that despite Saudi Arabia having the potential to become a regional hub for tech entrepreneurship in the Middle East, one of the biggest challenges preventing faster growth is the need to hire tech talent from outside of Saudi Arabia.

But what is a data scientist and what makes a good one?

Generally speaking, a data scientist is someone who is proficient in statistics and has the ability to take a problem and create a model that can be used to solve it. They are also skilled in mathematics, computer science, and programming. It’s important to note that data scientists are not simply statisticians or programmers. Along with a deep understanding of the field and an analytical mind that is well-versed in statistics and programming, they also have an inherent curiosity to solve problems.

So what does it take to become a quality data scientist? According to experts, a mix of hard and soft skills is key: 

  • Mathematical and statistical skills: Math and statistics aren’t just about numbers. Having mathematical and statistical skills means having critical thinking, problem solving, analytical thinking, quantitative reasoning, the ability to manipulate precise and intricate ideas, and construct logical arguments and expose illogical arguments, to name a few. All integral to the work of a data scientist.  
  • Machine learning and artificial intelligence skills: Machine learning is transforming how data mining and interpretation work. Artificial intelligence helps analyze large chunks of data, easing the tasks of data scientists in an automated process. That said, as we all know very well, where there is technology, there come hiccups. Hence why a data scientist must have deep knowledge of machine learning. They must be able to evaluate machine learning and intervene to solve non-standard problems.

  • Curiosity, creativity, and grit: Being a data scientist means having the opportunity to work on a constant stream of new and challenging puzzles. It takes curiosity, creativity and persistence to be able to solve those.

Now, do you think you can be a quality data scientist? We know you do, and we have some good news for you. Keep an eye out for the next round of the Misk Data Science Program. By joining the program you can fast track your career path and equip yourself with cutting-edge skills to meet the demands of the industry of the future and the rapidly changing economy.

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