How to Become a Data Scientist

It takes 4 to 5 years to become a data scientist. However, there are so many different paths to get there, so the time it can take to become a data scientist will vary.

Those who go the university route, for example, can become data scientists in 3 to 4 years, or the time it takes them to earn a bachelor's degree. However, most data scientists, over 75% to be exact, have at least a master's in data science. This takes an additional 1 to 2 years to complete.

Of those, another 38% of data scientists have a Ph.D. in data science or a related area like mathematics or computer science. Earning a Ph.D. can take a total of 7 to 8 years to achieve.

Not all routes to becoming a data scientist, however, mean paying for expensive graduate school. In fact, over 25% of data scientists do not have a graduate degree. They learned the skills they needed online and through on-the-job experience.

That's not to say that being self-taught is any easier than attending university. Self-teaching requires extreme discipline, as well as thorough research to make sure you're teaching yourself the right skills.

Not to mention, data science is a quickly evolving world to continually adapt the curriculum to keep up with new languages or procedures.

Yes, data scientist is a good career. Data scientists are paid well, have high job satisfaction, good job growth, and can work in a variety of industries.

Data science is one of the most highly paid jobs in any industry. The average data scientist, for example, makes an average of $116,100 per year.

For this reason, It's one of the most highly sought after jobs thanks to the abundance of data science positions available, the variety of industries open to them, and a lucrative pay-scale.

There is a lot of versatility in the types of industries that data scientists can work in. Data scientists are in particular demand in fields such as healthcare, banking, consultancy services, and e-commerce industries.

Data scientists experience a high level of job satisfaction, and one reason for this is that their role in many companies is critical to the business. They allow companies to make smarter business decisions. Companies rely on data scientists and use their expertise to provide better results to their clients.

The qualifications for a data scientist include the right combination of educational credentials, programing skills, statistics, and business acumen.

After all, a data scientist is part mathematician, part computer scientist, and part business strategist, and as such, they must have expertise in several different disciplines at once.

This complex skillset means that data scientists must consistently have one foot in the information technology sector and another planted firmly in the business world.

If available, the most sought-after majors for data science are statistics, computer science, information technologies, mathematics, or data science.

Educational credentials have an impact on your ability to get a job working as a data scientist. However, it's not the only path; over 75% of data scientists have at least a master's in data science, and another 38% have a Ph.D. in a related area like mathematics or computer science.

However, if you have a bachelor's degree in a related area and a good understanding of computers and mathematics, there are other options.

There are enough resources online to teach you most of the core concepts that you need to master. In addition, there are many online programs specifically for data science that include online courses, materials, and certifications.

Overall, a data scientist should have a large range of computer-related abilities. including:

• Mastering a Programming Language (R or Python)

• Proficient in applied mathematics (LA, Stats, Prob)

• Understand Machine Learning and Deep Learning

• Hands-on work experience (e.g., internship, online competition)

• Ability to build end-to-end projects with competitive data science

In addition, a data scientist must be proficient in mathematics, numeracy, and have a deep understanding of statistical theory. Plus, knowledge of SAS and Other Analytical Tools is important. Finally, having business acumen and good communication skills play a big part in being a good data scientist.