Data science is one of the hottest careers in the 21st century. In today’s high-tech world, many businesses have questions that can be answered by “big data”. From companies to government institutions, there is almost an infinite amount of information which can be sorted, interpreted, or applied to a wide range of purposes. In this post, you’ll learn how to become a data scientist.
What Is a Data Scientist?
Data science is a complex field that involves many different skills. Essentially, a data scientist is someone who gathers and analyzes data with the goal of reaching a conclusion. They do this through various techniques. They may present the data in a visual context, allowing a user to look for clear patterns that would not be noticeable if the information was just presented on a spreadsheet. Data scientists also create advanced algorithms that are used to determine patterns as well as convert the data into something that can be useful.
Data scientists work in different settings, but a majority of them work in office-like settings that enable people to work in teams and collaborate on projects. Much of the work may involve uploading data into the system or writing code for programs that will analyze the information.
The pace and atmosphere of the work environment will largely depend on the company and industry you work in. You could work in a fast-paced environment that would require quick results, or you could work in an organization that values methodical, detailed progress.
How to Become a Data Scientist – Education Requirements
There are many paths to land a career in data science. It’s important to note that it’s almost impossible to launch a career in this field without a college education. At the very least, you need a four-year bachelor’s degree. However, bear in mind that 73% of professionals working in this industry have a graduate degree; 38% have a PhD. If you want an advanced leadership position, you’ll have to earn either a master’s or a doctorate.
Some schools offer data science degrees. That can provide you with the necessary skills to process and analyze data, and will involve technical information related to computers, statistics, analysis techniques, and more. It will also allow you to make decisions based on your findings.
While a degree in data science is the most obvious choice, there are also technical and computer-based degrees that can help launch your career in data science. Degrees that can help you learn data science include:
• Computer science
• Applied math
• Social science
After completing one or more of these degrees, you will likely have a broad range of skills which apply to data science. Such skills include experimentation, quantitative problem solving, coding, handling large sets of data, and so much more. The ability to understand people, business, and marketing is also important in data science.
Data Scientist Salary and Job Outlook
If you become a data scientist, you are poised to earn a high salary. Although the Bureau of Labor Statistics doesn’t’ provide salary information for data scientists as of the moment, they do have information on computer and information research scientists. According to the BLS, the 2018 median pay for people working in that field is $118,370 per year.
Glassdoor reports that the national average salary for data scientists in the United States is $117,345 per year (as of September 2019). PayScale, meanwhile, reports that the average salary for a data scientist is $91,168 per year.
Besides earning a high salary, data scientists can look forward to more job opportunities in the future. According to the BLS, the demand for computer and information research analysts is expected to grow by 16% for the period 2018 to 2028.
Now that you know how to become a data scientist, you can plan ahead to achieve your career goal. As more and more businesses rely on information to make important decisions, the need for people who can compile, organize, store, and interpret information will continue to grow. For this reason, data analysts are expected to be in high demand in the foreseeable future.