How to Become a Data Scientist

By Sierra Powell – Becoming a data scientist is a growing trend in today’s tech- and data-driven age. As more data becomes available, professionals who can collect, organize, clean, and analyze them are becoming increasingly valuable to various industries, including finance, consumer electronics, healthcare, and so on. But for aspiring data scientists fresh out of high school or those looking to transition careers, the road to a full-time job can be a long and exhausting one. Here is a roadmap that can help guide your efforts as you pursue a data scientist career.

Pursue the Right 


What you decide to pursue in college is an important first step when trying to get an early foothold on a data science career. While it’s not uncommon to find data scientists coming from a completely different undergraduate background or even without any bachelor’s degree, what you major in college can help set you up for more career opportunities and the ability to pursue postgraduate studies if you wish to. As for a data scientist candidate, a degree in IT or information technology, computer science, match, or another STEM-related field is a pragmatic option.

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Earn a Master’s Degree

Once you earn your bachelor’s degree, consider going for a master’s degree. It’s time-consuming and can bring a heavy financial burden but there are benefits to pursuing a master’s degree as well. For starters, you effectively increase your earnings potential by up to 18 percent. Another great advantage of a master’s degree is that it widens your career prospects. Some company roles, particularly ones that include managerial responsibilities, may look for a master’s degree as a minimum requirement.

Know What You Want to Specialize In

One of the main allures of becoming a data scientist is job security. As mentioned earlier, any industry that deals with data, whether it’s topographical measurements, profits, and losses, or personal information, can find utility in hiring a data scientist. Know what you want to specialize in early on. This helps you determine what minor studies, certifications, volunteer programs, and secondary skills to focus on. For example, a data scientist employed by an auto dealership may be heavily focused on consumer or marketing analytics and, subsequently, developing campaigns that can boost the dealership’s sales numbers.

Read the Right Books

There are several books out there that are not required material by your college, but these titles can help give you a competitive edge against other data science candidates. For instance, R for Data Science by Hadley Wickham and Garrett Grolemund is a good introduction to working with data in R language. With the book, you’ll learn how to clean your datasets, draw plots, and learn the best practices to do these things. Other titles of general interest include Everybody Lies by Seth Stephens-Davidowitz, Naked Statistics by Charles Wheelan, and Weapons of Math Destruction by Cathy O’Neil.

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Master a Data Science-Oriented Programming Language

Data scientists clean, sort through and analyze large chunks of data using programming languages, like Python, R, SQL, and SAS. This makes it easier to unload large datasets, automate redundant computations, and create charts and graphs that will help you present your findings to other people in a more organized format. But rather than try to learn multiple programming languages all at once, pick one and focus on that language. Beginners are highly recommended to start with Python since the language’s syntax is a lot more beginner-friendly, plus you can often build fairly complex projects and tasks without having to go through steep learning curves.

Read: 8 Programming Languages that Every Teachie Should Master

Prep For Interviews

Job interviews for tech roles are notorious for being highly technical. The process is typically stretched into multiple parts or stages and involves technical and behavioral questions. Not being prepared for either type of question can throw off even the most highly decorated graduates and professionals. Practice for your data scientist interview by speaking your answers out loud. What are the benefits and drawbacks of a linear model? Explain what a random forest is. How do you use SQL to capture all duplicates in a dataset? These are only some of the problems that your interviewer might throw at you during the interview.

In addition to the prerequisites mentioned above, you should also start developing a data scientist’s mindset. Try to approach everyday problems in a logical and organized manner.


Sierra Powellsierrapowellwriter[@]

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