How To Become A Data Scientist

Are you a student, intern, or recent graduate interested in learning what it takes to become a data scientist? Check out this comprehensive guide to the skills, education, and knowledge you’ll need!

What Is a Data Scientist?

A data scientist is a professional employed to analyze and interpret complicated digital data, like search engine statistics, with the goal of assisting an organization in its decision-making. If you’ve crunched the data and you’re considering becoming a data scientist, congratulations! You’ve already taken the first step toward achieving an incredibly fulfilling and stimulating career.

Similar Job Titles

Data science is basically the collection and analysis of various forms of data with the purpose of translating that analysis into high-tech ideas that generate profit for organizations.

If you’re considering a career in data science, then it’s safe to say that you’re interested in statistics and data analysis. If that’s the case, a career as a financial analyst or an accountant may also be worth considering. However, if collecting and analyzing staggering amounts of unruly, unfiltered data and converting it into something useful intrigues you, then learning how to become a data scientist may be the right choice. In that case, an education is the best place to start.

Relevant Education Needed

There’s no use dancing around the fact that a career in data science requires a lot of math. Probability, statistics, and linear algebra are utilized extensively and will serve as the foundation for any and all future learning within the field and in your career.

Students working toward a career as a data scientist should obviously consider choosing a school that offers a degree in data science. If that’s not a feasible option, however, then students should take as many classes in the field of data science and analytics as possible. Students should also develop a firm understanding of programming and how it relates to data science, specifically development environments such as “Python” and “R,” as well as data analysis, data visualization, and machine learning.

Expected Skills

There are a number of skills that all data scientists will be expected to possess. Some of the required skills can be learned through your own study, while others can only be learned in the classroom or on the job.

• Text Analytics: Extrapolating business insights through the analysis of unstructured data.
• Machine Learning: Utilizing statistical methodology to create computer systems with the ability to improve at a task exponentially, or “learn.”
• Pattern Recognition: A function of machine learning dedicated to the recognition of data patterns and regularities.
• Deep Learning: A method of machine learning utilized in formulating data models.
• Data Visualization: The restructuring of data into images, graphs, and models for deeper analysis.
• Data Preparation: Converting data from one format to another.

Hard Skills

Hard skills are defined as tangible skills that can be quantified. The ability to type, speak French, or run Microsoft Excel are considered hard skills because you can either successfully type, speak French, and run Excel, or you cannot. The hard skills in the field of data science can generally be classified into two groups: computer science and analytics.

Computer Science

The computer science skills expected of a data scientist include a full understanding of statistical programming language software (“R” and “Python”) as well as a fluency in database querying language (SQL) and analytics software like SAS.

Analytics

A full understanding of statistics and linear algebra is essential for every data scientist. Students looking to excel in the field of data science should have experience with statistical tests and pattern recognition, likelihood estimators, and the algorithms of multifunction linear algebra. These skills are critical in machine learning and considered very important to all organizations that utilize data or are data-driven.

Soft Skills

Soft skills are skills that are less quantifiable and more open to interpretation, such as “works well with others” and “effort.” Successful data scientists have the following skills in spades and utilize them on a daily basis.

• Communication Skills: As in any professional position, the ability to communicate is critical to the successful data scientist. Data scientists must have the communication skills to work well with colleagues, but also to communicate with professionals who are not necessarily fluent in the complicated jargon of programming and SQL language. Data scientists who understand their trade well enough to communicate about it to others who don’t understand data science are a valued asset to any data-driven organization.
• Business Insight: Data scientists need to fully understand whichever industry they find themselves employed in. This means having the ability to discern problems that are business critical from those that are not.
• Think Outside The Box: One of the greatest skills a student can acquire on their way to becoming a data scientist is the ability to think outside the box when it comes to data. Data scientists rarely experience a shortage of business data to analyze, and data-driven organizations are always looking for new ways to leverage this information. A student who shows they can think outside the box will be seen as a valuable potential asset during the interview process.

Knowledge

Don’t forget that a successful data scientist is always learning. They stay on top of the latest programming and data trends, and they never let their mathematical and statistical analysis skills get rusty. In fact, it’s very common for data scientists to continue their education and achieve advanced degrees.

Data science requires a keen intellect, an adept hand at programming, and an eye for patterns. If you’ve checked the data and you still feel that a career in data science is right for you, then the only thing left to do is hit the books, power up the computer, and break out the spreadsheets!

If you’re preparing for interviews be sure to check out our data science interview questions and answers.