How to Become a Data Scientist

. 6 min read

Before we get into the 'how', let's look at the 'why'

Glassdoor’s ‘50 best jobs in America for the year 2019’ has Data Scientist topping the list yet again for the 4th year in a row. With a job score of 4.7 and job satisfaction rating of 4.3, Data Scientist has a median base salary of $108,000 and claims 6,510 open positions in the United States.

The jobs that are listed in Glassdoor’s survey as based on three main determinants: earning potential based on the annual median base salary, job satisfaction rating and the availability of job openings. Data Scientist staying up the ladder for 4 consecutive years is proof enough of the demand and relevance the role has in the era of Big Data.

#1 Job by median base salary, job openings and job satisfaction

The need and significance of Data Scientists

Every day, 2.5 quintillion bytes of data are created. From Google search to Facebook feeds and Amazon buys to Netflix subscriptions, a huge amount of data is generated daily that needs constant and effective analysis and management.

Emerging technologies like automation, data science, machine learning, and other forms of Artificial Intelligence have become inevitable for every organization that handles multiple petabytes of data in one form or the other. As industries wake up to the irrefutable potential of data analytics, the new breed of data professionals is born. These ninjas of computing have become indispensable to the corporate world, in order to decode the complex matrix of huge data and shift the growth trajectory upward.

Every industry, area of business and segment of consumers requires data scientists to gather, organize, and analyze data. From developing and managing machine learning algorithms to data-driven decision-making within the company, organisations are leveraging the wisdom of data engineers and Chief Data Officers to make a huge impact on their businesses.

According to the International Data Corporation (IDC), the Big Data analytics market — which is relatively a small segment of the larger Data Science and Analytics (DSA) market — will grow to over $187 billion by the year 2019, simultaneously raising the need and demand for more data specialists.

Machine Learning Engineers, Data Scientists and Big Data Engineers rank high as the top emerging jobs on LinkedIn.  Job opportunities in this field are predicted to grow by 11 percent by the end of 2021.

The global scenario of data mining

As per a research study conducted by IBM, in 2015, there were already over 2,350,000 job listings for core Data Science and Analytics (DSA) in the United States. And the number of DSA job listings, by the year 2020 is predicted to grow by nearly 364,000 listings, and 2,720,000 openings which reinforce the prominent role of data specialists in the near future.

Succeeding the clan of data scientists and analytics is yet another category of data navigators: the data-driven decision-makers or data-enabled marketing managers. These hybrid data advisers, who have their organizations in their fist, comprises one-third of the data savvy professional job market with a projected increase of 110,000 positions by 2020.

Job roles

Data Scientist isn’t a single job that accommodates everything data but is an amalgamation of various job responsibilities from analysis, development, research and decision-making. The job profile can vary according to the existing requirements in each organization.

For example, a data scientist might be responsible for analyzing customer information and creating effective marketing campaigns in service industry. Yet another data science professional could be involved in helping out a retail chain come up with an attractive price range for their products. In the initial phase, you might begin by uploading numbers and data into the system or writing code to analyze the information.

The job roles differ from Data Systems Developers and Data Scientists to Data Analytics Managers and Functional Analysts. The job profiles stretch across an undulant canvas and could vary from analyzing threat levels in Cyber Security Department to helping small startup businesses manage data to retain customers.

The work atmosphere will mainly depend on the company that you work in. You could be in a fast-paced work environment demanding or providing quick results, or you could be in an organization that is more methodical and inclined more to detailed progress. Depending on the type of data science job you are in and the nature of the organization you work for, you may find a work environment with scope for creative thinking, or you could be in a job that is designed for effective productivity.

Payscale

DSA isn’t just an in-demand job with an exciting climb, but this “sexiest job of the century” (according to the Harvard Business Review), has also got the money bags clinking! According to Forbes, Jobs that specify machine learning skills pay an average of $114,000 in the U.S. The average salary for a data scientist is more than $125,000. Data scientist jobs, as advertised, pay an average of $105,000 and data engineering jobs, an average of $117,000.

Data Science salaries by education stream and degree

A Roadmap to become a Data Scientist

Proficiency

The many-layered path to becoming a data scientist begins, like any other technology, with acquiring proficiency in the subject. This multi-disciplinary science has coding as its spine with a whole lot of skills like quantitative problem solving, experimentation, and data handling adding to its core.

Data science, a relatively young field, is all about computing and technology, and accessing information from large databases. It uses code to manipulate data, and visualizes numbers in a digital format. Hence, as in any other emerging and fast-growing job, a solid foundation in software engineering is an asset to enter into Big Data development.

Though rooted in computer-related studies, DSA could also include areas of statistics. Both the sciences have similarities and have common goals like the application of huge data to derive at conclusions.

Data taken from payscale.com
  • An academic background in traditional sciences like computers, economics, applied maths, or physics can be foundational in pursuing higher studies in data science.
  • With its multi-disciplinary nature, data science profits from business training or human behavior management, which helps with more accurate conclusions in their work.
  • Attend a well-structured, accredited program that provides training by experienced industry professionals, along with hands-on training on real life projects and industrial projects,
  • A professional certification in Data Science can always bring in breakthroughs.

Practice

It is important to get sufficient hands-on training with your data science preparations. If you do not land up with the desired data scientist job following your graduations or certifications, try to grab as many freelance projects that you could lay your hands on. It is also beneficial to attend training programs that give you opportunities to real-time hands-on experience.

Preparation

To be a successful data scientist, one needs to have a thorough comprehension of various tools as well as programming languages.

  • Sharpen your programming skills.
  • Be adept at programming languages like Python, SQL, SAS, Hadoop etc.
  • Training and certification programs in Big Data Analytic tools, like R Programming, Tableau, XPlenty etc.,  can sharpen your skills in data connection establishment, statistics, data mapping and transformation etc.
  • Develop your understanding in relevant areas like quantitative analysis, product intuition and consumer behavioral patterns and their roles in data science.
  • Attend boot-camps and training programs to update your theoretical as well as practical know-how.

Promotion

Showcase yourself to the industry. Create a perfect public portfolio that you could upload on social media networks like LinkedIn. Connect with experienced professionals from the industry who could expose you to relevant communities, career programs, industry updates and at times, even great job opportunities!

Proactiveness

Be persuasive in pushing yourself up the hill. Don’t lay back waiting for that perfect breakthrough. Take initiative in finding and getting involved in freelance projects where you could try your skills at, acquire confidence in yourself and perhaps get a mention worth an opportunity, with your efforts!

Conclusion

Chiseling your skills, attitude and aptitude to enter into this era’s most glorified career path will reap its reward. Creativity, focus, organizational skills and persuasion are key attributes necessary to make your mark as a successful data professional. A keen eye for business, marketing and an innate sense of understanding people will also qualify as a powerful tool in a data science career.

A basic know-how or academic back-up in business, psychology, political science, or liberal arts, though seemingly insignificant, could complement a data science degree.

Check out GreyAtom's Data Science Masters Program, if you are interested in a career as a data scientist. It a program created in partnership with the industry, and therefore is geared towards getting your foot in the door of prestigious companies.



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