Key steps to become a data scientist without experience

When it comes to a high-growth, high-demand career option with excellent growth prospects, data science is one of the first options that comes to mind. It uses technology to extract useful insights from huge piles of data with companies of all sizes in all sectors, and accordingly offers strategic guidance. It is a high-flying career option that pays well and offers many job openings every year.

What does a data scientist do?

Given the huge amounts of data being generated on a daily basis, companies are looking to turn it into information and optimize their strategies. But this is not something just anyone can do, and requires a dedicated data scientist to be a part of key decisions.

A data scientist collects, cleans, and analyses huge amounts of data. For this, (s)he maintains dashboards and databases, solves problems and runs experiments by interpreting data, builds algorithms, and presents the findings to stakeholders in a form they can comprehend and appreciate.

Is data science difficult?

This depends on the interests and background of the person. The role is for someone who enjoys working with data and numbers. Knowledge of machine learning or software engineering is not as important as for data engineers, but coding is required to build predictive models. The learning curve is steep, as the problems are tough, and a lot of domain knowledge and technical expertise are required.

What qualifications are required for data science?

Many data science professionals have several massive open online courses (MOOCs), advanced degrees, and other fancy buzzwords on their profiles. This should not discourage aspirants as, at its heart, data science looks at solving an actual business problem by making sense of disorganized data.

A degree is not a compulsion for a career in data science — be it a master’s degree or even a bachelor’s degree. They may be listed among the desired qualifications in job postings, but given the massive demand for data science professionals, companies are open to considering candidates who have non-traditional qualifications such as an online data science certification, and/or can show they have the requisite skills for the role at hand.

Apple, Google, and IBM in fact no longer ask for college degrees. Therefore, a candidate looking to break into the field could just pick up a Senior data scientist certification and/or other online courses and certification programs. (S)he could also self-learn through online courses and modules.

What steps must a candidate take to become a data scientist without experience?

Here are the specific actions to take:

· Self-assessment: The candidate must evaluate why a company should want to hire him/her even if (s)he does not have experience. Aspects to consider are why or why not a company would hire, what skills are known or must be learned, and a strong grasp on industry trends such as in-demand roles and languages.

· Key skills to master: The candidate must know programming languages such as Python and R, mathematics, and possibly Java, SAS, and SQL at a later stage. Soft skills such as communication, teamwork, and visualization are important too. Some of these are covered by popular online data science certifications.

· Practice: It is important to test the learning against real-time problem statements to understand what has truly been learnt. This experience helps the candidate become more confident. Hackathons are a great way to get practice. Along with this, the person can also apply the skillsets in the real world through professional practical experience, and get real-time feedback on the same. It helps to have work samples on GitHub, LinkedIn, or personal websites.

· Accepting reality: Given the criticality of the assignment and the high pay packages, nobody would want to pay for it unless the person is proven capable of dealing with complex problems. A ‘data scientist’ designation may not even be given at the start, though it becomes likely as the person learns more.

· Connect with leaders: Existing domain masters are great sources of valuable advice about the field. Along with this, it is advised to focus on networking with other professionals at domain or industry events.