Jumpstart Your Career In Data Science


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Data Science has been ranking consistently high among the top career choices for new graduates and experienced professionals alike. Data Science, as a practice, has benefited immensely by being in the thick of exponential trends that companies across industries are going through right now, including increased ubiquity of data, advancements in machine learning algorithms, and improvements in technology.

Thus, it is not surprising that several young and experienced professionals are interested in a career in Data Science. Here are few steps that can help you jumpstart a career in the field.

Step 1: Develop a data ninja growth mindset

Data Science offers no single, best answer to a problem statement. The solution is not in the data, but often it’s in the mind of the data scientist. So, first develop a data ninja growth mindset. What does this mean?

a) Being inquisitive and having a learner mindset: Data Science is an evolving space; ask a lot of questions about the problem and data b) Being humble: You don’t know all the answers upfront and you may be wrong

c) Being able to work hard: Spend more time cleaning and preparing the data first than developing advanced algorithms

d) Being resilient and iterating solutions: Your first model is often wrong. Be willing to scratch-off your initial answers and iterate quickly to better solutions

Step 2: Self-learning

There are several self-learning opportunities available online, both paid and free. Check out Data Science introduction courses available across platforms. Start with some basic courses on Statistics and Probabilities, before proceeding to basic/advanced introductions to Data Science. Pick up a few languages and learn them thoroughly.

Step 3: Get credentials if you have the time and resources

While self-learning is a great first step to start your learning journey, in some cases it may be worth investing in accredited Data Science courses. Most reputed Engineering and Management schools offer such courses that will help you in two ways, including getting a Data Science degree/certification from a reputed institute, and learning from the best professors and industry specialists.

Step 4: Do exercises and get yourself noticed

While as part of self-learning, you would have done some exercises with sample datasets, now would be a good time to go for online Data Science competitions. Organisations continue to conduct Hackathon programmes to identify talent. The problems posed here are usually not exactly like real-world problems in terms of size and scope, but this would give you an opportunity to get hands-on experience in solving a problem. The intention is not to merely to win, but to learn how to solve a unique problem within a time constraint, and benchmark yourself against other practitioners.

Step 5: Ask for internships, internal work assignments

If you are a graduate seeking a career in Data Science, look for companies that offer an internship to final year students. As an intern, you may be asked to do some foundational work in Data Science that may include gathering, cleaning, and preparing the data than building advanced machine learning models. Invest time in this crucial stage and build strong relationships with stakeholders in the organisation.

If you are already working in an organisation, ask for internal work assignments or stretch projects with the Data Science team to advance your career path.

Step 6: Acing the interview for your first Data Science job

If you follow these steps, no special prep will be required for your job interview other than learning about the company and what they do. In the interview, showcase the effort you have put in, refer to your credentials and highlight the problems you have worked on. Companies like recruiting talent that have invested upfront in the areas they are hiring for.

Once you join the organisation, look for a role that would allow you to work on real business problems for internal or external stakeholders. Be curious and ask a lot of questions to fully understand this Data Science value chain. Start with the big picture. Prioritise understanding the organisation’s industry and domain, how value is generated and how Data Science can enable and transform the organisation.

Source: www.educationtimes.com

Also read: 20 Ways To Use Social Media To Advance Your Career

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