Driven by the hype over the field of data science — and a potentially fat paycheck — working professionals across industries have been looking to reorient their career trajectories. The lockdown, enforced to slow the spread of coronavirus, has offered them the opportunity to upskill themselves to transition into a career in data science. This transition, however, will not be smooth and would require additional efforts to make that final leap.
Instead of randomly learning whatever you come across, aspirants need to devise an effective strategy that can help them reap more benefits and avoid burnouts. Since data science is relatively new, universities did not offer lessons that enabled students to obtain machine learning skills. As a result, most data scientists have been successful through self-study. If you aspire to become like them, this is the right time to take your dream seriously and become a data scientist.
Learn The Basics Of Data Science
Due to the crisis, various ed-tech organisations have made their data science courses free to help aspirants upskill and eliminate the talent gap problem in the space. However, you should not focus on learning everything that the courses offer. First, build your foundation by learning statistics and mathematics. Then move on to data analysis and basic machine learning techniques.
“I know that deep learning and AI are in trend. But, it would be a strict no from my side if the interviewee is not strong on the basics,” said Rahul Agrawal, Data Scientist of Walmart Labs. Organisations seek candidates who have a strong foundation, not someone who can write fancy machine learning algorithms. But, you will still have to find a specialisation within data science to master, which can help you deliver value for the organisation.
Choose A Specialisation
Data science is vast, and if you start paying attention to every development in the space, you may not be able to dive deep. While there are numerous ways in which you can find a specialisation, the most natural approach is to understand how data science can be implemented in your current professional work.
For instance, if your job involves documentation, you should start gaining an in-depth understanding of natural language processing. Go back to the free courses and take advanced courses on the specialisation of your choice. “Being specific is necessary to succeed. Aspirants should pick up a particular class of problems within a domain and apply data science techniques to solve them,” said Shashank Shekar, Head of Data Science at RadiusAI.
Implement The Learning In Your Job
On obtaining the required knowledge, try to deploy the data science techniques in your workflows. Randomly applying ML techniques for the sake of showcasing during interviews will not add any value. Identify a problem in your job that can be carried out effectively with data science. This will demonstrate your capability and invoke interest among recruiters.
Attend AI Conferences
Attending AI conferences such as plugin will not only help you understand what is happening in the ever-changing data science landscape, but also give you an idea around where the industry is heading. This will enable you to plan accordingly and be aware of trends in the market. Besides, you also get an opportunity to expand your network, which can help you in obtaining a reference when looking for jobs. Undoubtedly, an internal switch should be the first choice, but having access to jobs outside your firms through referrals can bring better opportunities too.
Get A Mentor
Having mentors to help, especially professionals from the same field, can assist you in shaping your career. For this, you can obtain guidance through the AIM mentoring circle. Being an enabler for the data science market, AIM has an exceptional network of data science leaders and influencers who can suggest various approaches to becoming successful data scientists.
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