Are you a professional thinking about a career transition to Data Science? Read on!
The field of data analytics continues to attract different groups from fresh graduates to those with years of experience in other fields. These groups, on the other hand, must have different strategies for transitioning to a career in data science.
Fresh graduates may have more flexibility in allocating their time to learning or sharpening their skills and might even want to take some time off to attend bootcamps, participate in data science fellowships or self-study. However, those with work experience can make a smoother transition if, rather than quitting or taking time off, leverage their competitive advantages they gain during their time in industry. To be specific:
To Become A Data Scientist:
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DO NOT QUIT YOUR JOB!!Do not even think about it! It is rarely a good option, no matter how excited and determined you are! Not only for many of us our job is the main source of income, getting to work on projects and interacting with people have a significant impact on our well-being, help us build and expand our network and connect us to new opportunities. Let me be clear, under no circumstances do not quit your job to prepare yourslef for becoming a data scientist!!
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Read, Consistently! As trivial as it looks, the gigantic volume of blog posts, articles, books, videos, tutorials, talks, slides and presentations, online courses, … are in your service, most of them for FREE, to guide you in the direction you want to go. Use them and use them often! Use these resources to not only learn new skills but also to learn more about the differences between career paths in data science- from product analysts, business analysts, statisticians, …-, get a sense of the trends in data science and to figure out where you see yourself a fit! Read consistently: data science is a vast field and the more you read and learn, the more valuable you become for your future employer!
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Leverage Your Network i. Use your network to connect to data scientists and speak with them about their roles, experiences, projects, and a career path in analytics. ii. Use your network to connect to the opportunities you may not be aware of! Let them know you want to transition to data science and you appreciate if they can help you along the way. iii. Use your network to find roles with an overlap between your roles, responsibilites and skills and data science roles (More on this in #5)
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In Your Current Role In your current role, when it is possible, identify and commit to data analysis work. Projects that require data analysis, coding, statistical analysis, hypothesis testing, visualization, and even building Machine Learning models like linear and logistic regressions can teach you a ton about data science work. They can also help you to get a sense if you actually like these kind of tools and techniques. Remember, despite the hype, we spend a big chunk of our time cleaning, something that many of us are not excited about!!
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Your Experience Is Your Competitive Advantage And What Makes You Stand Out! A smooth way to transition to data science roles is to find a role where you can take advanatge of your current knowledge and skills and at the same time, allows you to broaden your data skills. This way you will use your competitive advantage (your skills and domain expertise coming from your current role) and in the meantime you expose yourself to learning opportunities in real-world scenarios (the data science projects you get to work on). Remember: few people may have your experience and domain knowledge that is required for jobs in a particular area. As an example, someone with a finance background has a much bigger appeal to a financial institution than someone with a background in healthcare!