By Kelly Van Roy, S2DS Community Co-ordinator

Data Science has been a trending career for almost a decade now and is seeing a resurgence of interest with so much hype around the latest technologies. It’s no wonder with ChatGPT becoming a household name and Google releasing their newest contender Gemini. The government estimates that UK businesses are struggling to recruit for specialist data roles, and according to the U.S. Bureau of Labor Statistics, the employment rate for data scientists will grow by 35% from 2022 to 2032. With so much demand and new innovation, how do you know where to start?

Despite the flashy trends, like any beginner, aspiring data scientists need to start with the fundamentals. At its core, Data Science is all about extracting meaningful insights from data. This relies on learning familiar statistical techniques, like hypothesis testing and regression analysis. With a foundation in stats, you begin to unlock the diverse toolbox of data science techniques for any data you encounter.

Mastering a coding language like Python is another essential starting point. Python’s intuitive nature and widespread industry use make it an ideal choice. There are dozens of Python courses out there to support you, from beginner-friendly apps like Mimo to Coursera courses such as “Python for Everybody”. Once your coding skills are up to par, delve into introductory Data Science. Bootcamps that provide combined coding and introductory DS principles can be costly, so remember that there are many free learning options offering the same valuable knowledge to get you started on your journey.

Don’t just stop at theory – gain some practical experience too. Project work is a fantastic way to apply the skills you’ve learned to real-world scenarios. Kaggle offers a wealth of free datasets for independent projects, or you can dive into their collaborative competitions to test and advance your skills. On GitHub you can explore open-source data science projects and learn from their well-documented code to contribute to existing projects or find inspiration for your own real-world data challenges.

So far, this roadmap is for those who are starting data science as complete beginners. But what if you already have coding skills and data science experience from your time in academia? While analytical MSc’s and PhDs may use similar techniques in their academic research, commercial data science demands real-world expertise. The gap lies in lack of commercial experience, not technical skills.

This is where practical, hands-on experience shines. Platforms like Kaggle and bootcamps can bridge the gap, equipping you with the commercial know-how needed to compete and thrive as a data scientist. This is what Kevin Prinsloo chose to do. Kevin did his MSc in Cognitive Neuroscience (2012-2013) at the University of York before attaining his PhD. During his postdoc, he decided to gain data science experience through the Science to Data Science (S2DS) programme, our 5-week remote bootcamp that focuses on team-based project work with real commercial projects. He says,

“Participating in the Science to Data Science (S2DS) fellowship was a pivotal moment in my career trajectory. As a postdoctoral researcher, I was keen to explore opportunities outside academia, and S2DS provided the perfect platform. Working on a real-world industry project, I was able to harness my academic expertise in natural language processing and machine learning. The experience not only boosted my confidence but also distinguished me from other candidates transitioning into
industry roles. S2DS was instrumental in showcasing how my academic skills could be translated into impactful industry solutions. Now, as a Data Science Consultant at Blenheim Chalcot, my role synergizes my academic background in cognitive neuroscience with the practical exposure I gained during the S2DS fellowship… My work spans from enhancing educational technologies to automating healthcare processes. Leveraging tools like the Azure platform offering and various AI tools, I blend technological sophistication with a nuanced understanding of human cognition and behaviour, creating impactful
solutions that address complex business challenges and cater to human-centric needs.”

Kevin’s journey is only one path into data science, and your ideal route will depend on your
interests. Remember, data science isn’t a rigid roadmap, it’s a powerful toolkit of techniques
anyone can acquire and apply to virtually any industry. In our increasingly data-hungry
world, the possibilities are truly endless.

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Bio:
Kelly Van Roy is the Community Co-ordinator of the Science to Data Science (S2DS)
programme with Pivigo, Ltd. She oversees all applicants and programme scheduling, making
the experience as fun and empowering as possible.