When it comes to data science, there’s always more learning to be done. As our technological capabilities change, so do the systems and processes associated with them. This constant evolution is what makes data science such a fascinating subject.
It also means that as a data science practitioner, you need to be constantly improving and expanding on your skillset. Dedicating time to this is essential, whether you’re just starting out on your career path, or already hold years of professional experience.
Luckily, there are an array of ways to build on your knowledge.
Practice your programming languages
Increasing your proficiency in Python and R is always a good investment of your time. You will see both languages being mentioned frequently in data science roles. Though Python and R have a significant amount of overlap in terms of their capabilities, learning both could open up far more opportunities for you when seeking work.
Naturally, being adept at one will always be preferential to only partially knowing both. However, after learning your first choice, understanding the second should be noticeably simpler. Each come with their own strengths – research these and decide which makes the most sense for your career ambitions before getting started.
Task yourself with new projects
Thanks to the resources and communities being built online, there are a wide variety of ways to advance your abilities. Groups on LinkedIn, Facebook, Reddit and more will often feature projects you can assist with – or at the very least, ideas for projects you can take on yourself.
Joining an active data science community has its own benefits as well. Immersing yourself in the discussions and materials being shared can provide insights and experiences that you might not gain exposure to otherwise. Plus, seeing others analyse and explain their processes could help you develop your own communication skills for interviews and company presentations.
Home in on a specialty
By studying data science, you open yourself up to several career routes – which we’ve outlined in our Data Science career guide. Since there are many industries utilising data science, identify one you feel passionate about. Once you do this, you can study the more unique requirements of working in that field, which will be useful when applying for jobs.
Having a robust understanding of an industry (or industries) allows you to more smoothly acclimate into a new working environment, while remaining flexible enough to avoid feeling constrained to a particularly niche area.
Looking to get into data science as a career? Discover our data science career guide:
Work on your presentation
An essential aspect of being a data science practitioner is the ability to communicate your findings with others – both visually and verbally. Whether this occurs in a one-on-one setting, a small group, or a large meeting, your work is only truly effective when it can be understood.
Remember, the professionals you collaborate with will come from different backgrounds, with varying levels of understanding around what you do. It’s up to you to make your research accessible to them. When working on your own projects, think about how you might adapt your presentation style to suit different audiences. What visualisations make the most sense? Is there a clear connect between your findings and their work? These are all valuable questions to consider.
Take an online data science course
Of course, a surefire way to develop your data science skills is to invest in an accredited course. Our flexible, tutor-led online courses were developed by our renowned data science team at the University of Southampton – one of the top 100 universities in the world.
Along with enhancing your theoretical and practical know-how, our courses will help you hone the collaborative and communicative abilities needed to succeed in a professional setting. You can also supplement your learning with additional tutorials around Python and statistics.
Ready to advance your data science capabilities? Our courses are fully online and completed in six-weeks: