If there’s one constant in data science, it’s this: the sector never stands still.
It’s a field of expertise where perpetual growth is a certainty; an area where you’re constantly learning new things to stay at the sharp edge—and, if you’re not learning, it’s a sector that’ll swallow you whole and leave you behind like seaweed at low tide.
Consequently, this continual shapeshifting and evolution means you must work extra hard to keep your core skills—analytics and data processing—up-to-date, while learning new skills that will make you an asset to any company looking for a data scientist.
But how can you begin to take the next step when it seems your working life is already dedicated to pursuing excellence?
Well, there are a number of ways to hone your skills in the data science field, even if you’re employed full-time, but we’re going to focus on three paths towards calibrating your learning and becoming a data science superstar in next to no time. Take a look …
1. Gain a postgraduate qualification
If you’re keen to supplement your existing undergraduate degree and training with a postgraduate qualification, there are distance learning courses you can take to help you become a data science and machine learning whizz – and you can learn while maintaining your full-time role.
You can learn data science course from here, the qualification you’ll gain from them will help develop the skills you need to analyse complex data to support strategic decision making in a variety of industries.
Don’t Miss-
How is Machine Learning Important for Data Scientists?
What a Great Database Can Do for Your Business
2. Online courses
Aside from an online qualification through an accredited institution, you can take other online courses to supplement your existing skills. For instance, sites like Treehouse, Lynda and CodeSchool offer the kind of knowledge you should be absorbing.
Best of all, you can broaden your knowledge by taking online courses in other areas not necessarily related to data science. Examples include programming courses in languages like Python, C++, and .net. After all, every company is looking for a well-rounded employee with a wide skill base.
3. Find a mentor
This is a big one. If you can find a mentor who works in the data science or machine learning field, reach out to them and ask whether they’d consider mentoring you through the next phase of your career development.
For example, try connecting with programmers, statisticians and engineers. Don’t be afraid to ask questions, listen to their experiences, and, above all, try to learn from the mistakes they’ve made and the challenges they’ve faced in their data science careers.
Now it’s your turn …
Do you have any top tips for up and coming data scientists to bolster their existing skillsets? Perhaps you’re starting your own journey to the top of the data science field? What would you like experienced data scientists to share?
Whatever your story, we want to hear from you—leave a comment below.
