A select few very lucky people get their learning path just right the first time. For the rest of us, time and experience can change our paths. If you’ve already started learning data science and have been employed for some time, you might be thinking of a refresh or new skills. So how can you relearn data science and infuse new life into your career? Here are some of our best ideas for re-starting your learning path.
Take a micro-course
Mini-courses, micro-courses—these are targeted courses designed to teach you a valuable skill in just a few hours to relearn data science. Our very own AI+ training, for example, offers targeted courses in artificial intelligence disciplines, machine learning, and many other subjects.
The point of micro-courses is to reskill. You can even refresh your memory for that programming language you might have forgotten or learn a new one. You could make your move from data analyst to data engineer using a few well-placed micro-courses.
Since you already have a foundation, relearning a practical skill can be a great jumping-off point for a new era in your data science learning path.
Join a competition
Nothing helps you relearn data science like some friendly competition. Data science competitions, such as Kaggle, offer stakes and some fame if you can rise above other participants to solve the puzzle. A competition is also a chance to do something new—maybe you’ve never worked with a particular type of data set or asked that specific question. Now you have to get out of your comfort zone and draw on all your experience.
A competition can be an excellent networking opportunity and could help you gain some confidence in a new area. Plus, doing well in these challenges could translate into new opportunities in your career. Look for hackathons specifically centered around some skills you haven’t practiced in a while to give you a jumpstart.
Join a subscription
Data science practically requires a mindset of continuous learning. If you join a subscription platform, you’ll have a constant stream of new inspiration and chances to pick up skills and relearn data science once again. Plus, if you find a skill has fallen by the wayside, you can always go back to videos you’ve watched before.
We offer our very own AI+ training, a subscription platform where you can learn basic to advanced skills and watch thought leaders in the field conduct workshops or training tutorials for a variety of popular areas. We’re always uploading new things and offering opportunities to expand your data science mastery.
Some of the courses and trainings we’ve had include:
- Drift Detection in Structured and Unstructured Data
- AI and Machine Learning with Tensorflow
- Evaluating, Interpreting, and Monitoring Machine Learning Models
- Natural Language Processing in Accelerated Business Growth
- Applied Pandas: Twitter Analytics
Head to meetups and conferences
Collaborating and networking with peers in your industry can help spark learning as well. Look around your area for data science meetups. Are they talking about things you care about? Holding demonstrations? Conducting projects? Here you can apply your skills but also get some guidance from others, thanks to community knowledge.
Conferences are another way to do some relearning. At ODSC West 2022 coming up this November, for example, we will host workshops and training sessions for everyone from beginners to advanced data scientists. You can pick and choose from new skills or refreshers in some you haven’t practiced in a while. And even better, you can add to your network for advice or help.
Increase the difficulty
If your current data science pursuits aren’t challenging you to continue learning, up the ante. Get bigger and bigger datasets to work with. Try to solve problems faster. Try scaling your algorithm if you can. You might take part in more advanced courses or trainings for a specific purpose. You could even teach someone else a skill.
Increasing the challenge of projects yourself can help you gain a deeper understanding of your topic. It pushes you to acquire new ways of working with data. In some cases, such as the teaching example, you can even learn some valuable communication skills as well.
You can approach this in several ways. First, you can look at the types of tasks you normally take on at work and find ways to increase the difficulty on your own time. Another option could be finding projects that interest you and require skills you don’t have just yet. Yet another choice could be taking on projects in collaboration with others so that you can learn and get out of your comfort zone.
Adopt a mindset of continuous learning
Relearning data science is always going to be something you’ll undertake. There might be skills you don’t always use but need to resurrect for a new project. You might start a new job or gain an interest in a pressing question. Whatever the reason, a mindset of continuous learning will help you overcome any stalls in your learning trajectory.