The popularity of Data Science and Machine Learning has surged in recent years due to evolving technology and the massive generation of big data. Their application pours down to so many fields and industries, and some of the biggest global companies are utilizing AI in a big way in their business strategy. That said, there is still a lot of uncharted areas waiting to be exploited and this field will continue to ‘disrupt’ life as we know it.
It is of no surprise therefore that machine learning and programming for everybody(getting started with python) were the #4 and #3 most popular courses on coursera in 2020 respectively. The PYPL index ranks Python as the most searched tutorial language on Google, and a lot of the players are in the data science/ML/AI field.
So if you are currently studying to be a data scientist, you are in good company. The pie is large and the possibilities are endless, but we all want to get over with the learning and get our hands dirty doing actual work and implementing solutions.
I have compiled a list of effective ways to quickly learn and apply the concepts you learn from my own personal experience growing in the field this past year. Whether you are taking a structured degree, an online MOOC, or self studying through YouTube and books, applying these strategies will quicken your data science learning journey and and also make you stand out. And who knows, you might even earn some money while at it.
- Join a community
Local communities and groups have been instrumental in my data science journey. Meetup.com, whatsApp and telegram groups are some platforms for engaging with and contributing to local groups in your area and beyond. Being part of a community is beneficial in many ways. Members organize local events and competitions, share various opportunities such as jobs, scholarships, trainings and so much more. You can also post technical questions and get solutions quickly. I have also experienced members forming study groups and mentorship teams to create products that have gone to impact individuals and the community.
2. Volunteer
Giving your time is a very rewarding experience. You can apply as a teaching assistant or a mentor at a local college. There are also websites such as omdena.com and datakind.org that take up volunteers to tackle real world problems. Other volunteering opportunities are to lead a local community group by reaching out to the organizers and offering your time, or commit a few hours a week to answer beginner data science questions on online forums such as quora or stackoverflow.
3. Apply for those jobs anyway
There will hardly be a time when you feel completely confident of your skills in your first year learning of data science, or for some people, even a few years in. But it helps to know what the industry expects of you and the other way round. I therefore encourage you to occasionally apply for those junior roles and even attend interviews if invited. This is also an opportunity to network and stand out to potential future employers. linkedin.com is a great place to find jobs. Make sure your profile is updated and stands out. Watch this video on how to improve your linkedin profile. It’s meant for writers but it can apply to anyone.
4. Compete in challenges
A lot of practicing data scientist swear by kaggle.com as a great experience builder. You can find challenges from beginner level to advanced. Their courses are also tailored for beginners and will get you participating in competitions in no time. You also get to see how different people approach similar problems. I would encourage you to also share your kernels and get feedback about your solution from fellow competitors. Zindi.com is another great platform for data science and Machine Learning competitions in Africa where your chances of winning money are higher.
5. Share what you are learning
"If you want to learn something, read about it. If you want to understand something, write about it. If you want to master something, teach it." Yogi Bhajan
Whether by blogging on medium.com or vlogging on youtube.com, you get to share what you have learnt to an audience in your own words, and consequently learn more and retain it. You can actually earn some Money from the two websites I have mentioned above, but there are also many other places to document your technical experiences such as dev.to, quora.com and many others.
6. Upload your notebooks on github
This is more for yourself than for the public. Throughout your learning journey, chances are that you will go through a lot of jupyter notebooks. My advice is that you upload all these notebooks on your github account. Not only are you building up your portfolio, but making you accountable for all those big and smaller projects you begin to completion. You will also get used to organizing your code and markdowns well for presentation to an audience other than yourself.
7. Follow industry experts on social media
I have found industry experts in this field to be very generous with information and even their time. Twitter, LinkedIn and YouTube are great platforms for reaching out and connecting with industry influencers. You can pose questions or ask for advice, and some even organize live sessions and webinars to connect with their followers.
8. Keep up with industry news and trends
Machine learning and AI is an ever growing field. Technologies are developed daily and papers are published all the time. It is important to stay motivated by knowing what you are capable of accomplishing with the knowledge you are acquiring. Some of this technology does not even require much coding and you might find yourself creating a usable product even as a beginner. You can signup for newsletters such as such as those in this list. I have found YouTube to be a great resource for recent AI news by just typing ‘ai news’ on the search bar and fresh content is displayed.
Finally, remember that you need to put in the time. Practice coding every day while still allocating time to connect and catch up on current trends.
"Success has to do with deliberate practice. Practice must be focused, determined, and in an environment where there’s feedback." Malcolm Gladwell
Use these strategies to get through the initial learning process faster so that you can spend your time building actual solutions that will be impactful in the society.