How To Up-Skill In Data Science
Once you become a data scientist, that's not the end; this is just the beginning.
A career in data science means you constantly need to be looking to improve due to the pace of the field. It doesn't mean you need to work continually, but you should have some processes that allow you to keep improving regularly or at least at a rate desired by you.
In this article, I will explain my framework for up-skilling in data science, and hopefully, it will clarify or give you some ideas how you can approach it as well.
Where do you want to go?
The first step in anything is deciding where you want to go. Saying you want to "up-skill" is vague, so you should be clear on your direction.
What I mean by direction is kind of up to you, but in my experience, it generally means these things:
- Is there a particular area you want to learn?
- Is there a technical tool you want to learn?
- Is there a specific industry you want to get into?
- Is there a specific role of data you want to work as?
Again, these are not all the options, but they give you a sense of how you should approach this stage. You essentially want to easily explain what you are up-skilling towards.
Once you have an end goal in sight, it's much easier to navigate your "up-skilling," and you can always tweak your direction later on if need be.
As the famous saying goes
You can't steer a stationary ship
Oh, and one more thing: If you want to up-skill in a way that helps you in the job market and likely increases your compensation, then I recommend keeping up with trends and investing time in learning the things that are popular or will be popular in years to come.
The elephant in the room is that learning GenAI and LLMs will benefit you in the current market, as that's where investor money is going. I don't recommend chasing trends purely for financial gain, as some intrinsic motivation should be involved. However, to each their own!
How do you get there?
Now you have a target you want to up-skill towards; you need a way of getting there.
Networking with individuals who have already reached your desired position is the most effective approach. You can get their advice, which will be tailored specifically to you.
For example, I want to pivot to being a Machine Learning Engineer, so I contacted my friend Kartik Singhal, a Senior Machine Learning Engineer at Meta, for his advice and guidance. He provided me with many resources and taught me how to approach my learning if I wanted to achieve this transition.
He has a great newsletter, The ML Engineer Insights, that I recommend you check out if you are interested in MLE stuff!
Even though I have an online presence that helps build these connections, you certainly don't need one.
People frequently ask me for data science advice, and I always reply, giving them the best guidance I think would work for them.
You can literally message so many people, and chances are at least one person will reply! LinkedIn is by far the best site for this, but you can use many others, so don't limit yourself.
If you don't want to do that, chances are there are some free online resources, roadmaps and videos explaining how to reach your target. The only downside is that they won't be personally tailored to you, but it probably doesn't matter so much if you are a complete beginner.
As an example, if you want to learn LLMs, then Andrej Karpathy has probably the best course on this and its free on YouTube!
After you have all this information, create a learning plan or roadmap to clearly define your actions. These online resources will often already have one created for you.
I find people often over-complicate this step. All you need is a plan that heads you in the right direction. It doesn't need to be the "best", whatever that means, but as long as it covers everything you think you need, it's fine. Don't overthink it.
What do you do?
The question now comes to how you make sure you stick to your plan and actually do the work required to up-skill.
As the book Atomic Habits made famous, it's all about the systems you put in place.
You do not rise to the level of your goals. You fall to the level of your systems.
The first strategy I employ is blocking out time in my calendar specifically designated for up-skilling. I recommend at least two hours a week to make decent progress, but I would debate an hour a day is preferable if you can.
I firmly believe that no matter who you are, there is some time in your week you could squeeze in learning. Don't get me wrong, I understand it's harder for some people than others, but if it's something you want to prioritise, then you will figure out a way.
I have a separate article (linked below) explaining how to schedule time for learning like this and the steps you can follow.
If you are working at a company, ask to get involved in projects related to what you want to learn. For example, I am looking to pivot into machine learning engineering, so I asked my line manager if I could work on more projects focusing on the deployment and software engineering side.
You will be surprised how receptive people are often; all you have to do is ask! The worse they can say is no, which is normally quite unlikely.
If your company can't put you on specific projects, suggest you want some learning and development time in your work week. From my experience, many tech-based companies have this as a perk, as they also want their employees to grow. Not only does this benefit the employees, but also the company as they have more up-skilled workers.
This gives you flexibility and means you don't have to learn outside of work hours if you don't have time. Again, from my experience, many companies and management are pretty receptive to this, and I am sure most people will be on board with the idea. Suggest it to your line manager if you have time.
Useful Habits and Practises
The following are some helpful practises and habits that really help me continuously learn:
- You should always have something that you are learning or want to learn in mind. I have a massive list of areas I want to learn more about that I constantly update!
- Take time when learning a topic. Careers are long, like four decades. So, you can afford to be patient and understand it deeply, which will pay off in the long run.
- Learning by doing and physical implementation is the best way to learn. Build something, don't just take courses.
- Employ radical focus; this is a superpower nowadays. Remove as many distractions as possible and concentrate fully in that time block.
- Building a study schedule is a game-changer and really a non-negotiable. It helps you stick to your learning.
- Chain the things you learn to be relevant topics. For example, learn neural networks, RNNs and CNNs, and finally LLMs.
Summary & Further Thoughts
Data Science is a career filled with continual learning, which you must do to stay on top of your game. This is both a blessing and a curse because it keeps the work interesting, but you must invest time and strategies to stay current. Hopefully, this article will give you some ideas and methods for staying sharp in data science!
Another Thing!
I have a free newsletter, Dishing the Data, where I share weekly tips and advice as a practising data scientist. Plus, when you subscribe, you will get my FREE data science resume and short PDF version of my AI roadmap!
Connect With Me!
- LinkedIn or Instagram.
- My YouTube Channel to learn technical data science and Machine Learning concepts!
