Learning a new skill can be hard. And when it comes to starting learning data science, one can expect hours and hours of studying.
You need to understand mathematics, statistics, programming, business acumen and a number of countless tools. It could get intimidating for a beginner and motivation might be hard to come by.
In this article, we discuss 5 ways that may help you stay excited about your work, without going off the learning curve.
These are condensed from various resources I’ve come across over the past couple of years – books, Ted Talks and peers!
1. Start Small
How many times has it happened that you created an elaborate study plan, got the resources, but just couldn’t stick to it? We’ve all been there!
The key to forming habits is to make them as simple as possible.
Instead of setting unrealistic and mammoth goals, make them easier for you to digest. Break down your goal into smaller chunks, then build on them.
For example, if your goal is to learn SQL by the end of this month, start out with writing 2 queries a day. Or watching one tutorial video on YouTube every day.
The key here is to stay consistent. Start out with the bare minimum, then add to it. Before you know it, you’ll see the magic of compounding happening!
2. Set clear goals and create metrics
Shooting in the dark without knowing your target rarely works.
It is important to have realistic goals and a way to measure your progress.
That is, you need a ‘scoreboard’ for yourself to give you immediate feedback.
For example, if you’re trying to learn a new ML model, you could have the following metrics of success:
· Watch 2 videos explaining the concept
· Read 2 articles that explain the methodology in depth
· Code the algorithm from scratch
· Demonstrate understanding of a project and share it with peers
Creating these metrics gives you a checklist and a simple ‘yes/no’ answer to whether or not you learnt to your best potential today.
3. Keep score and be accountable
When you learn data science, you already collect and analyze a lot of data. Why not look at your own?
It could be helpful to keep a track of the metrics mentioned above on a spreadsheet. Tracking your month-by-month progress might help you point yourself in the right direction from time to time.
You may create a graph of your progress over months and even turn it into an entire project for your portfolio!
Another trick is to have an accountability partner. Keep a study partner who can hold you accountable. Extra points if they are also learning with you.
It might make you more responsible for learning daily and keeping up with them.
4. Scheduling is key
Motivation is often hard to come by. What works more often than not are habits.
Scheduling your day removes the effort to think about what to do “next” after each activity.
Remember to keep some “downtime” for yourself in the schedule. This is when you reset and recharge.
5. Optimize your surroundings for productivity
Learning a new skill is already hard, so you might want to make things around it as easy as possible for yourself.
Remove any distractions from your workspace, even those tabs on your screen that are not relevant to the task at hand. Or leave your phone in another room, except for break time. You get the gist.
These simple habits could change the way you work, not just for data science. This is it for this blog, thanks for reading!
