One of the toughest challenges for aspiring data scientists is consistency. If you can acquire incremental knowledge and skills in data science consistently, you’ll be amazed at how much knowledge you’ll acquire after a year or two.
If you want to succeed in your studies but are doing the same thing that you’ve always been doing then how can success find you. To get a different result, you need to take a different approach.
In this article, we shall explore the 7 pro tips that you can use implement now to effectively study data science and make some serious progress.
1. Find your Purpose
A lot of people are looking at starting a career in data science without having a clear idea about why they want to do it (If you do, kudos!) While this is completely understandable, in the long run it can also set you up for disaster.
The Okinawans have long been known to have longevity and the underlying secret to this is there strong sense of purpose in what they call the Ikigai.
Have you taken a moment to identify for yourself what your Ikigai is. So what exactly is Ikigai? It can be thought of as the feeling that makes you want to wake up in the morning. Such feeling arises from the intersection of the 4 major concepts as summarized in the diagram below.
Are you studying data science because you enjoy it or see being a data scientist as an interesting career? Having a understanding of your why can help your reflection of what drew you into the world of data science. Perhaps you enjoy problem-solving and research. Or maybe it’s because you’re simply curious about how the world works and want to learn more. Asking yourself these kinds of questions can help put passion in perspective.
Remember this, write it down or post it on a wall. In the future, when you get stuck or lose hope, come back to this to recharge and get back up and realize what you’ve started!
2. Set Goals and Make Plans
One of the biggest challenges with learning data science is that we struggle to set effective goals. We often set too many goals which cause us to lose motivation and energy.
So how can you exactly set your goals and make the necessary plans to reach them?
It’s quite simple actually. Here’s what you need to do in 2 steps:
- Think of the end (the goal) in mind.
(Habit 2 from Stephen Covey’s 7 Habits of Highly Effective People)
- Reverse engineer and list down what you need to know and/or do in order to reach that goal. Make it as detailed as you possibly can.
3. Have Grit
We may have heard about the growth mindset as proposed by Carol Dweck and how cultivating one can help us be open minded to embrace incoming challenges as well as to grow intellectually and physically. Another important concept is Grit.
Life has a way of throwing curveballs. You can’t know in advance which ones will come and which will miss. But you can focus your efforts on the ones that matter, and develop the skills and resilience to bounce back.
Angela Duckworth, a psychologist and best-selling author of Grit: The Power of Passion and Perseverance, has found that the combination of passion and perseverance (which she refers to as grit) is responsible for the success of high-achievers.
Many of us know how difficult it is to keep learning new things. It is crucial to have a willingness to persist in spite of difficulties. It also helps to have a positive approach to life. It is often easy to give up when things don’t go as expected or when setbacks happen. You need the combination of both grit and motivation get you moving in the right direction for an extended period of time. Motivation will get you enthusiastic in starting the journey of learning data science but it is grit and habits that can help sustain your efforts over the long-term. The journey will have its ups and downs, you just need to persevere and hang in there. Thus, being a part of a community such as the 66 Days of Data may help you to have a greater sense of belonging as you are not in the journey alone but there are hundreds or thousands of other aspiring data scientists who are struggling, growing and thriving.
4. Have Good Habits
Getting good at data science isn’t an overnight process. It’s a process that involves developing good habits.
“Motivation is what gets you started. Habit is what keeps you going.”
— Jim Rohn
According to James Clear, author of Atomic Habits, all you really need to do to start creating good habits is to address the key factors consisting of Cue, Craving, Response and Reward by making it obvious, attractive, easyand satisfying. Inversion of these factors will allow you to break bad habits namely by making it invisible, unattractive, difficult and unsatisfying. I’ve summarized this in a graphical diagram shown below and I highly recommend reading the Atomic Habits for a more in-depth coverage.
5. Be Consistent
Consistency in learning is a challenging, but essential skill. Learning data science requires perseverance in light of the vastness of the field (i.e. there is simply too much to learn).
5.1. Break Tasks into Bite-Sized Chunks
Learning data science doesn’t have to be an all encompassing task. You can break down the learning to small bite-size units. By starting small, you can build up your knowledge stack. Each day you’ll move one step closer to becoming a better version of yourself. You might start off with one simple project or analysis then progress to something more extensive.
5.2. Be Publicly Accountable
If you are having trouble being consistent in your learning journey, Ken Jee might have the answer for you. Ken is a good friend of mine and a prominent data science YouTuber. He launched the 66 Days of Data initiative that aims to help you achieve consistency.
To participate in the 66 Days of Data initiative, you just need to do the following:
- Learn or do data science for at least 5 minutes a day
- Track your progress, reflect on what works and what doesn’t
- Share your learning progress publicly on Twitter or LinkedIn
Sounds simple? That’s the goal. As you will notice this initiative helps you to build a habit of learning on a consistent basis by making this as easy as possible. The public accountability that arises from you sharing your progress will also help you to be engaged and have a sense of purpose that you look forward to while learning alongside like-minded individuals in the data community who are also participating in the challenge.
6. Pay Attention to Your Process
Focusing on the process helps to keep your mind away from all the negative thoughts and anxieties that may be holding you back. By focusing on the present moment and on actually getting the work done (i.e. doing the actual reading, working on coding exercises and projects, etc.) you are getting the momentum rolling. As you focus more and more on the process, you are seeking out ways to improve your productivity. You are pondering:
How can I improve the model’s performance?
How can the code run more efficient and compute in less time?
Seeking out solutions and solving these questions helps you to be more productive and engaged to the learning journey. When you’re having fun learning, the learning will not feel like a chore but instead is something that you look forward to doing, day in and day out.
“When you fall in love with the process, not the product you don’t have to wait to give yourself permission to be happy, you can be satisfied anytime your system is running”
— James Clear, Author of Atomic Habits
The great thing about participating in challenges, such as the 66 Days of Data or the 100 Days of Code, is that it keeps you focused on the process. How so? It’s because of the public accountability where you post your daily progress for the entire community to see.
Let’s say that you’re missing posts, others in the community may be wondering what happened or it is also the case that you don’t want to let others down. Thus, you strive to make daily progress so that you can proceed with the challenge without fail.
7. Use tools to stay focused and organized
7.1. Pomodoro Technique
Studying according to the Pomodoro technique is a great hack that can help you to study effectively.
The idea is quite simple:
- Study intensely for 25 minutes
- Take a 5 minutes break to stretch and relax in order to recharge before embarking on additional rounds.
The great thing about this approach is that the time constraint helps to eliminate procrastination as you dedicate the entire duration of 25 minutes to studying, coding or working on a data project.
This deep work if coupled with the elimination of distractions such as keeping your phone out of sight may help to boost productivity.
7.2. Productivity Tools
Notion is a great and free productivity tool that you can use to track your progress (Step 2 of the 66 Days of Data) while taking notes to reflect on what works (then double down on these) and what didn’t.
Below you will find an example of a Notion template that you can use to keep track of your learning progress.
You can even beef up the template and customize it to also include links to your favorite resources that you use in your data science learning journey such as links to available computing resources (e.g. Google Colab, Kaggle, etc.), API documentations, online tutorials (e.g. Real Python, Machine Learning Mastery, GeeksforGeeks, etc.), cheat sheets, etc. Remember to make it easy in order to instill a new habit.
Break into data science may be a daunting task but with the proper mindset and the proper habits, you can overcome these obstacles and prevail. Don’t think about learning data science as a short term goal, so don’t worry about where you are now. Today you may be struggling to keep up but keep at it and you’ll be amazed where you’re at in 6 months or a year from now. Think of this learning journey as a marathon and make incremental progress day by day. You got this!