How to NOT learn Data Science in 2024
Like seriously…
Introduction
With the year ending in like 2 months, it’s almost customary for people to think about their new year’s resolutions and with that comes the push turning into a shove of learning things and dramatically changing their lives forever!
It’s a good thing, for obvious reasons, to keep up with the ever evolving technology you’ll always have to learn something new and better.
Data Science is not a new thing and you can find videos and articles and even courses (ikr!), on “How I would learn Data Science in 2023”, 2022, 2021 and I kinda don’t want to find out what it dates back to. Anyway, my point being, you have the resources and some of them are actually helpful when you listen to it, but do you?
With the vast landscape of information at our fingertips, it’s not suprising to fall in a rut and then sitting there stuck, for the new year’s clock to strike 12 and then questioning your life choices. It’s not your fault. (Kinda).
It’s evident that knowing what not to do can gain you more and better results.
This is exactly what we are going to do today. Rather that going through the same things over and over again and then rinse and repeat, we will be taking a look at how to avoid the pitfalls so that you can automatically reach your destination and end this journey for good!
1. Do NOT start from the START
I know, crazy right. Well it’s always preferred to not haste things up and learn concepts systematically, in an order to save you so much time later in your learning process, but I cannot emphasize enough how important it is to know what exactly is your goal and what you want to do.
Whether you even want to be a Data Scientist, or you want to be an AI developer or you simply want to leverage machine learning for your new startup or just want to show off at parties. Pick your poison.
Learning things with your endgoal defined will save you both time and energy while keeping you ahead on your path. You will not spend your time learning things which are completely unrelated and unecessary for you at that point in time.
If you don’t have a set vision of what you want to achieve, I think it’s time to take a deep breath and think about it. (Fun Fact: It’s completely okay to borrow other peoples’ goals until you find yours. Just start now.)
2. Do NOT jump without an ABC plan
Let me explain. Everyone knows this plan. Learn, make projects, update resume and reach out to people for jobs. But for how many of you has it actually worked?
After you decide your goal, you shouldn’t have one master plan but three A, B and C plans which would help balance your mood, energy and honestly willpower to get through the day.
I’ll not tell you how to make a detailed curriculum because that’s very subjective and everyone learns differently. Find one curriculum and stick to it, you can always branch out to different resources for topics but stick to one curriculum. I would rather tell you how to study.
Consistency is key and no one can argue that and lets be honest not everyone can be David Goggins (if you are, please push me). So to achieve your dreams while still contemplating life, you need to know your ABCs.
Plan A: Action
For the days where you are full of energy and can smile without coffee. Here you would want to study things like programming, SQL, machine learning concepts and devote most of your time in getting your hands dirty. You’ll learn only if you do it yourself.
Having a set timeline is a game changer. Make a calender where you write the most topics and practice that you can do in a day. That’s your Plan A.
Plan B: Backup
Here comes the interesting part. There are days when you’re too tired, from work or family or just sick. As we’re playing on consistency, something is always better than nothing.
From the calender where you wrote the most topics, underline 1–2 which you can do that day and leave the rest. You can study small topics like Outliers or Central tendencies in statistics. Just don’t break your cycle.
Plan C: Contingency
We are now in the days where we don’t want to get out of bed. I get that. What you can do these days is watch videos. Like do nothing, retain nothings, just watch videos of topics that you were supposed to study that day. Trust me, you’re already ahead.
3. Do NOT overdo it
Discussing about the curriculum bit we just discussed, don’t nitpick. There is no such thing as a fool proof plan. There is no perfect condition or perfect equipment or lighting or temperature or pencil to study.
Perfectionism and progress never go hand in hand, and is probably one of the biggest pitfalls in your learning. Avoid it like plague(or covid).
If you fixate on making the best curriculum, that’s all what you will able to do. Instead of making your schedule 100 times, make a one that’s kinda works and stick to it.
This is not a competition with other people, it’s about increasing your skills and value, in the industry.
4. Do NOT fall in the tutorial trap
The most valuable(debatable) thing we have in this time while learning is convinience. For everything that you study and whatever you aspire to do, we have a tutorial for that.
But you should know there’s a difference between watching and doing. It’s very easy how your mind tricks you into believing that you know it now and you go blank when it’s your time to write the code.
Data Science as a field is quite experiment based and if you don’t do it yourself, you can’t present other people’s results.
While learning it’s very important to not watch 50 tutorials and add it as a skill in your resume. You will never learn like this.
You need to experiment and fail. If you’re getting everything right and are facing no errors, what did you even learn?
Use multiple resources if you have to but Do It Yourself, If you’re not seeking answers for your errors, you’re probably doing something wrong.
5. Do NOT shy away from sharing your work
Yes, you read that right. There’s a really good book by Austin Kleon, called Show Your Work, where he articlates this idea of sharing and learning.
Human beings want to know where things come from, how they were made, and who made them. The stories you tell about the work you do have a huge effect on how people feel and what they understand about your work, and how people feel and what they understand about your work affects how they value it.
A daily dispatch is even better than a resume or a portfolio, because it shows what we’re working on right now.A good daily dispatch is like getting all the DVD extras before a movie comes out — you get to watch deleted scenes and listen to director’s commentary while the movie is being made.
Share what you love, and the people who love the same things will find you.
Now that doesn’t mean that you don’t gain any insights out of this. Kleon very aptly said:
If you want fans, you have to be a fan first. If you want to be accepted by a community, you have to first be a good citizen of that community. If you are only pointing to your own stuff online, you’re doing it wrong. You have to be a connector.
Finding and building your own community of like minded people will always keep you motivated and accountable. The power of building connections is really rad. The ratio of people getting hired through connections has always been so much more than the traditional route.
Share your work kaggle or partcipate in hackathons. Write blogs or articles like the one you’re reading. Share your work on LinkedIn. Work on projects and share that code in a github repository. If you come accross people facing the same issue as you were before, help them. Do not shy away!
6. Do NOT hand over your exceptions
Apart from the contrary belief of going to people, be it your friends or collegues, the best approach to tackle a hiccup is to analyse the problem by yourself first.
As a person in the Data field, debugging is the baseline thing expected from you and this approach will improve your critical thinking ability by a lot. Getting help from another person should be your last resort. That’s how you learn.
If you are stuck with a problem, it’s highly unlikely that you’re the first one. As a Data Scientist, research should be a second nature. Resources like Stack Overflow, Github and even ChatGPT now, will help you debug your code so much faster, and help you learn. So experiment!
7. Do NOT give up!
Recently I came across this meme, which made me actually realise how crazy the learning journey is for all the data scientists and people with similar job profile. It’s very easy to lose hope.
Getting stuck is a very natural process, and most of the times what get’s you going is not your motivation (which is honestly never reliable), it’s the Discipline. This is the reason I suggested that you stick with your ABCs.
Failure is the part of any process and life in general, what will set you apart is your practice of not giving up. Revisit your plan, find solutions for your loopholes and do not give up!
Conclusion
I hope I was able to convince you with this reverse psychology technique. It worked for me and if you do it right, it’ll work for you too.
What I would recomment that you DO apart from the ‘not doing’ list is to Trust Yourself. and remember that your work is something you do, not who you are.
If you are looking for a detailed curriculum which encompasses everything that you will ever need on your data jouney, check out Accredian!
