How not to get overwhelmed by learning data science

Shruthi Jain
4 min readApr 11, 2021

Data Science not a big word but a vast field. sometimes we feel like how much ever you learn is not enough. I understand that I have been going through this stage for a long time. the overwhelming feeling you get when you are trying to get into the data science field and be a part of it is something I am sure all can relate to. This phase is quite natural so don’t worry we all go through it but I feel there are ways we can overcome this feeling and feel confident in ourselves while entering the field.

I have heard somewhere on LinkedIn about being a tortoise and not being a Rabbit in the data science field. which means that being consistent like a tortoise in your data journey will get you success rather than being the fast pace and then burning out in between your data science journey.

let me tell you some tips to overcome this overwhelming feeling :

One step at a time

Data science is such a vast field and having to learn so much always makes us want to study faster and learn every single skill as soon as possible but this will only confuse you and not let you master one skill at a time. you will feel that everything you learned has some bits and pieces left to understand or learn or you will soon start feeling demotivated because of weight-age you have been taking on yourself for this. so always go one step at a time. take your time to master one skill and then jump to the next this will improve your efficiency of work and will lead to being the best practice in every single skills.

Don’t Rush

This point co-relates to the first point. Don’t rush your studies or career path. you will have to practice and work on implementing those skills which you want to master so that you can improve the quality of your work and don’t burn out in between.

Take some break

This journey or path is not all about learning and learning or keep on doing work, No you will have to take breaks in-between all the study time or work time. always have a timer in front of you and take a break after every 30 to 40 minutes. this necessary because we do so much of our work on laptops and PCs that it affects our health in a lot of ways. So you have rules like this for your self-care and take action on it and have some self-care time.

Don’t start something when one is still ongoing

This concept is something that everybody would not agree on because it’s all about multitasking. when I say multitasking I mean it in the field of studies. some people are great at multitasking and learning different concepts at the same time but this can cause a lot of problems. I dealt with it. I was learning all over the place and it got me confused and not gave me a strong understanding of the concepts.

Make a Plan and Follow it

Pre-plan everything you wanna do. make benchmarks and in how many days you wanna achieve and then plan your learning. this will surely help you to learn without being all over the place and give you a structure and approx time you might be prepared for next thing. I suggest making To-Do list for every day or week about study topics or work to complete and keep it where it will more visible.

Don’t jump straight away into internships or jobs

Learn first and then implement them in real-life projects ie Internships and jobs. I had when through this that I was learning and also applying for internship and did get one. but I was not satisfied with my work because of my lack of knowledge. I agree that internships are the best way to learn and gain experience but your base needs to be strong like your basics of all concepts or technical parts need to be strong to gain more knowledge and experience.

So now you know how not to get overwhelmed with or to start your data science journey. go ahead then. I started following these things right now and of course, these are my personal opinion so if you have any other ways go for it.

Happy Learning !

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Shruthi Jain

Hello all , i am Shruthi and very passionate about learning Data Science . writing from my personal experience for real solution in relations to Data Science.