The perfect way of learning Data science

Hello guys this is Phanindra sai. this is my 1st blog post, wish me the best for my blog posts journey.

when I started learning data science, machine learning, and AI. Iā€™ve seen many videos, roadmaps, and online paid and free courses but most of them have not made me complete the whole course and itā€™s not only my problem many learners. starting a thing and leaving it without completion like courses, tutorials and etc feels terrific and gets a feeling that we canā€™t do this. but,

The method or technique I am going to tell you may help you. It is Spiral learning.

you may go to the internet and searched it but there is nothing like spiral learning. suppose the picture on top is your roadmap to becoming a data scientist and you started learning it while many of them canā€™t complete 1/4 of it due to loss of interest or may think this is not the best roadmap or something. even if you complete this it may take you more than 1 to 2 years. until then you may forget the things you learned at the beginning.

But the perfect and efficient way is spiral learning. where you will learn more than the roadmap shown above and you will not forget them easily. we learn the same roadmap 5 times. yes, you read it right 5 times. there are a total of 5 layers or you can call 5 levels,

1. Testing theĀ waters.

The goal of this level is to get you familiar with the ML or data science universe. You will learn a bit about everything. you donā€™t do anything practical just learning and knowing how data science works. what we learn here and what tools, languages, software we use as data scientists.

2. Gaining Conceptual depth.

The goal of this level is to learn the core machine learning concepts and algorithms. At this level, we learn more about statistics, mathematics, data science workflow, and ml, Dl algorithms

3. Learning Practical Concepts.

The goal of this level is to get you introduced to the practical side of machine learning. What you learn at this level would really help you out there in the wild. you can say this is the toughest level as you do coding a lot and here you will apply all your concepts in a practical way.

4. Diving into different domains.

This is the level where you would dive into different domains of Machine Learning. Mastering these will make you a true Data Scientist. At this level, you think more about solving real-world problems than concepts. you will go through medical, industrial, agriculture, sports, and different domains. by this, you would think more like a problem solver than a coder

5. pushing it with projects.

The objective of this level is to sharpen the knowledge that you have accumulated in the previous 4 levels. At this level trust me you are almost a data scientist but you should be job prepared. for this you will be doing lots of projects and preparing your portfolio. going through coding tests, interview questions.

In this process of learning, you may learn python or machine learning again and again but, every time you learn again you go deeper than the earlier level, so at 1st level, you may not understand well but as the level increases, your level of understanding increases and applying it in practical make easier and problem-oriented.

I learned this technique from a youtube channel called campus X. he is a very good teacher, I will suggest you do check his youtube channel. even a roadmapof machine learning with free videos and code.

thatā€™s it guys, I hope you understood the advantages of learning in this method.

All the best for your data science journey. thank you