I am a Master's student in physics. Lately I have been intrigued by the field of data science. I have beginner level knowledge of python, undergraduate level knowledge of mathematics and master's level knowledge of physics. I now want to learn data science. I scoured the internet but could not get a good answer. I am willing to give around 1 to 1.5 hours daily. What steps should I take to become proficient in data science i.e what books should I read, what courses should I do, etc.?
I too have a masters degree in physics so maybe you can relate!
The first thing to do would be to get your fundamentals in python strong. Pick up a python beginners tutorial from Youtube and learn all the fundamentals. This should take you less than a week.
A good starting point for all newbies is Kaggle. It is a platform for budding and experienced data scientists where you can start learning data science from scratch if you are a newbie (there are Kaggle tutorials for this purpose) or you can take part in Kaggle competitions where you can gauge where you stand against other people.
Another great option is Youtube tutorials (as mentioned above). There are literally hundreds of tutorials on virtually every topic you could imagine in data science/data analysis. Also they will provide you with proper guidelines and best practices to follow in data science.
One channel I would recommend following is Krish Naik (I personally follow his channel too). He has complete tutorials on virtually almost all topics in data science. His channel is a good place to build your fundamentals in data science. Basically the strategy to follow is (and he recommends the same strategy!) follow his tutorials to get a good grasp on basics and once you get them, you can move on to advance stuff. I personally followed this same strategy and trust me it helps a lot!
Another channel you could follow is Codebasics. He has some good tutorials too!
I did not read any books because I am much more comfortable learning from videos where each and every small and silly things are explained. But it is a matter of personal preference. If you learn easily from books then go ahead.
There is plenty of material on the internet for data science be it articles, videos or competitions. Just select one or two platform and start without wasting too much time on gathering the materials or choosing which platform to start!
I'm also a newbie in the DS world but one thing that I find really helpful which I highly recommend is to do a lot of work "by hand". All the model that you can use in DS or Machine Learning are perfectly covered and implemented in python libraries like pandas and sklearn. What I found super useful in the beginning is: try to write models on you own. For example create a simple linear regression, create a Perceptron (the simplest Neural Network) or a basic classifier like a K-Nearest Neighbor from the scratch only using basic python libraries like numpy.
This will really help you Understand how a model works.