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Andriy Burkov shared this post recently https://julienbeaulieu.github.io/2019/09/25/comprehensive-project-based-data-science-curriculum/
P.S. Some great answers up there!
Depending on how good you're with Python/R you can either start with Kaggle Kernels and playground competitions. Else start with Python/R (codeacademy is a good place to start with syntax and basics ) then start exploring kaggle kernels. Idea is to be able to understand what's written and try to make meaningful and deliberate changes to the kernel code. For ML basics "Introduction to Statistical Learning " is a good book to start with. PDF is freely available and chapter end assignments are in R.
Learning Data Science is a continuous process as technologies keep evolving, but it's essentially important to learn basics.
I would highly recommend learning on your own. The open source community is too kind and everything is already up their in front.
Python : YouTube (so many channels out their, offering beginner to advanced course for free)
Machine learning : do the Andrew Ng course on Coursera, its the best for beginner
Kaggle : while competition and datasets are great way to practice, Kaggle also offers courses, python, machine learning, sql, stats everything, try those as well.
Statistics : Just read ISLR
If you actually following in the order above, you will be good too go. Also, keep exploring GitHub
Learning Data Science by our own is a tedeous task. I would recommend its better to get enrolled for Certification in Data Science & Machine Learning with any reputed training institute. My all time favourite is AnalytixLabs. They also cover Python.
You can practise python coding by your own by following geeksforgeeks.
I got started with data science a year before I got placed in insti.
The first step was to get familiar with python. I've done this course called intro to data science with python on coursera. This helps a lot with the data wrangling involved in a DS job.
For ML concepts, I don't know good courses as I've taken just one which was in insti. Maybe others can answer this.
But what helped me greatly were kaggle and data science competitions. Kaggle has tutorials as well which help you get started right away with applying ML algorithms.
However it is important to know the math behind the algos and the assumptions as they determine which approach is to be used given a scenario. Go through Kaggle kernels (notebooks), if there's some new concept/ algorithm you don't know, read some blog related to it.
Finally take part in competitions to apply your learnt skills and here you will be forced to learn new things to push your performance. Also you would be learning a lot from peers.
All the best!
Thanks so much everyone - Your responses are really helpful :)