Depends on what you're interested in, your goals and your starting point. You have two main learning lanes:
1. Theory.
Math, calculus, linear algebra, probability, statistics, ML algorithms. ISLR is a very good beginner-ish resource that helps you understand the algorithms but doesn't go too deep into the math. As you go deeper, you may realise that you have gaps in your math knowledge and you need to cover a lot more probability, calculus and linear algebra.
2. Programming.
2.1. Good software engineering practices - writing maintainable code, design patters, version control, unit testing etc.
2.2. Tooling - knowing the language-specific ecosystem of libraries (the OP is an attempt to teach you this in two languages at the same time). This is what most beginner resources focus on; your knowledge here has the least transferability and tends to go out of date quickly. Still, using the right tools and knowing them well goes a long way.
I'm sorry in advance if I'm taking too much of your time.
#2: coding is my hobby and have been writing well designed apps for a long time, so thats not an issue
#3: ISLR is teaching how to do ML algo in R, so there goes that point
#1 is what I'd like more information. Good important is the maths to work as a data scientist? I'm planning ISLR and then maybe ESL or some advance course
A few of my friends work on Data science and they said that maths isn't that important as in, one needs to know the formulas and why things work the way they do as in not rote learning the math.
It'd be great if you can list down intermediate courses, the learning market has drowned good tutorials and books with not so good guides!
> A few of my friends work on Data science and they said that maths isn't that important
Without the math you won't understand anything in ESL.
Which might be okay if the job doesn't require you to go into that much depth - some data science jobs are more focused on research (very math-heavy), some on ETL and/or engineering, others on business understanding and communication; it's a really broad title.
1. Theory.
Math, calculus, linear algebra, probability, statistics, ML algorithms. ISLR is a very good beginner-ish resource that helps you understand the algorithms but doesn't go too deep into the math. As you go deeper, you may realise that you have gaps in your math knowledge and you need to cover a lot more probability, calculus and linear algebra.
2. Programming.
2.1. Good software engineering practices - writing maintainable code, design patters, version control, unit testing etc.
2.2. Tooling - knowing the language-specific ecosystem of libraries (the OP is an attempt to teach you this in two languages at the same time). This is what most beginner resources focus on; your knowledge here has the least transferability and tends to go out of date quickly. Still, using the right tools and knowing them well goes a long way.