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 This is what most beginner resources focus on; your knowledge here has the least transferability and tends to go out of date quickly.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.
I can list a bunch. But the answer depends on a lot of things.
1. Which language?
2. Do you have any background in programming and if yes how much and in which language? Beginners can range from "I don't know what a loop is" to "I've been a front end developer for a few years and want to try something new". These people will obviously need to approach things very differently.
3. What's your math background? Do you know what a derivative is and how matrix multiplication works? Do you want to go in depth or do you need just a general understanding of the algorithms? Some people will need to start at precalculus if they really want a solid foundation. The same people also most likely won't have the patience or interest to stick with the theory for long enough.
4. How will you use what you're learning? Is there a specific goal related to this and does it have a time horizon?
...
Anyway, I'm rambling. Data science is just programming and statistics. Your general learning lanes are
1. Theory - calculus, linear algebra, statistics, ML algorithms.
2. Programming.
2a. Good software development principles - writing maintainable code, version control, testing, design patterns etc.
2b. Tooling - learning the language-specific ecosystem of libraries. This is what most beginner resources (including the OP) focus on and is also the one that constantly expires and has the least transferability to other skillsets and fields. Obviously, it's still necessary - using the right tools and knowing them well goes a long way.
You mentioned ISLR - it's a good beginner-ish theory book that helps you understand how the algorithms work without going too deep into the math.
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.