In this video I talk about how to ultralearn data science. This learning philosophy is based off of Scott Young's book ultralearning (linked below). Scott used this approach to learn the entire undergrad MIT Computer Science curriculum in a single year (and pass all the exams). Ultralearning has 9 principles and I show you how you can apply many of them to data science.
Ultralearning Book: amzn.to/33LTvYf
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Principle 1: Metalearning - Plan your learning journey. Think about your data science goal and create a roadmap to get here. This should take up to 10% of your total learning time. If you want to become a data scientist, you need to learn programming and math. Figure out exactly what concepts you need to know from each disciplines.
For learning Coding: • How To Learn Programmi...
For learning Math: • Math Needed for Master...
Principle 2: Focus - To stick to your data science learning plan, you should train yourself to focus. This is a learned skill. You should pay careful attention to your environment and schedule your time wisely.
Video on data science focus: • How to Stay Productive...
Principle 3: Directness - This is my favorite one. If you want to learn data science, you should do data science. You will quickly find out where you are weakest. You should then work on these things.
Video on projects for your portfolio: • The Projects You Shoul...
Where to start: • Where YOU Should Start...
Beginner Projects: • 3 Proven Data Science ...
Project Example: • Data Science Project E...
Principle 4: Like
Principle 5: Subscribe
Principle 6: Feedback - to maximize your data science performance, you need to focus on feedback loops. Programming is great because you have immediate feedback from error messages. I also recommend reaching out to your friends who are data scientists or people in your network to get feedback on your projects. I also am happy to give resume or project feedback, but please watch all my relevant videos first.
Playlist: • Data Science Portfolio...
Principle 9: Exploration - You need to keep learning because data science is constantly changing.
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25 июл 2024