How to Learn Math for Data Science, The Self-Starter Way

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How to Learn Math for Data Science, The Self-Starter Way

original source : https://elitedatascience.com/learn-math-for-data-science

Math Needed for Data Science

every data scientist needs to know some statistics and probability theory. We have a guide for that: 통계 확률은 기본적으로 공부해야한다. 

linear algebra and multivariable calculus 는 포지션에 따라 공부의 강도가 아래와 같이 달라 지게 된다.  

Application-Heavy Machine Learning Positions

interviewers may still test your basic linear algebra and multivariable calculus. 

R&D-Heavy Machine Learning Positions

Other roles need much more original ML research and development. You may need to translate algorithms from academic papers into working code. Or, you might research enhancements based on your business’s unique challenges.

In other words, you’ll be implementing algorithms from scratch much more often.

For these positions, mastery of both linear algebra and multivariable calculus is a must.

Step 1: Linear Algebra for Data Science

For application-heavy roles…

Khan Academy has short, practical linear algebra lessons. They cover the most important topics. (유튜브에 있는 khan academy  Linear Algebra
 시리즈가 구 버전인것 같은데 내용상의 차이는 없는것 같다.)

For R&D-heavy roles

MIT OpenCourseWare offers a rigorous linear algebra class. The video lectures and course materials are all included.

(유튜브 MIT 18.06 Linear Algebra, Spring 2005 를 보면된다.)

And if you only need to review:

Step 2: Calculus for Data Science

For application-heavy roles…

Khan Academy has short, practical multivariable calculus lessons. They cover the most important concepts.

For R&D-heavy roles…

MIT OpenCourseWare offers a rigorous multivariable calculus class. The video lectures and course materials are all included.

And if you only need to review:

  • Multivariable Calculus Review (Video) – This is quick review of multivariable calculus in the format of solving practice problems. Recommended if you’ve taken multivariable calculus before and just need a quick review.