How to Learn Math for Data Science (and stay sane!)

🤖 Coursera's courses mentioned:

👉 Comprehensive math book for DS:

👉 Statistics books with R:

👩🏻‍💻 COURSES & RESOURCES
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🙋🏻‍♀️ LET'S CONNECT!
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🔑 TIMESTAMPS
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0:00 - Intro
0:37 - The math you need to know for DS
1:12 - !!You don’t need to know everything!!
2:23 - Linear algebra essentials
3:10 - Calculus essentials
4:04 - Statistics & probability essentials
5:07 - Discrete math
6:08 - Tips for learning math
12:30 - Bonus tip
13:24 - Outro

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As a member of the Amazon and Coursera Affiliate Programs, I earn a commission from qualifying purchases on some of the links above. It costs you nothing but helps me with content creation.

#Math #DataScience #Datanerd #DataAnalysis #CoffeeData #ThuVu #dataanalytics

[Thuvu]

This video took me an *exponential* amount of time to edit, which is beyond my *limit* (bad math puns 😅). Please show it some love and let me know your thoughts/ questions/ struggles below! 🙌🏽

[wthxrsh]

Maths is scary but when you learn maths with Data Science perspective, it actually starts making sense and you find it interesting!

[LukeBarousse]

The "Math for Data Science" guide, we all need!!! 🔥🙌🏼🔥

Thu, the level of work and detail you put into this video is insane. 🤯 More vids like this, plz! 👏🏼

[lpac]

Great tips! Especially about your points around a note taking system and coding the math. When I started my journey, it took me a few months to realize my note taking system was horrid that I wasn’t retaining and needed a better note taking system. I also moved to electronic vs hand written. Coding the algorithm from scratch in python was also key. Patience was so key, many times I wanted to jump to the end result w/o understanding the algorithm itself.

[intheshademusical]

I just discovered your excellent videos and I binge-watched them last night. I love the way you explain things. I was thrilled that you recommended my "Data Science Math Skills" course - it's my best effort to leave out all the hard stuff beginners don't need to get started.

[andrewign]

I found discrete mathematics, which I *did not* learn in university, irreplaceably useful for my CS courses. (Udemy, Amoure.)
I did actually complete "Mathematics for Machine Learning". Great style of teaching! Wouldn't be able to do it on my own.

[philiprhome]

for calculus and linear algebra, they are more important in machine learning especially deep leaning

[deterdinghenry]

I don't know a thing about DS. But I know Math and how to study it, and in fact I worked many years in Financial Math and taught it for ten years at the Universidad de Buenos Aires. So, a tip: if you try to study Math as in this video, with the book in front of you and a little notepad at the left, —being you're righthhanded—you'll hate Math for life. Try then to do it reverse: a big notepad in front of you, the pen in your hand, the book on the screen. A virtual one, of course. Then, learn one math idea, eigenvectors for instance, and start to work in your notebook over it. And write Math. 90% time writing, thinking, explaining; and just about 10% time reading. And you'll succeed. Otherwise, I completely share your pov about learning. Greetings.

[MediumSizedPizza]

Great job verbalizing the learning process! This video took thoughts out of my brain 🧠 in a really structured way. 🙌💯

[Rocky-wtlz]

The timing on this. I've just picked up a book on linear algebra and Calc and have been grinding at it.

I love your car analogy, tho I'd like to expand on that. Hobbyist aside, it can be useful to have a further understanding on how stuff works because say, if your car breaks down in the middle of the road, knowing how some parts works can help you diagnose and sometimes even fix the issue you run into yourself. Ofc this isn't necessary to drive a car, but it's a parallel progression imo, if you know more, you have an edge over people who don't 😎

[anuragthakur]

Your videos are truly amazing the editing, quality
You even told us about the topics which we are supposed to dig deep into along with the tips

[flwi]

Thanks a lot for putting together this guide! It gave me a good overview of what I might need to dive into if I decide to tackle the new course by Andrew Ng. Highly appreciate your effort! 🙏

[johnk]

Fabulous. I especially liked the animations of linear algebra things that flew by.

I also liked your brief discussion of imposter syndrome ("don't act on it") vs enthusiasm ("take action on it").

Finally, I appreciated you saying that you're not smart enough to do research... for me, realizing that I am really not as smart as I thought or imagined myself to be has been helpful in "getting real", without falling down the pit of imposter syndrome.

[kamertonaudiophileplayer]

It is an amazing channel. Every data scientist should start from here. Keep your tremendous effort for the very important topic.

[SundasKhalid]

Love the video and effort you put into it 👏

[jesuis_jo]

All the people that currently is teaching, (making videos or producing material), know about how hard is to produce it, so thank you. I encourage you to continue doing it, best. Jo

[quangphu]

Thank you for such great tips in learning Mathematics for DS, Thu. I was lost in the maze of math and statistics for DS; your video did shed some light on the learning path and how to structure these complicated concepts for later reference

[ankithguzz]

Brilliant summary! This is the first time I've come across your channel and I have subscribed now!

[senthilkumarpalanisamy]

This is one of the best clear cut video, people who scare about the internal math of ml model can get confidence to learn after watching this video. Thanks much sharing madam, please do post more !!!!

[YuTv]

Youre not wrong. Theoretical Math, Physics, Chemistry and Engineering are wayy harder than programming because youre discovering/inventing the science wile trying to solve daily tech problems. Programmer dont understand this.