How To ACTUALLY Learn The Math For Data Science

In this video, I am going to explain to you the ACTUAL maths you need to be a data scientist.
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Video Transcript: medium.com/towards-data-scien...
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🔗 LINKS MENTIONED:
Practical Statistics for Data Scientists: Practical Statistics for Data Scientists: amzn.to/42Xbd9N (affiliate link)
Mathematics for Machine Learning: amzn.to/48C9Uht (affiliate link)
Outline of statistics: en.wikipedia.org/wiki/Outline...
freeCodeCamp Statistics: • Statistics - A Full Un...
freeCodeCamp Calculus: • Calculus 1 - Full Coll...
freeCodeCamp Linear Algebra: • Linear Algebra - Full ...
Linear Algebra for Machine Learning and Data Science: www.coursera.org/learn/machin...
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TIMESTAMPS
0:00 Intro
0:41 What To Learn
2:18 Probability & Statistics
6:11 Calculus
9:10 Linear Algebra
WHO AM I?
Hi, I'm Egor! I am a Data Scientist with a master's in Physics currently living in London. I share data science tutorials, advice and general tech topics!
DISCLAIMERS
Views and opinions are my own and do not represent or reflect the opinions of my current or past employer or any organisations I am associated with.

[@alexchan7654]

Your video series is a god sent for someone trying to break into data science, seriously, thank you 🙏🏼

[@egorhowell]

Thanks Alex, really appreciate it!

[@ganaparthileela1137]

Also go and check maths for data science in Geeks of geeks, the syllabus description is in good structure. 1. Linear Algebra & Matrix: Linear Combinations Vectors & Matrices Quantities Vectors Matrices Transpose Matrix Inverse Matrix Trace of a Matrix Determinant Matrix Dot Product Linear Mappings Functions Measurements Linear Mapping Composition Vector Spaces Formal Rules Algebraic structures Vector subspaces of a Linear Mapping Data Redundancy Linear dependence Basis and dimension Dimension of matrix spaces Fundamental Theorem of Linear Algebra Data Information Partition of linear mapping domain and codomain Data Partitioning Mappings as data The Singular Value Decomposition (SVD) 2. Probability & Statistics: Probability Continuous and Discrete Random variable Central Limit Theorem Probability distributions - Binomial, Poisson, Normal Statistics Mean, Median and Mode Standard Deviation and Variance Similarity measures - Pearson, Cosine, Spearman Hypothesis testing T-test Paired T-test p-value F-Test z-test 3. Calculus: Maxima and minima Mean value theorem Product and chain rule Taylor’s series Derivatives Gradients of Matrices Backpropagation Gradient Descent Algorithm Useful Identities for Gradient computation Higher-Order Derivatives Multivariate Taylor Series Fourier Transformations Area under the curve you should also have some discrete math knowledge and that is listed below. 4. Geometry & Graph Knowledge Deals with the angles, measurements, and proportions of ordinary objects To understand Distribution plots, Scatter plots, Boxplot (quartile, percentile) To visualize the graphs and ability to generate insights from them. Convex and Concave graphs and their properties

[@egorhowell]

They have great resources as well!

[@ganaparthileela1137]

Could u also please briefly explain about Mlops, generative AI and LLMS as well.@@egorhowell

[@egorhowell]

Hey, yeah I might do a video on it in the future!

[@ganaparthileela1137]

@@egorhowell definitely bro

[@joelwillis2043]

if this is how the incoming competition thinks of math I have nothing to worry about

[@harrysdatajourney]

Really great video Egor. This is super helpful!

[@egorhowell]

Thank you Harry!

[@jsc0625]

This is great, thank you! Really clear explanations

[@egorhowell]

Thank you very much!

[@zanyatta1]

Excellent video, thank you:)

[@egorhowell]

Glad you liked it!

[@yerkomunozescobar581]

Thank you! Great video :)

[@egorhowell]

Thank you!!

[@Suman522i1]

that i was looking for, thanks :)

[@egorhowell]

glad I could help!

[@datawithtess]

Another great video from my friend

[@egorhowell]

Thank you very much :)

[@muhammedesrefhasan]

Man, I appreciate 🙏

[@egorhowell]

Glad you liked it!

[@yyy-ml1yg]

great video

[@egorhowell]

Thank you!

[@supahottfire]

Hi I really liked your video. However, I think that the Mathametics for Machine Leanring book is extremely complex. I come from an engineering background but trying to do data science now. Are there any other books you would recxomend as a one stop shop? Are the videos a one stop shop to learn that subject ? Any answer woild he helpful.

[@egorhowell]

Hey, yeah Mathematics for Machine Learning does into a lot of detail and can be overwhelming for some people which I can appreciate. I use it more like a reference text to be honest. The practical statistics for data science is excellent, and essential Math for Data Science I have also heard good things about, but haven't used it myself yet. Hope this helps!

[@AliceShisori]

I'm actually a dropout and have mostly worked jobs with absolutely zero math included, I've been learning python and sql and while I'm digesting slowly thus far, do you think there's really a pathway to learn math from scratch for people like me? I most likely will never reach your level but I just need to reach the "bare minimum" for this field, or for any kind of analytics really. Been spending much efforts to learn so I just don't wanna give up this soon. really appreciate the videos bro

[@egorhowell]

Hey, it depends on your background. I would say the "bare minimum" is what you learn in the final years of school. For example, if you are in the UK, A level maths would be sufficient in my opinion. There is an always a pathway, it may just take you a bit longer. The resources I mentioned are still applicable to people like you! You may just have to work slower and look up things you are unfamiliar with along the way. And, I know it can be tough, but you have the right attitude in not giving up! It takes time, and you have to trust that it will work in the end. Take it one step at a time, learn Python and SQL like you are, then move onto the maths sections. Let me know if you need anything else :)

[@AliceShisori]

@@egorhowell thank you very much for reading my comment and give me a serious answer. I did rewatch your video and will try to stick to it step by step on my own progress. I'm not very hopeful since I know this field and maybe IT in general is always fast pacing and evolving very often but in my country (Vietnam) even though it's already a trendy career path right now most colleges and universities still haven't taught it yet so at least the overall saturation and competition isn't massively high. I think in your vids you explain the key points nicely and easy to understand which is very helpful, especially for people who aren't adept or fast learners like me. Keep doing the good work and I think your channel will grow exponentially!!!

[@egorhowell]

@@AliceShisori Thank you Alice, I hope all goes well. Remember to enjoy the process!

[@howlpanda]

I share the same sentiment - my background is in food science, so I took calculus and some linear algebra, but this was over 15 years ago! (I learned some R too) I'm keen to do this career change because I think my entrepreneurial experience will come in handy and make me a good data scientist, but not being in school for that long and now relearning math while also learning how to code for the first time feels daunting. I'm currently doing an online MSc at St. Andrews which is great because they don't require a CS background. But for many folks like me, I think we just have to keep our head up high, and take it slowly. I am realizing I'm going at it with a slower pace than my class mates, and that's ok. Thank you for these videos, it's not as anxiety-inducing as the others I've watched haha

[@howlpanda]

PS. I also have the O'Reilly books, I agree that they're great! I also got some books from Amazon to help with refreshing Linear Algebra, they're the ones by Richard Han. Basics, with lots of exercises you can practice with. My textbook for my ML Algorithms course is Machine Learning Refined: Foundations, Algorithms, and Applications by Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos

[@kumarshubh9786]

this maths roadmap can be for ml?

[@egorhowell]

Yes, I kind've use DS and ML interchangeably. (I know they are different)

[@ChristopherCordero-up7xq]

Do I need to learn the same math for data analytics?

[@egorhowell]

Good question. Its similar, but not quite the same. For example, as an analyst you will unlikely be building ML models. So, you will using calculus a bit less. For data analytics, the main area you should focus on is probability and statistics. Hope this helps!

[@ChristopherCordero-up7xq]

@@egorhowell thanks for reply bro, any course you recommend for probability and calculus? thanks.

[@egorhowell]

Hey, the one linked in the video is good. The textbook and video from freeCodeCamp :)

[@UMARTHEDON]

love from India 🇮🇳

[@egorhowell]

Cheers mate!