Mathematics for Data Science (Series)
This is the pilot post of blog post series ‘Mathematics for Data Science’, this post covers the context, table of content & links to upcoming posts of this series topic-wise.
For Data Science & Machine Learning beginners, it is essential to develop a mathematical understanding of underlying concepts. Data Science is simply an evolved version of statistics and mathematics (combined with programming and business logic). Many data scientists struggle to explain intrinsic details of predictive models. More than just deriving accuracy, understanding & interpreting every metric, calculation behind that accuracy is important.
Even for a lot of higher level courses/books on Machine Learning/Data Science, you need to brush up on the basics in mathematics, but if you refer these topics in textbooks, you will find the Data Science context missing. This blog series targets to bridge that gap, it will cover all the underlying mathematics, to build an intuitive understanding, and you will be able to relate it to Machine Learning and Data Science.
Before continuing with this post, if you are loving the content, check out my post on ‘How to launch your DS/AI Career in 12 weeks?’
Context & Introduction
Pilot post covers the context & introduction to the blog-post series.
Linear Algebra for Data Science
Second post covers what, why & how part of Linear Algebra in context of Data Science/Machine Learning.
Multivariate Calculus for Data Science
Third post covers what, why & how part of Multivariate Calculus in context of Data Science/Machine Learning.
Probability & Statistics for Data Science
Last post, which is a blog-post series in itself, covers what, why & how part of Probability & Statistics in context of Data Science/Machine Learning.
I am writing a blog-post series named ‘Data Science: The Complete Reference’, which will cover all concepts & topics related to Data Science, so please follow me & stay tuned.
Ankit Rathi is an AI architect, published author & well-known speaker. His interest lies primarily in building end-to-end AI applications/products following best practices of Data Engineering and Architecture.