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Do I need to know calculus for data science?
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Whether you loved or hated it in college, calculus pops up in numerous places in data science and machine learning. It lurks behind the simplelooking analytical solution of an ordinary least squares problem in linear regression or embedded in every back-propagation your neural network makes to learn a new pattern. It is an extremely valuable skill to add to your repertoire. Here are the topics to learn:
- Functions of a single variable, limit, continuity, differentiability
- Mean value theorems, indeterminate forms, L'Hospital's rule
- Maxima and minima
- Product and chain rule
- Taylor's series, infinite series summation/integration concepts
- Fundamental and mean value-theorems of integral calculus, evaluation of definite and improper integrals
- Beta and gamma functions
- Functions of multiple variables, limit, continuity, partial derivatives
- Basics of ordinary and partial differential equations

Where You Might Use It
Ever wondered how exactly a logistic regression algorithm is implemented? There is a high chance it uses a method called "gradient descent" to find the minimum loss function. To understand how this works, you need to use concepts from calculus: gradient, derivatives, limits, and chain rule.

by Platinum (132,156 points)

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