Master calculus 2 using Python: integration, intuition, code [4/2025, ENG]

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Master calculus 2 using Python: integration, intuition, code

Год выпуска: 4/2025
Производитель: Udemy
Сайт производителя: https://www.udemy.com/course/pycalc2_x/
Автор: Mike X Cohen
Продолжительность: 18h 49m 59s
Тип раздаваемого материала: Видеоурок
Язык: Английский
Субтитры: Отсутсвуют
Описание:
[spoiler="What you'll learn"]
Calculus
Integration
Python
numpy
sympy
matplotlib
scipy
Visualization
Mathematics
Geometry
Proofs
Applied integration
Riemann sums
[/spoiler]
Requirements Calculus 1 (differentiation)
Some python coding (numpy, matplotlib, sympy) Description

The beauty and power of integral calculus

If Calculus 1 was about understanding change, then Calculus 2 is about accumulation: how small changes stack up to build area, volume, probability, and complexity. Integration is where mathematics meets imagination — it's where the abstract and the physical merge.

From Riemann sums to probability distributions, from arc lengths to solids of revolution, integral calculus provides the tools to describe, quantify, and visualize everything from the motion of particles to the structure of data. It’s a gateway to multivariable calculus, mathematical modeling, and data science.

And it's not just a theoretical subject. Integration is foundational to fields including physics, engineering, machine learning, quantitative finance, and statistics. If you want to understand the algorithms behind data science or build the mathematical foundation needed for AI, you need to understand integrals.

So whether you're here to strengthen your math background, prep for a university course, or just challenge your brain — welcome.

Why learn integral calculus?

There are three reasons to study integrals: Real-world relevance: Integral calculus is used in nearly every STEM discipline — especially in areas like physics, economics, biology, and computer science. You’ll learn how to compute volumes, model systems, and understand distributions — even extend into multivariable integration.
Cognitive training: Integration requires both precision and creativity. You’ll develop deep reasoning skills as you learn to connect concepts, derive formulas, and implement algorithms. It’s like mental weightlifting.
Math as a lifelong hobby: Instead of scrolling through another social-media feed, why not learn how to calculate the surface area of a rotating shape or simulate a probability distribution from scratch?? This course is a good way to keep your mind sharp and intellectually active. Learn calculus the old way, or learn it the modern way?

You could learn integration by watching a lecture filled with blackboard equations and hoping it sinks in. Or you could take a more interactive, hands-on approach.

This course follows the principle that “you can learn a lot of math with a bit of coding.”

You'll use Python — especially NumPy, SymPy, and Matplotlib — to visualize integrals, implement numerical approximations, explore convergence, and gain intuition for the fundamental ideas of calculus.

There are three key reasons to use Python in this course: Deeper insight: Code helps make abstract concepts concrete. You’ll build simulations and generate visuals that bring integrals to life.
Practical skills: Numerical integration and symbolic computation are essential tools in applied mathematics and data science.
Active learning: Coding forces you to think precisely and analytically, which leads to better retention and understanding. So this is just about coding integrals?

Not at all. This isn’t a programming course, and it's not about using Python to sidestep the math. The goal is to use code as a thinking tool — to help you understand what's going on mathematically, not to replace understanding with computation.

In this course, you'll learn both how to integrate — with techniques like u-substitution, integration by parts, partial fractions — and why integration works, from multiple conceptual perspectives: geometric, analytic, and numerical.

You’ll also explore integration in surprising contexts: creating art from math, modeling randomness with probability distributions, and measuring volumes and surface areas of 3D objects.
[spoiler="Are there exercises?"]
Yes — lots of them! Almost every theoretical concept includes one or more exercises for you to solve, and I walk through all of the solutions step-by-step.

Even better: You’ll learn how to create your own calculus exercises, complete with solutions, so you can tailor your practice to exactly what you need. Think of it as building your own personal study plan — powered by Python and guided by your intuition.
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Is this the right course for you?

This course is designed for learners who already have some experience with derivatives (e.g., from my Calculus 1 course or a university-level intro class). If you're ready to go deeper — into integration, area, volume, probability, and multivariable calculus — then this course is for you.

It's particularly well-suited for: University students or autodidacts learning integral calculus
Data scientists, engineers, or coders wanting to strengthen their math foundations
Lifelong learners who want a challenging and engaging intellectual pursuit No course is right for everyone — so check out the preview videos and reviews before enrolling. And remember: Udemy offers a 30-day money-back guarantee, so there’s no risk if you decide the course isn’t a good fit.
[spoiler="Who this course is for:"]
Calculus students looking for better educational material
Scientists who use calculus concepts in their research
Anyone curious about the amazing beauty of calculus on computers!
Machine-learning and A.I. enthusiasts
Engineers who simulate and measure experimental results
Data scientists (current or aspiring)
Mathematicians who want to implement math in code
Coders who want to use Python to learn math
Anyone looking for an intellectually stimulating hobby
[/spoiler]
Here is the link to the first part of this course: [Udemy, Mike X Cohen] Master calculus 1 using Python: derivatives and applications [1/2025, ENG]

Формат видео: MP4
Видео: avc, 1920x1080, 16:9, 30.000 к/с, 1645 кб/с
Аудио: aac lc sbr, 44.1 кгц, 62.8 кб/с, 2 аудио
[spoiler="MediaInfo"]
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[spoiler="Скриншоты"]

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