Applied Calculus with Python
Learn essential calculus concepts and implement them in Python for real-world problem-solving in data science and AI.
Learn essential calculus concepts and implement them in Python for real-world problem-solving in data science and AI.
This comprehensive course combines calculus theory with practical Python programming skills. Designed for both Python programmers seeking to develop calculus foundations and mathematics students looking to implement calculus concepts in Python, the course covers a wide range of topics. You'll learn to apply essential calculus concepts to develop robust Python applications that solve various real-world challenges. The curriculum includes functions, rates of change, derivatives, derivative rules and applications, and integrals. Through video lectures, readings, worked examples, and hands-on Python coding, you'll gain practical experience in solving equations, working with functions, and computing and applying derivatives and integrals. This course is ideal for those interested in applied math, data science, cybersecurity, or artificial intelligence, as well as those needing a refresher in calculus or Python coding.
4.8
(34 ratings)
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What you'll learn
Understand and implement basic Python programming for mathematical computations
Master the concept of functions and their graphs, including exponential and logarithmic functions
Learn to compute limits and derivatives both analytically and using Python
Apply derivative rules to solve optimization problems and analyze function behavior
Understand the basics of integral calculus and use Python for numerical integration
Develop skills in using SymPy for symbolic mathematics computations
Skills you'll gain
This course includes:
571 Minutes PreRecorded video
9 quizzes,4 ungraded labs
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This course offers a comprehensive exploration of applied calculus using Python. Students begin with an introduction to Python programming and SymPy, a symbolic mathematics library. The curriculum then delves into functions, including linear, quadratic, exponential, and logarithmic functions, both from a theoretical perspective and their implementation in Python. Students learn about rates of change, limits, and derivatives, including how to find them analytically and using Python. The course covers derivative rules and their applications, such as finding maxima and minima, and optimization problems. The final module introduces integral calculus, including definite integrals, antiderivatives, and the Fundamental Theorem of Calculus. Throughout the course, students engage with practical examples and use Python to solve real-world problems, bridging the gap between mathematical theory and computational application.
Introduction to Python
Module 1 · 1 Hours to complete
Functions
Module 2 · 5 Hours to complete
Rates of Change and the Derivative
Module 3 · 5 Hours to complete
Derivative Rules and Applications
Module 4 · 5 Hours to complete
Accumulated Change and Integrals
Module 5 · 4 Hours to complete
Fee Structure
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Instructor
Associate Teaching Professor and Director of Online Programs at Johns Hopkins University
Dr. Joseph W. Cutrone is an Associate Teaching Professor and the Director of Online Programs at Johns Hopkins University, where he specializes in mathematics education. He earned his PhD in Mathematics from Johns Hopkins University, with research interests focused on higher-dimensional algebraic geometry. Since 2006, Dr. Cutrone has taught mathematics at various levels, both online and in traditional classroom settings, holding positions at Johns Hopkins, Northwestern University, Towson University, and Goucher College. His commitment to education is reflected in his extensive teaching portfolio, which includes courses such as Algebra, Applied Calculus with Python, and Linear Algebra. Dr. Cutrone is also active in online education, contributing to platforms like Coursera. His scholarly work includes several publications on topics related to algebraic geometry and has been recognized for its impact within the mathematical community.
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4.8 course rating
34 ratings
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