Calculus through Data & Modelling: Vector Calculus (Coursera)

Calculus through Data & Modelling: Vector Calculus (Coursera)

This course continues your study of calculus by focusing on the applications of integration to vector valued functions, or vector fields. These are functions that assign vectors to points in space, allowing us to develop advanced theories to then apply to real-world problems. We define line integrals, which can be used to fund the work done by a vector field. We culminate this course with Green's Theorem, which describes the relationship between certain kinds of line integrals on closed paths and double integrals.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

In the discrete case, this theorem is called the Shoelace Theorem and allows us to measure the areas of polygons. We use this version of the theorem to develop more tools of data analysis through a peer reviewed project.
Upon successful completion of this course, you have all the tools needed to master any advanced mathematics, computer science, or data science that builds off of the foundations of single or multivariable calculus.
Course 4 of 4 in the Integral Calculus through Data and Modeling Specialization

Syllabus

WEEK 1
Vector Fields and Line Integrals
In this module, we define the notion of a Vector Field, which is a function that applies a vector to a given point. We then develop the notion of integration of these new functions along general curves in the plane and in space. Line integrals were developed in the early19th century initially to solve problems involving fluid flow, forces, electricity, and magnetism. Today they remain at the core of advanced mathematical theory and vector calculus.

WEEK 2
The Fundamental Theorem for Line Integrals
In this module, we introduce the notion of a Conservative Vector Field. In vector calculus, a conservative vector field is a vector field that is the gradient of some function f, called the potential function. Conservative vector fields have the property that the line integral is path independent, which means the choice of any path between two points does not change the value of the line integral.
Conversely, path independence of the line integral is equivalent to the vector field being conservative. We then state and formalize an important theorem about line integrals of conservative vector fields, called the Fundamental Theorem for Line Integrals. This will allow us to show that for a conservative system, the work done in moving along a path in configuration space depends only on the endpoints of the path.

WEEK 3
Green's Theorem
In this module we state and apply a main tool of vector calculus: Green's Theorem. Green's theorem gives a relationship between the line integral of a two-dimensional vector field over a closed path in the plane and the double integral over the region it encloses. The fact that the integral of a two-dimensional conservative field over a closed path is zero is a special case of Green's theorem.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Physics 101 - Rotational Motion and Gravitation (Coursera) Coursera
Rice University

Physics 101 - Rotational Motion and Gravitation (Coursera)

This course serves as an introduction to the physics of rotational motion and gravitation. Upon completion, learners will have an understanding of how mathematical laws and conservation principles describe the motions and interactions of objects all around us. They will gain experience in solving physics problems with tools such as graphical analysis, algebra, vector analysis, and calculus.

Oct 12th 2026
4 Weeks
Applied Calculus with Python (Coursera) Coursera
Johns Hopkins University

Applied Calculus with Python (Coursera)

This course is designed for the Python programmer who wants to develop the foundations of Calculus to help solve challenging problems as well as the student of mathematics looking to learn the theory and numerical techniques of applied calculus implemented in Python. By the end of this course, you will have learned how to apply essential calculus concepts to develop robust Python applications that solve a variety of real-world challenges.

Oct 26th 2026
5-12 Weeks
Foundations: Data, Data, Everywhere (Coursera) Coursera
Google

Foundations: Data, Data, Everywhere (Coursera)

This is the first course in the Google Data Analytics Certificate. These courses will equip you with the skills you need to apply to introductory-level data analyst jobs. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Oct 6th 2026
5-12 Weeks
Calculus through Data & Modelling: Integration Applications (Coursera) Coursera
Johns Hopkins University

Calculus through Data & Modelling: Integration Applications (Coursera)

This course continues your study of calculus by focusing on the applications of integration. The applications in this section have many common features. First, each is an example of a quantity that is computed by evaluating a definite integral. Second, the formula for that application is derived from Riemann sums. Rather than measure rates of change as we did with differential calculus, the definite integral allows us to measure the accumulation of a quantity over some interval of input values.

Oct 12th 2026
4 Weeks
Deep Learning and Reinforcement Learning (Coursera) Coursera
IBM

Deep Learning and Reinforcement Learning (Coursera)

This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning.

Oct 12th 2026
5-12 Weeks
Introduction to Logic (Coursera) Coursera
Stanford University

Introduction to Logic (Coursera)

This course is an introduction to Logic from a computational perspective. It shows how to encode information in the form of logical sentences; it shows how to reason with information in this form; and it provides an overview of logic technology and its applications - in mathematics, science, engineering, business, law, and so forth.

Oct 22nd 2026
5-12 Weeks
Calculus: Single Variable Part 2 - Differentiation (Coursera) Coursera
University of Pennsylvania

Calculus: Single Variable Part 2 - Differentiation (Coursera)

Calculus is one of the grandest achievements of human thought, explaining everything from planetary orbits to the optimal size of a city to the periodicity of a heartbeat. This brisk course covers the core ideas of single-variable Calculus with emphases on conceptual understanding and applications. The course is ideal for students beginning in the engineering, physical, and social sciences.

Oct 26th 2026
3 Weeks
Process Data from Dirty to Clean (Coursera) Coursera
Google

Process Data from Dirty to Clean (Coursera)

This is the fourth course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. In this course, you’ll continue to build your understanding of data analytics and the concepts and tools that data analysts use in their work. You’ll learn how to check and clean your data using spreadsheets and SQL as well as how to verify and report your data cleaning results. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Oct 6th 2026
5-12 Weeks
Generalized Linear Models and Nonparametric Regression (Coursera) Coursera
University of Colorado Boulder

Generalized Linear Models and Nonparametric Regression (Coursera)

In the final course of the statistical modeling for data science program, learners will study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semi-parametric generalized additive models (GAMs). Emphasis will be placed on a firm conceptual understanding of these tools.

Oct 12th 2026
4 Weeks