Complex Analysis (saylor.org)

Offered by Saylor.org,
Complex Analysis (saylor.org)

This course is an introduction to complex analysis, or the theory of the analytic functions of a complex variable. Put differently, complex analysis is the theory of the differentiation and integration of functions that depend on one complex variable.

Such functions, beautiful on their own, are immediately useful in Physics, Engineering, and Signal Processing. Because of the algebraic properties of the complex numbers and the inherently geometric flavor of complex analysis, this course will feel quite different from Real Analysis, although many of the same concepts, such as open sets, metrics, and limits will reappear. Simply put, you will be working with lines and sets and very specific functions on the complex plane—drawing pictures of them and teasing out all of their idiosyncrasies. You will again find yourself calculating line integrals, just as in multivariable calculus. However, the techniques you learn in this course will help you get past many of the seeming dead-ends you ran up against in calculus. Indeed, most of the definite integrals you will learn to evaluate in Unit 7 come directly from problems in physics and cannot be solved except through techniques from complex variables.
We will begin by studying the minimal algebraically closed extension of real numbers: the complex numbers. The Fundamental Theorem of Algebra states that any non-constant polynomial with complex coefficients has a zero in the complex numbers. This makes life in the complex plane very interesting. We will also review a bit of the geometry of the complex plane and relevant topological concepts, such as connectedness.
In Unit 2, we will study differential calculus in the complex domain. The concept of analytic or holomorphic function will be introduced as complex differentiability in an open subset of the complex numbers. The Cauchy-Riemann equations will establish a connection between analytic functions and differentiable functions depending on two real variables. In Unit 3, we will review power series, which will be the link between holomorphic and analytic functions. In Unit 4, we will introduce certain special functions, including exponentials and trigonometric and logarithmic functions. We will consider the Möbius Transformation in some detail.
In Units 5, 6, and 7 we will study Cauchy Theory, as well as its most important applications, including the Residue Theorem. We will compute Laurent series, and we will use the Residue Theorem to evaluate certain integrals on the real line which cannot be dealt with through methods from real variables alone. Our final unit, Unit 8, will discuss harmonic functions of two real variables, which are functions with continuous second partial derivatives that satisfy the Laplace equation, conformal mappings, and the Open Mapping Theorem.
Course Requirements:
Have completed Single-Variable Calculus I, Single-Variable Calculus II, Multivariable Calculus, Linear Algebra, and Differential Equations or their equivalents. Completion of Real Analysis I or its equivalent is recommended, but concurrent enrollment is acceptable.

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

Related Courses

Real Analysis II (saylor.org) Saylor Academy
Saylor.org

Real Analysis II (saylor.org)

Real Analysis II is the sequel to Saylor’s Real Analysis I, and together these two courses constitute the foundations of real analysis in mathematics. In this course, you will build on key concepts presented in Real Analysis I, particularly the study of the real number system and real-valued functions defined on all or part (usually intervals) of the real number line.

Legacy Course
Self-Paced
Cálculo Diferencial e Integral unidos por el Teorema Fundamental del Cálculo (Coursera) Coursera
Tecnológico de Monterrey

Cálculo Diferencial e Integral unidos por el Teorema Fundamental del Cálculo (Coursera)

Los cursos de Cálculo Diferencial y Cálculo Integral tradicionalmente se ofrecen separados y respetando ese orden. El primero estudia la derivada, y el segundo, la integral, siendo este momento en el que aparece el Teorema Fundamental del Cálculo (TFC) para establecer la relación entre ambos conceptos. En el presente curso vamos a hacer una diferencia: introduciremos la derivada y la integral como conceptos relacionados desde un principio.

Aug 24th 2026
5-12 Weeks
Probability - The Science of Uncertainty and Data (edX) EdX
MIT,MITx

Probability - The Science of Uncertainty and Data (edX)

Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference. The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.

Sep 1st 2026
13-24 Weeks
Linear Algebra II (saylor.org) Saylor Academy
Saylor.org

Linear Algebra II (saylor.org)

Linear algebra is the study of vector spaces and linear mappings between them. In this course, we will begin by reviewing topics you learned in Linear Algebra I, starting with linear equations, followed by a review of vectors and matrices in the context of linear equations.

Legacy Course
Self-Paced
Differential Equations (saylor.org) Saylor Academy
Saylor.org

Differential Equations (saylor.org)

Differential equations are, in addition to a topic of study in mathematics, the main language in which the laws and phenomena of science are expressed. In basic terms, a differential equation is an expression that describes how a system changes from one moment of time to another, or from one point in space to another.

Legacy Course
Self-Paced
Introduction to Statistics (saylor.org) Saylor Academy
Saylor.org

Introduction to Statistics (saylor.org)

The purpose of this course is to introduce you to the subject of statistics as a science of data. There is data abound in this information age; how to extract useful knowledge and gain a sound understanding in complex data sets has been more of a challenge. In this course, we will focus on the fundamentals of statistics, which may be broadly described as the techniques to collect, clarify, summarize, organize, analyze, and interpret numerical information.

Self Paced
Self-Paced
Precalculus I (saylor.org) Saylor Academy
Saylor.org

Precalculus I (saylor.org)

Precalculus I is designed to prepare you for Precalculus II, Calculus, Physics, and higher math and science courses. In this course, the main focus is on five types of functions: linear, polynomial, rational, exponential, and logarithmic. In accompaniment with these functions, you will learn how to solve equations and inequalities, graph, find domains and ranges, combine functions, and solve a multitude of real-world applications.

Legacy Course
Self-Paced