Digital Signal Processing 1: Basic Concepts and Algorithms (Coursera)

Digital Signal Processing 1: Basic Concepts and Algorithms (Coursera)

Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices.

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

In this series of four courses, you will learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. Hands-on examples and demonstration will be routinely used to close the gap between theory and practice.
To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.
Course 1 of 4 in the Digital Signal Processing Specialization.
What You Will Learn

  • The nature of discrete-time signals
  • Discrete-time signals are vectors in a vector space
  • Discrete-time signals can be analyzed in the frequency domain via the Fourier transform

Syllabus

WEEK 1
Module 1.1: Digital Signal Processing: the Basics
Introduction to the notation and basics of Digital Signal Processing

WEEK 2
Module 1.2: Signal Processing Meets Vector Space
Modeling signals as vectors in an appropriate vector space. Using linear algebra to express signal manipulations.

WEEK 3
Module 1.3: Fourier Analysis: the Basics
The fundamental concepts behind the Fourier transform and the frequency domain

WEEK 4
Module 1.4: Fourier Analysis: More Advanced Tools
Delving deeper in the world of Fourier analysis.

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

Related Courses

Introducción a la Minería de Datos (Coursera) Coursera
Pontificia Universidad Católica de Chile

Introducción a la Minería de Datos (Coursera)

En este curso, aprenderás de manera gradual y práctica los conceptos básicos de Minería de Datos, junto a los algoritmos más utilizados hoy en día. Al finalizar el curso, serás capaz de entender la importancia de manejar la información y de explorar por ti mismo distintas bases de datos reales. Este curso es el primer paso para convertirte en un/a profesional con habilidades básicas de un científico de datos o Data Scientist, de manera tal que puedas abrirle la puerta al futuro.

Aug 10th 2026
5-12 Weeks
Computer Science: Programming with a Purpose (Coursera) Coursera
Princeton University

Computer Science: Programming with a Purpose (Coursera)

The basis for education in the last millennium was “reading, writing, and arithmetic;” now it is reading, writing, and computing. Learning to program is an essential part of the education of every student, not just in the sciences and engineering, but in the arts, social sciences, and humanities, as well. Beyond direct applications, it is the first step in understanding the nature of computer science’s undeniable impact on the modern world.

Aug 10th 2026
5-12 Weeks
Blockchain Scalability and its Foundations in Distributed Systems (Coursera) Coursera
The University of Sydney

Blockchain Scalability and its Foundations in Distributed Systems (Coursera)

Blockchain promises to disrupt industries once it will be efficient at large scale. In this course, you will learn how to make blockchain scale. You will learn about the foundational problem of distributed computing, consensus, that is key to create blocks securely. By illustrating limitations of mainstream blockchains, this course will indicate how to improve the technology in terms of security and efficiency. In particular, this course will help you: understand security vulnerabilities of mainstream blockchains; design consensus algorithms that tolerate attacks, and; design scalable blockchain systems.

Aug 3rd 2026
5-12 Weeks
The Fundamental of Data-Driven Investment (Coursera) Coursera
Sungkyunkwan University - SKKU

The Fundamental of Data-Driven Investment (Coursera)

In this course, the instructor will discuss the fundamental analysis of investment using R programming. The course will cover investment analysis topics, but at the same time, make you practice it using R programming. This course's focus is to train you to do the elemental analysis for investment management that you might need to do in your job every day. Additionally, the study note to do using Python programming will be provided.

Aug 10th 2026
4 Weeks
Parallel programming (Scala 2 version) (Coursera) Coursera
École Polytechnique Fédérale de Lausanne

Parallel programming (Scala 2 version) (Coursera)

With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library.

Aug 10th 2026
4 Weeks
Operations Research (2): Optimization Algorithms (Coursera) Coursera
National Taiwan University

Operations Research (2): Optimization Algorithms (Coursera)

Operations Research (OR) is a field in which people use mathematical and engineering methods to study optimization problems in Business and Management, Economics, Computer Science, Civil Engineering, Electrical Engineering, etc. The series of courses consists of three parts, we focus on deterministic optimization techniques, which is a major part of the field of OR. As the second part of the series, we study some efficient algorithms for solving linear programs, integer programs, and nonlinear programs.

Aug 3rd 2026
5-12 Weeks
Probabilistic Graphical Models 3: Learning (Coursera) Coursera
Stanford University

Probabilistic Graphical Models 3: Learning (Coursera)

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

Aug 3rd 2026
5-12 Weeks
Excel/VBA for Creative Problem Solving, Part 2 (Coursera) Coursera
University of Colorado Boulder

Excel/VBA for Creative Problem Solving, Part 2 (Coursera)

Excel/VBA for Creative Problem Solving, Part 2" builds off of knowledge and skills obtained in "Excel/VBA for Creative Problem Solving, Part 1" and is aimed at learners who are seeking to augment, expand, optimize, and increase the efficiency of their Excel spreadsheet skills by tapping into the powerful programming, automation, and customization capabilities available with Visual Basic for Applications (VBA).

Aug 10th 2026
4 Weeks
Decision Making and Reinforcement Learning (Coursera) Coursera
Columbia University

Decision Making and Reinforcement Learning (Coursera)

This course is an introduction to sequential decision making and reinforcement learning. We start with a discussion of utility theory to learn how preferences can be represented and modeled for decision making. We first model simple decision problems as multi-armed bandit problems in and discuss several approaches to evaluate feedback. We will then model decision problems as finite Markov decision processes (MDPs), and discuss their solutions via dynamic programming algorithms. We touch on the notion of partial observability in real problems, modeled by POMDPs and then solved by online planning methods.

Aug 3rd 2026
5-12 Weeks
Approximation Algorithms Part I (Coursera) Coursera
École normale supérieure

Approximation Algorithms Part I (Coursera)

How efficiently can you pack objects into a minimum number of boxes? How well can you cluster nodes so as to cheaply separate a network into components around a few centers? These are examples of NP-hard combinatorial optimization problems. It is most likely impossible to solve such problems efficiently, so our aim is to give an approximate solution that can be computed in polynomial time and that at the same time has provable guarantees on its cost relative to the optimum.

Aug 3rd 2026
5-12 Weeks
Programming Fundamentals (Coursera) Coursera
Duke University

Programming Fundamentals (Coursera)

Programming is an increasingly important skill, whether you aspire to a career in software development, or in other fields. This course is the first in the specialization Introduction to Programming in C, but its lessons extend to any language you might want to learn. This is because programming is fundamentally about figuring out how to solve a class of problems and writing the algorithm, a clear set of steps to solve any problem in its class.

Aug 10th 2026
4 Weeks