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

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.