Digital Signal Processing (DSP) From Ground Up™ in Python

Course Curriculum

Chapter 1: Set Up

Lecture 1: Downloading Python

Lecture 2: Installing Python

Lecture 3: Using IDLE

Lecture 4: Installing Python packages

Lecture 5: Testing the packages

Chapter 2: Python Essentials

Lecture 1: Printing statements

Lecture 2: Variables

Lecture 3: Lists

Lecture 4: Operators

Lecture 5: Conditions

Lecture 6: For Loops

Lecture 7: While Loops

Lecture 8: Functions

Lecture 9: Dictionaries

Lecture 10: Classes and Objects

Chapter 3: Signal Statistics and Noise

Lecture 1: Signal Statistics and Noise

Lecture 2: Coding : Plotting signals with pyplot

Lecture 3: Coding : Importing signals and dealing with subplots

Lecture 4: Coding : Generating signals

Lecture 5: Mean and Standard Deviation

Lecture 6: Coding : Computing the Signal Mean

Lecture 7: Coding : Developing the Signal Mean algorithm

Lecture 8: Coding : Computing the Signal Variance

Lecture 9: Coding : Developing the Signal Variance algorithm

Lecture 10: Coding : Computing the Standard Deviation

Lecture 11: Coding : Developing the Signal Standard Deviation algorithm

Chapter 4: Quantization and The Sampling Theorem

Lecture 1: Nyquist Theorem ( Sampling Theorem )

Lecture 2: The Passive Low-Pass Filter

Lecture 3: The Passive High-Pass Filter

Lecture 4: The Active Filter

Lecture 5: The Bessel, Chebyshev and Butterworth filters

Chapter 5: Linear Systems and Superposition

Lecture 1: Introduction to Linear Systems

Lecture 2: Understanding Superposition

Lecture 3: Impulse and Step Decomposition

Chapter 6: Convolution

Lecture 1: Introduction to Convolution

Lecture 2: The Convolution Operation

Lecture 3: Examinging the Output of Convolution

Lecture 4: The Convolution Sum Equation

Lecture 5: A Closer look at the Delta function

Lecture 6: Coding : Examining the signals

Lecture 7: Coding : Computing the convolution of two signals

Lecture 8: Coding : Developing the Convolution algorithm

Lecture 9: Coding : Computing the De-convolution of two signals

Lecture 10: Coding : Correlation

Lecture 11: The Identity property of convolution

Lecture 12: The Running Sum and First Difference

Lecture 13: Coding : Computing the running sum of a signal

Lecture 14: Coding : Developing the Running Sum algorithm

Lecture 15: Coding : Computing the First Difference of a signal

Lecture 16: Coding : Developing the First Difference algorithm

Chapter 7: Fourier Transform

Lecture 1: Introduction to Fourier Analysis

Lecture 2: The DFT Engine

Lecture 3: Understanding Forward and Inverse DFT

Lecture 4: Coding : Developing the Discrete Fourier Transform (DFT) algorithm

Lecture 5: Coding : Developing the DFT magnitude algorithm

Lecture 6: Coding : Developing the Inverse Discrete Fourier Transform (IDFT) algorithm

Lecture 7: Coding : Computing the IDFT of an ECG signal

Lecture 8: Symmetry between Time domain and frequency domain -Duality

Lecture 9: Polar Notation

Lecture 10: Introduction to Spectral Analysis

Lecture 11: The Frequency Response

Chapter 8: Complex Numbers

Lecture 1: The Complex Number System

Lecture 2: Polar Representation of Complex Numbers

Lecture 3: Euler's Relation

Lecture 4: Representation of Sinusoids

Lecture 5: Representing Systems

Chapter 9: Complex Fourier Transform

Lecture 1: Introduction to Complex Fourier Transform

Lecture 2: Mathematical Equivalence

Lecture 3: The Complex DFT Equation

Lecture 4: Comparing Real DFT and Complex DFT

Chapter 10: Fast Fourier Transform (FFT)

Lecture 1: An Overview of how FFT works.

Lecture 2: Understanding the complexity of calculating DFT directly

Lecture 3: How the Decimation -in-Time FFT Algorithm works

Lecture 4: Coding : Computing the FFT of a signal

Chapter 11: Digital Filter Design

Lecture 1: Introduction to Digital Filters

Lecture 2: The Filter Kernel

Lecture 3: The Impulse,Step and Frequency response

Lecture 4: Understanding the Logarithmic scale and decibels

Lecture 5: Information representations of a signal

Lecture 6: Time domain parameters

Lecture 7: Frequency domain parameters

Lecture 8: Designing digital filters using the spectral inversion method

Lecture 9: Designing digital filters using the spectral reversal method

Lecture 10: Classification of digital filters

Chapter 12: Designing Finite Impulse Response (FIR) Filters

Lecture 1: The Moving Average Filter

Lecture 2: The Multiple Pass Moving Average Filter

Lecture 3: The Recursive Moving Average Filter

Lecture 4: Coding : Smoothing signals with the median filter