Foundations | The Complete Python and Data Science Bootcamp

Book Overview & Buying
Table Of Contents

The Complete Python and Data Science Bootcamp

By : Ron Erez

Buy this Video

The Complete Python and Data Science Bootcamp

By: Ron Erez

Buy this Video

Overview of this book

This course is designed to transform beginners into skilled programmers by starting with Python essentials and building steadily toward advanced concepts. Early sections focus on syntax, control flow, loops, and data structures, ensuring a solid grounding in the language. From there, you’ll apply these skills in projects like Turtle Graphics and coding exercises that strengthen problem-solving abilities. Once the core programming skills are in place, the journey moves into critical computer science foundations such as recursion, searching, sorting algorithms, and object-oriented programming. You’ll then progress into professional practices like error handling, file management, and unit testing, giving you the same toolkit developers use in real-world applications. The final phase dives into data science and AI. You’ll learn data manipulation with NumPy and Pandas, create meaningful visualizations with Matplotlib and Plotly, and explore image processing techniques. The course culminates in PyTorch fundamentals, introducing GPU-powered computation and tensor operations that form the backbone of modern machine learning. By the end, you’ll have both the knowledge and confidence to work with data-driven technologies at scale.

Introduction

Foundations

Introduction to Python Basics

First Steps in Python and the Python Programming Language Structure

Python Program Structure: Input and Output

Indentation and Code Blocks

Using the Python Interpreter

More Details on the Print Function

Basic Data Types in Python

Numerical Operations

Assignment and Incremental Assignment

Multiple Assignments

Variable Names, Snake Case, Camel Case

Keywords and Our First Import Statement

Escape Sequences

Data Type Conversions

Substrings and Slicing

Multiline Strings and Docstrings

Installing and Introducing PyCharm

Control Flow and Conditional Statements

Introduction to Control Flow and Conditionals

If Statement and Logical Operators

Complex Conditions

Nested If Statements

Loops

Introducing Loops

For Loops Using Range

General For Loops Using Range

Looping over Lists and Tuples

Prime Numbers and Breaking Out of Loops

Looping over a List of Strings Using Split

While Loops

The While Loop and Validating Input

Factorial Using the While Loop: Example of an Infinite While Loop

Factorial Using the While Loop and Incremental Assignment

Nested Loops

Capstone Project Using Turtle Graphics

Welcome to Turtle Graphics

Introducing Turtle Graphics

Avoiding Magic Numbers

Generalizing Example and Using Parameters

Completing Turtle Graphics Background

Turtle Graphics Capstone Project

Functions

Introduction to Functions

Simple Functions

More Examples of Functions

Functions with Default Parameters

Breaking Down Problems Using Functions

Function Scope: Local and Global Variables

Accessing a Global Variable from Within a Function

Call by Order vs. Call by Name/Keyword Arguments

Variable Number of Arguments in a Function Call

Sum Example with Type-Checking

String Methods

Type Annotations and Functions

Type Annotations with Lists

Lists

Introduction to Lists

Creating Lists and Accessing List Elements

List Methods

Nested Lists

List Slicing

List Comprehensions

List Comprehensions and Filtering

For Loop Appending vs. List Comprehension

Exercises—Functions, Loops, Lists

Welcome to the Exercise Section

Exercise—Loops

Solution—Average

Solution—Sum of Even Digits

Solution—Min and Max in a List

Solution—Count Vowels

Solution—Multiplication Table

Exercise—List Comprehensions

Solution—List Comprehensions Squares

Exercise—List Comprehensions Squares: Filtering

Solution—List Comprehensions Squares: Filtering

Exercise—List Comprehensions Squares: Multiplication

Solution—List Comprehensions Squares: Multiplication

Exercise—List Comprehensions Squares: Flatten

Solution—List Comprehensions Squares: Flatten

Exercise—List Comprehensions Squares: Temperature

Solution—List Comprehensions Squares: Temperature

Equality vs. Identity

Introduction

Aliasing

Beware of the "is" Operator

Shallow Copy

Deep Copy

type vs. isinstance

Comparison and Inequalities

Inequalities and Sorting

Reverse Sorting

General Sorting by a Key Function

Exception and Error Handling

Syntax vs. Run-Time Errors

TypeError in Average Function

Catch All Errors

Catch Multiple Exceptions

Handling Exceptions Separately

Using else and finally

Safe Division Example

Raising a Built-In Exception

Example of Raising an Exception

Raising a Custom Exception

Recursive Programming

Factorial Recursive vs. Non-Recursive Implementation

Implementing the Exponential Function Using Recursion

Simple Recursive Fibonacci

Counting Number of Calls in Simple Recursive Fibonacci

Assignment Expressions and Efficient Fibonacci

Comparing the Run-Time of Fibonacci Implementations

Searching and Sorting Algorithms

Linear Search Boolean

Linear Search Return Index

Searching a Sorted List—Bird's-Eye View of Binary Search

Searching a Sorted List—Implementing Binary Search

Worst-Case Run-Time Complexity: Linear vs. Binary Search

MaxSort

BubbleSort

QuickSort

Data Structures Beyond Lists

Introducing Dictionaries

Safely Accessing Dictionaries Using the get Method

Real-World Example Using Nested Data Structures and the get Method

Dictionary Methods

Introducing Tuples

More on Tuples

Tuple Methods index and count

Introducing Sets

Set Methods

Exercise—Numbers to Superscripts

Solution—Numbers to Superscripts

Exercise—Word Frequency Counter with Optional Case Sensitivity

Solution—Word Frequency Counter with Optional Case Sensitivity

Exercise—Generate Square Dictionary

Solution 1—Square Dictionary

Solution 2—Square Dictionary

Exercise—Anagrams

Solution 1—Anagrams Using Character Frequency Counter

Functions Within Functions

Solution 2—Anagrams Using Sorting

Exercise—Unique Characters

Solution—Unique Characters

Object-Oriented Programming

Classes, Instance Attributes, Class Attributes, and Methods

Encapsulation

Inheritance

Polymorphism

Constructors and Destructors

The hasattr Function

The str and repr Methods

Class Methods vs. Static Methods vs. Instance Methods

Complex Numbers and Class, Static, and Instance Methods

Custom Equality and Comparison Operators for Classes in Python

Dunder (Magic) Methods

Coding Exercise—Fraction Class and Magic Methods

Coding Solution Part 1—Fractional Addition and Subtraction

Coding Solution Part 2—Subtraction Alternative, str, repr

Unit Testing with pytest

Introduction to Unit Testing with pytest

Creating Our First Tests Using pytest

Using pytest.mark.parametrize for Efficient Test Cases

Solution to pytest.mark.parametrize Exercise

Folder Structure

File-Handling and Modules

Getting Started—Reading Text Files

The Methods read, readline, readlines

Coding Exercise—Remove Comments

Coding Solution—Remove Comments

Writing to Text Files

Writing to Files Using F-Strings

Writing to Files Using Print

Leveraging the with Statement for Safe and Efficient Code

File Access Mode

File Exceptions

File Methods

Importing Modules and Custom Modules

Importing Modules and Custom Modules Continued

NumPy

NumPy Arrays, Shape, and Reshape

NumPy Arrays of Zeros, Ones, and the Identity Matrix

Empty and Random

Indexing and Slicing in NumPy

Arithmetic and NumPy

Rough Idea of Linear Algebra and Its Applications

(Advanced) Concepts from Linear Algebra in NumPy

Solving Linear Systems

Logic: Element-Wise Comparison

Logic: Comparison with Scalars

Logic: Filtering and Where

Pandas

Getting Started with Pandas: Titanic Dataset Analysis

Filtering

Filtering and the isin Operator

Filter Rows Using notna

Examples of Filters and Logic

Solutions to the Filtering Exercises from the Previous Lecture

Filtering Columns

Applying concat to Two Series

Matplotlib, Graphing, and Statistics

Simple Bar Plot

Bar Plot—Calories per Day

Box Plot

Real-World Scenario: Customer Satisfaction Analysis—Box Plot

A Simple Scatter Plot

Scatter Plot Example—Average Daily Temperatures and Ice Cream Sales

Comparing Groups with Scatter Plots

Graphing a Function with Scatter Plot

Graphing Lines

Text Annotations

Linear Regression

Histograms

Subplots

Multiple Subplots with Different Colors and Titles

Enhancing Titles Using LaTeX

Image Subplots

Pie Chart

Stack Plot

Bar Chart

3D Plot Using a Mesh Grid

Matplotlib and Image Processing

Loading an RGB Image

Extracting RGB Channels

Converting an RGB Image to Grayscale

Exploring Color Maps

Creating n by n RGB Images

Image Manipulation—Thresholding

Image Manipulation—Compression

Image Manipulation—Squeeze Image

Image Manipulation—Inverting Images

Image Manipulation—Image Tiling

Plotly and Interactive Plots

Interactive Line Plot

Line Plot Modes

Interactive Scatter Plot with Tooltips

Interactive 3D Surface Plot

Figures as Dictionaries

Figures as Graph Objects

PyTorch Fundamentals

Google Colab and tqdm

Getting Help

Getting More Help

Introducing PyTorch and Tensors 1

Introducing PyTorch and Tensors 2

Using the GPU

Operators and More Operations

Indexing and Masking

Masking Continued

Cloning Tensors

Broadcasting—First Steps

Broadcasting Continued

More Broadcasting Examples

The Complete Python and Data Science Bootcamp

By : Ron Erez

The Complete Python and Data Science Bootcamp

By: Ron Erez

Overview of this book

Confirmation

Buy this book with your credits?

Submit Your Feedback

Create a Free Account To Continue Reading

Sign in to activate your 7-day free access