Python Foundations For Data Science: From Zero To Data Analy

Master Python for Data Manipulation, Visualization, and Introductory Machine Learning

What you'll learn

Foundational Python Programming: Acquire a strong grasp of Python basics, including data types, control structures, functions, and object-oriented programming.

Data Analysis and Manipulation: Master the use of Python libraries like NumPy and pandas to clean, manipulate, and analyze datasets.

Advanced Data Visualization: Learn to create visualizations using Matplotlib and Plotly to effectively communicate data-driven insights and trends.

Gain hands-on experience with PyTorch to build and evaluate machine learning models, including classification and regression tasks.

Develop robust and reliable code using error handling techniques and performing unit testing with pytest, ensuring your data analysis scripts run smoothly

As a bonus, explore Python fundamentals while having fun with turtle graphics, making the course accessible for both parents and children learning together

Requirements

A Computer with Internet Access: You’ll need a computer with a reliable internet connection to install the necessary software and access the course materials.

Motivation to Learn: This course is beginner-friendly, requiring no prior programming or data science experience. All you need is a willingness to learn and a desire to dive into Python for data science.

No prior experience is needed—just bring your curiosity and enthusiasm to learn Python and data science!

Description

Welcome to "Python Foundations for Data Science"!This course is your gateway to mastering Python for data analysis, whether you’re just getting started or looking to expand your skills. We begin with the basics, ensuring you build a solid foundation, then gradually move into data science applications.I'd like to stress that we do not assume a programming background and no background in Python is required.What You'll Learn:Python Foundations: Grasp the essentials of Python, including data types, strings, slicing, f-strings, and more, laying a solid base for data manipulation.Control and Conditional Statements: Master decision-making in Python using if-else statements and logical operators.Loops: Automate repetitive tasks with for and while loops, enhancing your coding efficiency.Capstone Project - Turtle Graphics: Apply your foundational knowledge in a fun, creative project using Python’s turtle graphics.Functions: Build reusable code with functions, understanding arguments, return values, and scope.Lists: Manage and manipulate collections of data with Python lists, including list comprehension.Equality vs. Identity: Dive deep into how Python handles data with topics like shallow vs. deep copy, and understanding type vs. isinstance.Error-Handling: Write robust code by mastering exception handling and error management.Recursive Programming: Solve complex problems elegantly with recursion and understand how it contrasts with iteration.Searching and Sorting Algorithms: Learn fundamental algorithms to optimize data processing.Advanced Data Structures: Explore data structures beyond lists, such as dictionaries, sets, and tuples, crucial for efficient data management.Object-Oriented Programming: Build scalable and maintainable code with classes, inheritance, polymorphism, and more, including an in-depth look at dunder methods.Unit Testing with pytest: Ensure your code’s reliability with automated tests using pytest, a critical skill for any developer.Files and Modules: Handle file input/output and organize your code effectively with modules.NumPy: Dive into numerical computing with NumPy, the backbone of data science in Python.Pandas: Master data manipulation and analysis with pandas, a must-know tool for data science.Matplotlib - Graphing and Statistics: Visualize data and perform statistical analysis using Matplotlib.Matplotlib - Image Processing: Explore basic image processing techniques using Matplotlib.PyTorch Fundamentals: Get started with deep learning using PyTorch, understanding tensors and neural networks.Why Enroll?Expert Guidance: Benefit from step-by-step tutorials and clear explanations.Responsive Support: Get prompt, helpful feedback from the instructor, with questions quickly addressed in the course Q&A.Flexible Learning: Study at your own pace with lifetime access to regularly updated course materials.Positive Learning Environment: Join a supportive and encouraging space where students and instructors collaboratively discuss and solve problems.Who This Course is For: Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science.Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python.Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge.Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making.Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies.Enroll now and start your journey to mastering Python for data science and data analysis!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Foundations

Lecture 2 Introduction to Python Basics

Lecture 3 First steps in Python and the Python Programing Language Structure

Lecture 4 Python Program Structure - Input and Output

Lecture 5 Indentation and Code Blocks

Lecture 6 Using the Python Interpreter

Lecture 7 More Details on the Print function

Lecture 8 Basic Data Types in Python

Lecture 9 Numerical Operations

Lecture 10 Assignment and Incremental Assignment

Lecture 11 Multiple Assignments

Lecture 12 Variable Names, Snake Case, Camel Case

Lecture 13 Keywords and our first Import Statement

Lecture 14 Escape Sequences

Lecture 15 Data Type Conversions

Lecture 16 Substrings and Slicing

Lecture 17 Multiline Strings and Docstrings

Lecture 18 Installing and Introducing PyCharm

Section 3: Control Flow and Conditional Statements

Lecture 19 Introduction to Control Flow and Conditionals

Lecture 20 If Statement and Logical Operators

Lecture 21 Complex Conditions

Lecture 22 Nested If Statements

Section 4: Loops

Lecture 23 For Loops using Range

Lecture 24 General For Loops using Range

Lecture 25 Looping over Lists and Tuples

Lecture 26 Prime Numbers and Breaking out of Loops

Lecture 27 Looping over a List of Strings using Split

Lecture 28 While Loops

Lecture 29 The While Loop and Validating Input

Lecture 30 Factorial using the While Loop. Example of an Infinite While Loop

Lecture 31 Factorial using the While Loop and Incremental Assignment

Lecture 32 Nested Loops

Section 5: Capstone Project using Turtle Graphics

Lecture 33 Introducing Turtle Graphics

Lecture 34 Avoiding Magic Numbers

Lecture 35 Generalizing Example and using Parameters

Lecture 36 Completing Turtle Graphics Background

Lecture 37 Turtle Graphics Capstone Project

Section 6: Functions

Lecture 38 Introduction to Functions

Lecture 39 Simple Functions

Lecture 40 More Examples of Functions

Lecture 41 Functions with Default Parameters

Lecture 42 Breaking down Problems using Functions

Lecture 43 Function Scope, Local and Global Variables

Lecture 44 Accessing a global variable from within a function

Lecture 45 Call by Order vs Call by Name/Keyword Arguments

Lecture 46 Variable Number of Arguments in a Function call

Lecture 47 Sum Example with Type-Checking

Lecture 48 String Methods

Lecture 49 Type Annotations and Functions

Lecture 50 Type Annotations with Lists

Section 7: Lists

Lecture 51 Introduction to Lists

Lecture 52 List Methods

Lecture 53 Nested Lists

Lecture 54 List Slicing

Lecture 55 List Comprehensions

Lecture 56 List Comprehensions and Filtering

Lecture 57 For Loop Appending vs List Comprehension

Section 8: Equality vs Identity

Lecture 58 Aliasing

Lecture 59 Beware of the 'is' Operator

Lecture 60 Shallow Copy

Lecture 61 Deep Copy

Lecture 62 type vs isinstance

Lecture 63 Comparison and Inequalities

Lecture 64 Inequalities and Sorting

Lecture 65 Reverse Sorting

Lecture 66 General Sorting by a Key Function

Section 9: Exception and Error Handling

Lecture 67 Syntax vs Run-Time Errors

Lecture 68 TypeError in Average Function

Lecture 69 Catch all Errors

Lecture 70 Catch Multiple Exceptions

Lecture 71 Handling Exceptions Separately

Lecture 72 Using else and finally

Lecture 73 Safe Division Example

Lecture 74 Raising a Built-in Exception

Lecture 75 Example of Raising an Exception

Lecture 76 Raising a Custom Exception

Section 10: Recursive Programming

Lecture 77 Factorial Recursive vs Non-Recursive Implementation

Lecture 78 Implementing the Exponential Function using Recursion

Lecture 79 Simple Recursive Fibonacci.

Lecture 80 Counting number of calls in Simple Recursive Fibonacci

Lecture 81 Assignment Expressions and Efficient Fibonacci

Lecture 82 Comparing the Run-Time of Fibonacci Implementations

Section 11: Searching and Sorting Algorithms

Lecture 83 Linear Search Boolean

Lecture 84 Linear Search Return Index

Lecture 85 Searching a Sorted List - Birds-eye View of Binary Search

Lecture 86 Searching a Sorted List - Implementing Binary Search

Lecture 87 Worst-Case Run-time Complexity Linear vs Binary Search

Lecture 88 MaxSort

Lecture 89 BubbleSort

Lecture 90 QuickSort

Section 12: Data Structures beyond Lists

Lecture 91 Introducing Dictionaries

Lecture 92 Safely accessing Dictionaries using the get Method

Lecture 93 Real-World Example using Nested Data Structures and the get Method

Lecture 94 Dictionary Methods

Lecture 95 Introducing Tuples

Lecture 96 More on Tuples

Lecture 97 Tuple Methods index and count

Lecture 98 Introducing Sets

Lecture 99 Set Methods

Section 13: Object-Oriented Programming

Lecture 100 Classes, Instance Attributes, Class Attributes and Methods

Lecture 101 Encapsulation

Lecture 102 Inheritance

Lecture 103 Polymorphism

Lecture 104 Constructors and Destructors

Lecture 105 The hasattr Function

Lecture 106 The __str__ and __repr__ Methods

Lecture 107 Class Methods vs Static Methods vs Instance Methods

Lecture 108 Complex Numbers and Class, Static and Instance Methods

Lecture 109 Custom Equality and Comparison Operators for Classes in Python

Lecture 110 Dunder (Magic) Methods

Lecture 111 CODING EXERCISE: Fraction Class and Magic Methods

Lecture 112 CODING SOLUTION Part 1 - Fractional Addition and Subtraction

Lecture 113 CODING SOLUTION Part 2 - Subtraction Alternative, __str__, __repr__

Section 14: Unit Testing with pytest

Lecture 114 Introduction to Unit Testing with pytest

Lecture 115 Creating our First Tests using pytest

Lecture 116 Using pytest.mark.parametrize for Efficient Test Cases

Lecture 117 SOLUTION to pytest.mark.parametrize Exercise

Lecture 118 Folder Structure

Section 15: File-handling and Modules

Lecture 119 Getting Started - Reading Text Files

Lecture 120 The Methods read, readline, readlines

Lecture 121 CODING EXERCISE - Remove Comments

Lecture 122 CODING SOLUTION - Remove Comments

Lecture 123 Writing to Text Files

Lecture 124 Writing to files using F-Strings

Lecture 125 Writing to files using Print

Lecture 126 Leveraging the `with` Statement for Safe and Efficient Code

Lecture 127 File Access Mode

Lecture 128 File Exceptions

Lecture 129 File Methods

Lecture 130 Importing Modules and Custom Modules

Lecture 131 Importing Modules and Custom Modules continued

Section 16: NumPy

Lecture 132 Numpy Arrays, Shape and Reshape

Lecture 133 Numpy Arrays of Zeros, Ones and the Identity Matrix

Lecture 134 Empty and Random

Lecture 135 Indexing and Slicing in Numpy

Lecture 136 Arithmetic and Numpy

Lecture 137 Rough Idea of Linear Algebra and its Applications

Lecture 138 (ADVANCED) Concepts from Linear Algebra in Numpy

Lecture 139 Solving Linear Systems

Lecture 140 Logic: Element-wise Comparison

Lecture 141 Logic: Comparison with Scalars

Lecture 142 Logic: Filtering and Where

Section 17: Pandas

Lecture 143 Getting Started with Pandas: Titanic Dataset Analysis

Lecture 144 Filtering

Lecture 145 Filtering and the isin operator

Lecture 146 Filter rows using notna

Lecture 147 Examples of Filters and Logic

Lecture 148 Solutions to the Filtering Exercises from the Previous Lecture

Lecture 149 Filtering Columns

Lecture 150 Applying concat to Two Series

Section 18: Matplotlib, Graphing and Statistics

Lecture 151 Simple Bar Plot

Lecture 152 Bar Plot- Calories per Day

Lecture 153 Box Plot

Lecture 154 Real-World Scenario: Customer Satisfaction Analysis - Box Plot

Lecture 155 A Simple Scatter Plot

Lecture 156 Scatter Plot - Example - Average Daily Temperatures and Ice Cream Sales

Lecture 157 Comparing Groups with Scatter Plots

Lecture 158 Graphing a Function with Scatter Plot

Lecture 159 Graphing Lines

Lecture 160 Text Annotations

Lecture 161 Linear Regression

Lecture 162 Histograms

Lecture 163 Subplots

Lecture 164 Multiple Subplots with Different Colors and Titles

Lecture 165 Enchancing Titles using Latex

Lecture 166 Image Subplots

Lecture 167 Pie Chart

Lecture 168 Stack Plot

Lecture 169 Bar Chart

Lecture 170 3D Plot using a Mesh Grid

Section 19: Matplotlib and Image Processing

Lecture 171 Loading an RGB Image

Lecture 172 Extracting RGB Channels

Lecture 173 Converting an RGB Image to Gray-Scale

Lecture 174 Exploring Color Maps

Lecture 175 Creating n by n RGB images

Lecture 176 Image Manipulation - Thresholding

Lecture 177 Image Manipulation - Compression

Lecture 178 Image Manipulation - Squeeze Image

Lecture 179 Image Manipulation - Inverting Images

Lecture 180 Image Manipulation - Image Tiling

Section 20: Pytorch Fundamentals

Lecture 181 Google Colab and tqdm

Lecture 182 Getting Help

Lecture 183 Getting More Help

Lecture 184 Introducing Pytorch and Tensors 1

Lecture 185 Introducing Pytorch and Tensors 2

Lecture 186 Using the GPU

Lecture 187 Operators and More Operations

Lecture 188 Indexing and Masking

Lecture 189 Masking Continued

Lecture 190 Cloning Tensors

Lecture 191 Broadcasting - First Steps

Lecture 192 Broadcasting Continued

Lecture 193 More Broadcasting Examples

Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science.,Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python.,Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge.,Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making.,Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies.