One Week Python

One Week Python

What you’ll learn

Master modern Python fundamentals as quickly as possible
Learn the Python you need to move on to Data Science or Web Development
Work with 3rd party Python libraries and modules
Complete dozens of exercises, quizzes, and projects
You’ll learn to code with Python while staying sane!

Requirements

No programming experience needed – I cover everything you need to know!
A computer (mac, windows, or linux) and an internet connection

Description

Don’t waste your time with 60+ hour behemoth courses that you never finish!  Instead, try this quick and effective pathway to Python that was designed with your sanity in mind.   This course is the p1erfect first step into the world of data science, web dev, machine learning, or any other discipline that requires Python knowledge.As an-person coding bootcamp instructor, I created this course to keep you engaged the entire way through.  It’s full of exercises, quizzes, challenges, and projects.  The slides, diagrams, and cheatsheets were painstakingly designed to help you visualize the tricky concepts.  There are no 20-minute monster videos in this course; the average video is only 4 minutes long.  Everything about this course has been designed to make it actually completable!This course covers all the Python essentials you need: everything from variables to data structures to object oriented programming and modules. You’ll fill up your Python toolbox so you can go on and tackle libraries like pandas, flask, scikitlearn, django, and more.What this course is not:  This course is not a complete guide to every single possible feature in the Python language.  It focuses on the 80% that is absolutely critical and worth your time, but there are other (much longer) courses that are more akin to Python textbooks that take the time to cover every feature.  In fact, I created one of those courses, and it happens to be 40 hours long!  As someone who has both created and purchased massive Udemy mega bootcamp courses, I know they are still the standard format on the platform, but maybe it’s time for something a bit more human and engaging. I thought it was worth a try at least.Here’s a detailed breakdown of what we cover:Painless installation for all platforms and usersWorking with numbersPython variablesStrings and string methodsBooleansConditional LogicBoolean LogicLoopsFunctionsScopeListsDictionariesSets Tuples*args and **kwargs Working with errorsCustom modules3rd party modules PIPObject Oriented ProgrammingClasses

Overview

Section 1: Welcome & Introduction

Lecture 1 Welcome To The Course!

Lecture 2 Join The Community!

Lecture 3 What This Course IS NOT

Lecture 4 Why You Should Learn Python

Lecture 5 What We Can Do With Python

Lecture 6 Download All Slides Here!

Section 2: Installation & Setup

Lecture 7 Python Versions: They Actually Matter!

Lecture 8 2 Ways of Running Python

Lecture 9 Installation For Mac Users

Lecture 10 Installation For Windows Users

Lecture 11 The “No-Installation” Option: REPL.it

Section 3: Python Numbers

Lecture 12 Intro to Data Types

Lecture 13 Integers and Floats

Lecture 14 OPTIONAL: Numeric Notations

Lecture 15 Basic Operators

Lecture 16 Lesser Known Operators

Lecture 17 Python Comments

Section 4: Variables Basics

Lecture 18 Introducing Variables

Lecture 19 Variable Naming

Lecture 20 Assignment Operators

Lecture 21 Numbers & Variables In The Wild

Lecture 22 The Print() Function

Lecture 23 ★ Magic Trick Exercise

Section 5: Strings Basics

Lecture 24 Introducing Strings

Lecture 25 String Variables

Lecture 26 String Operators

Lecture 27 String Indexing

Lecture 28 The Special Value None

Section 6: A Little More On Strings

Lecture 29 String Slices

Lecture 30 Revisiting Print()

Lecture 31 Escape Characters

Lecture 32 Triple Quoted Strings

Lecture 33 Strings In The Wild

Lecture 34 ★ Nico Hülkenberg Exercise

Section 7: Strings & Built-Ins

Lecture 35 Introducing Functions!

Lecture 36 The Len Function

Lecture 37 The Input Function

Lecture 38 Type Casting

Lecture 39 ★ Age Calculator Exercise

Lecture 40 F Strings

Lecture 41 F-Strings and Type Casting In The Wild

Lecture 42 ★ Shopping Cart Exercise

Section 8: The World Of Methods

Lecture 43 Introducing Methods: Upper and Lower

Lecture 44 Navigating The Docs

Lecture 45 Help() & ipython ‘?’

Lecture 46 Reading Function Signatures + Strip Methods

Lecture 47 Replace

Lecture 48 Other String Methods

Lecture 49 Method Chaining

Lecture 50 String Methods In The Wild

Lecture 51 ★ Press Release Exercise

Section 9: Booleans

Lecture 52 Introducing Booleans

Lecture 53 Comparison Operators

Lecture 54 Equality Operators

Lecture 55 Comparing Across Types

Lecture 56 Truthiness & Falseyness

Lecture 57 The “in” Operator

Lecture 58 OPTIONAL: Comparing Strings

Lecture 59 Booleans In The Wild

Section 10: Conditionals Basics

Lecture 60 Introducing Conditionals

Lecture 61 The If Keyword

Lecture 62 The Elif Keyword

Lecture 63 The Else Keyword

Lecture 64 ★ Name Length Codealong

Lecture 65 Generating Random Numbers With Randint()

Lecture 66 ★ Tweet Checker Exercise

Section 11: A Little More On Conditionals

Lecture 67 A Tangent On Indentation

Lecture 68 Nesting Conditionals

Lecture 69 ★ Water Boiling Codealong

Lecture 70 Conditionals In The Wild

Lecture 71 ★ Quick 1-Question Feedback Survey

Lecture 72 ★ BMI Calculator Exercise

Section 12: Writing More Complex Logic

Lecture 73 Logical AND

Lecture 74 Using Logical AND In Practice

Lecture 75 Logical OR

Lecture 76 Using Logical OR In Practice

Lecture 77 Logical NOT

Lecture 78 Using Logical NOT In Practice

Section 13: The Last Section On Conditionals

Lecture 79 Truthy/Falsey Testing

Lecture 80 Logical Operator Precedence

Lecture 81 Logical Operators In The Wild

Lecture 82 ★ Rock Paper Scissors Exercise Intro

Lecture 83 ★ Rock Paper Scissors Exercise Solution

Section 14: Loops Part I

Lecture 84 Introducing Loops!

Lecture 85 While Loops

Lecture 86 ★ While Loops Practice

Lecture 87 Avoiding Infinite Loops

Lecture 88 ★ Snake Eyes Codealong

Lecture 89 For Loops

Lecture 90 Loops and Indentation

Lecture 91 The range() function

Lecture 92 ★ 99 Bottles Of Beer Codealong

Lecture 93 ★ Loops Problem Set

Section 15: Loops Part II

Lecture 94 Break and Continue Keywords

Lecture 95 Working With Nested Loops

Lecture 96 ★ Dice Roller Exercise

Lecture 97 ★ Dice Roller Exercise Solution

Lecture 98 Loops In The Wild

Lecture 99 ★ Toothpick Game Exercise Intro

Lecture 100 ★ Toothpick Game Exercise

Lecture 101 ★ Toothpick Game Refactor

Section 16: Introducing Functions

Lecture 102 Introducing Functions

Lecture 103 Our Very First Function!

Lecture 104 Functions With An Input

Lecture 105 Functions With Multiple Arguments

Lecture 106 Introducing Return!

Lecture 107 Using The Return Keyword

Lecture 108 ★ Function Practice Set

Lecture 109 Default Parameters

Lecture 110 Ordering Default Parameters

Lecture 111 Keyword/Named Arguments

Section 17: Scope

Lecture 112 Global Scope

Lecture 113 Local Scope

Lecture 114 Scope In Loops and Conditionals?

Lecture 115 Enclosing Scope

Lecture 116 Built-In Scope

Lecture 117 Scope Precedence Rules

Lecture 118 The ‘Global’ Keyword

Section 18: Lists: The Basics

Lecture 119 Creating Lists

Lecture 120 Accessing Data In Lists

Lecture 121 Updating List Elements

Lecture 122 append() and extend()

Lecture 123 insert()

Lecture 124 List Slices

Lecture 125 Deletion Methods: pop(), popitems(), remove()

Lecture 126 Iterating Over Lists

Lecture 127 Lists + Loops Patterns

Lecture 128 ★ Grand Prix Exercise

Section 19: Lists: More List Stuff

Lecture 129 Nested Lists

Lecture 130 List Operators

Lecture 131 Sort(), Reverse(), and Count()

Lecture 132 Lists Are Mutable

Lecture 133 Comparing Lists: == vs is

Lecture 134 Join() and Split()

Lecture 135 List Unpacking

Lecture 136 Copying Lists

Lecture 137 ★ Todo List Exercise Intro

Lecture 138 ★ Todo List Exercise Solution

Section 20: Dictionaries

Lecture 139 Introducing Dictionaries

Lecture 140 Creating Your Own Dictionaries

Lecture 141 Accessing Data In Dictionaries

Lecture 142 Adding and Updating Data In Dictionaries

Lecture 143 The get() method and “in” operator

Lecture 144 Dictionary pop(), clear(), and del

Lecture 145 Dictionaries Are Mutable Too!

Lecture 146 Iterating Dicts: keys(), values(), and items()

Lecture 147 Fancy Dictionary Merging

Lecture 148 Lists and Dicts Combined!

Lecture 149 ★ Peak Dictionary Exercise

Section 21: Sets and Tuples

Lecture 150 Introducing Tuples

Lecture 151 Tuple Functionality

Lecture 152 Why Use Tuples?

Lecture 153 Sets Introduction

Lecture 154 Working With Sets

Lecture 155 Set Operators: Intersection, Union, Difference

Lecture 156 When Use Sets?

Section 22: Returning To Functions

Lecture 157 Introducing *args

Lecture 158 Using *args

Lecture 159 Introducing **kwargs

Lecture 160 Parameter List Ordering

Lecture 161 A Common Gotcha: Mutable Default Args

Lecture 162 Unpacking Args

Lecture 163 ★ Args/Kwargs Problem Set

Section 23: Working With Errors

Lecture 164 Common Error Types

Lecture 165 Raising Exceptions

Lecture 166 When To Raise?

Lecture 167 Try and Except

Lecture 168 LBYL and EAFP

Section 24: Modules

Lecture 169 Working With Built-In Modules

Lecture 170 Importing More Built-In Modules

Lecture 171 Fancy Import Syntax

Lecture 172 Creating Custom Modules

Lecture 173 3rd Party Modules: Pip & PyPI

Lecture 174 Our First Pip Package!

Lecture 175 Language Translator Package

Lecture 176 ★ Sentiment Analysis Fun Project Installation

Lecture 177 ★ Sentiment Analysis Fun Project

Section 25: Object Oriented Programming

Lecture 178 Introducing OOP

Lecture 179 Class Syntax

Lecture 180 Writing Our First Class

Lecture 181 Instance Methods

Lecture 182 Practicing Instance Methods

Lecture 183 Class Attributes

Lecture 184 Class Methods

Lecture 185 Inheritance Basics

Lecture 186 The super() Function

Section 26: The End: What’s Next??

Lecture 187 Looking Back At The Progress We’ve Made

Lecture 188 What We Did Not Cover

Lecture 189 Potential Pathways & Next Steps

Lecture 190 One Piece Of Advice

Anyone who wants to learn Python in as little time as possible,Busy people who can’t spend months completing a Python course,Anyone who plans on learning Data Science or Machine Learning but first needs a Python foundation

Course Information:

Udemy | English | 14h 15m | 6.00 GB
Created by: Colt Steele

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