Python

Introduction

• About the Course
• Learning Objectives
• Audience
• Approach
• Minimum Hardware Requirements
• Software Requirements
• Conventions
• Installation

Introducing Python

• Introduction
• Working with the Python Interactive Shell
• Writing and Running Simple Scripts
• Variables
• User Input, Comments, and Indentations
• Summary

Data Types

• Introduction
• Numerical Data
• Strings
• Lists
• Booleans
• Summary

Control Statements

• Introduction
• Control Statements
• The if Statement
• The while Statement
• while Versus if
• Loops
• The for Loop
• The range Function
• Nesting Loops
• Breaking Out of Loops
• Summary

Functions

• Introduction
• Built-In Functions
• User-Defined Functions
• Function Arguments
• Anonymous Functions
• Summary

Lists and Tuples

• Introduction
• List Syntax
• List Methods
• List Comprehensions
• Tuple Syntax
• Accessing Tuple Elements
• Accessing Tuple Elements
• Tuple Methods
• Summary

Dictionaries and Sets

• Introduction
• Working with Dictionaries
• Additional Dictionary Attributes
• Ordered Dictionaries
• The Basics of Sets
• Set Operations
• Frozen Sets
• Summary

Object-Oriented Programming

• Introduction
• A First Look at OOP
• OOP in Python
• Methods in a Class
• Class Versus Instance Attributes
• Class Versus Instance Methods
• Class Inheritance
• Multiple Inheritance
• Summary

Modules, Packages, and File Operations

• Introduction
• Defining Modules
• Imports and Import Statements
• Modules and Packages
• File Operations
• The file Object
• Reading and Writing to Files
• Handling Structured Data
• Summary

Error Handling

• Introduction
• Built-In Exceptions
• Handling Errors and Exceptions
• Custom Exceptions
• Summary

Appendix: PCAP Exam Objective

Appendix: PCEP-30-01 Exam Objective

Introduction 

• About the Course

Vital Python – Math, Strings, Conditionals, and Loops

• Introduction
• Vital Python
• Numbers: Operations, Types, and Variables
• Python as a Calculator
• Strings: Concatenation, Methods, and input()
• String Interpolation
• String Indexing and Slicing
• Slicing
• Booleans and Conditionals
• Loops
• Summary

Python Structures

• Introduction
• The Power of Lists
• Matrix Operations
• List Methods
• Dictionary Keys and Values
• Dictionary Methods
• Tuples
• A Survey of Sets
• Choosing Types
• Summary

Executing Python – Programs, Algorithms, and Functions

• Introduction
• Python Scripts and Modules
• Python Algorithms
• Basic Functions
• Iterative Functions
• Recursive Functions
• Dynamic Programming
• Helper Functions
• Variable Scope
• Lambda Functions
• Summary

Extending Python, Files, Errors, and Graphs

• Introduction
• Reading Files
• Writing Files
• Preparing for Debugging (Defensive Code)
• Plotting Techniques
• The Don'ts of Plotting Graphs
• Summary

Constructing Python – Classes and Methods

• Introduction
• Classes and Objects
• Defining Classes
• The __init__ method
• Methods
• Properties
• Inheritance
• Summary

The Standard Library

• Introduction
• The Importance of the Standard Library
• Dates and Times
• Interacting with the OS
• Using the subprocess Module
• Logging
• Collections
• Functools
• Summary

Becoming Pythonic

• Introduction
• Using List Comprehensions
• Set and Dictionary Comprehensions
• Default Dictionary
• Iterators
• Itertools
• Generators
• Regular Expressions
• Summary

Software Development

• Introduction
• Debugging
• Automated Testing
• Creating a PIP Package
• Creating Documentation the Easy Way
• Source Management
• Summary

Practical Python – Advanced Topics

• Introduction
• Developing Collaboratively
• Dependency Management
• Deploying Code into Production
• Multiprocessing
• Parsing Command-Line Arguments in Scripts
• Performance and Profiling
• Profiling
• Summary

Data Analytics with pandas and NumPy

• Introduction
• NumPy and Basic Stats
• Matrices
• The pandas Library
• Data
• Null Values
• Visual Analysis
• Summary

Machine Learning

• Introduction
• Introduction to Linear Regression
• Cross-Validation
• Regularization: Ridge and Lasso
• K-Nearest Neighbors, Decision Trees, and Random Forests
• Classification Models
• Boosting Methods
• Summary

Introduction

• Course Description
• How To Use This Course
• Course-Specific Technical Requirements

Setting Up a Python Data Science Environment

• Topic A: Select Python Data Science Tools
• Topic B: Install Python Using Anaconda
• Topic C: Set Up an Environment Using Jupyter Notebook
• Summary

Managing and Analyzing Data with NumPy

• Topic A: Create NumPy Arrays
• Topic B: Load and Save NumPy Data
• Topic C: Analyze Data in NumPy Arrays
• Summary

Transforming Data with NumPy

• Topic A: Manipulate Data in NumPy Arrays
• Topic B: Modify Data in NumPy Arrays
• Summary

Managing and Analyzing Data with pandas

• Topic A: Create Series and DataFrames
• Topic B: Load and Save pandas Data
• Topic C: Analyze Data in DataFrames
• Topic D: Slice and Filter Data in DataFrames
• Summary

Transforming and Visualizing Data with pandas

• Topic A: Manipulate Data in DataFrames
• Topic B: Modify Data in DataFrames
• Topic C: Plot DataFrame Data
• Summary

Visualizing Data with Matplotlib and Seaborn

• Topic A: Create and Save Simple Line Plots
• Topic B: Create Subplots
• Topic C: Create Common Types of Plots
• Topic D: Format Plots
• Topic E: Streamline Plotting with Seaborn
• Summary

Appendix A: Scraping Web Data Using Beautiful Soup

• Topic A: Scrape Web Pages