Assignment: Applying Data Analysis to a Real-World Use Case

Introduction to Python

Course Overview and Welcome

Welcome to our course: Introduction to Python! (2:00)
Introduction to Python Overview

Module 1 - Section 1 - Getting Started

What is a Scripting Language (5:20)
What is Python (8:17)
Who Uses Python?
Why Use Python?
History of Python
Python Extensions
Installing PyCharm - Option 1
Installing Visual Studio Code (VS Code) - Option 2

Module 1 - Section 2 - The Basics

Writing Your First Python Program – “Hello, World!” (3:27)
Writing Clean Python: The Power of Comments and Indentation
Basic Concepts in Python
Pseudo-code: Planning Your Program Before You Write Python (3:07)
Pseudo-code Helps You Think Like a Programmer
Pseudo-code and Python Comments

Module 1 - Section 3 - Variables and Data Types

Variables in Python: Storing and Using Data (8:31)
Variable Naming Conventions in Python
Data Types: The Foundation of Every Value (12:21)
Data Type Conversion in Python
Strings in Python: Working with Text (4:15)
String Formatting in Python: Inserting Data into Text
Escape Characters in Python: Inserting Special Symbols in Strings
Demo – Variables and Data Types (4:23)

Module 1 - Section 4 - Operators

Introduction to Operators (6:33)
Arithmetic Operators
Comparison Operators
Logical Operators
Assignment Operators
Bitwise Operators
Identity Operators and Membership Operators
Demo - Operators (4:08)

Module 1 - Section 5 - Math Modules

The Math Module in Python (6:13)
Power and Logarithmic Functions in Python's Math Module
Trigonometric Functions in Python
Power, Roots, and Logarithmic Functions
Rounding and Number Representation
Math Constants in Python
Demo - Math Module (4:08)

Module 1 - Section 6 - Input

Getting Input from the User in Python (5:10)
Converting Input to Numbers (7:46)
Common Input Errors and Handling Bad Input
Validating Input (3:58)
Using Input in a Program
Demo - Simple Survey (2:23)

Module 1 Labs

Lab - Simple Calculator
Lab - Temperature Converter
Lab - Kilometers to Miles Converter
Lab - Simple Interest Calculator
Lab - Road Trip Fuel Cost Estimator
Challenge Lab - Paint Project Estimator

Module 2 - Section 1 - Conditionals

Module 2 Overview: Control Flow, Error Handling, and Functions
Introduction to Conditional Control Flow (6:52)
Understanding if Statements
If-Else Statements
Nested if-else Statements
Demo - Dragon Quest - Nested if/else (2:24)

Module 2 - Section 2 - Loops

Introduction to Repetition and Why We Need Loops (6:46)
The while Loop
The for Loop with range()
Looping Through Lists and Strings
Loop Control Statements
Nested Loops
Loops and Accumulators
Loop Practice Patterns
Demo - Pet Simulator (4:09)

Module 2 - Section 3 - Exception Handling

What Are Exceptions and Why Do They Happen? (5:21)
The try and except Block
Handling Input Safely with Loops and Exceptions
Catching Specific vs. General Exceptions
Final Thoughts and Best Practices
Demo - Safe Division Calculator (3:31)

Module 2 - Section 4 - Functions

Introduction to Functions (5:28)
Defining and Calling a Function
Parameters and Arguments
Return Values
Returning Multiple Values from a Function in Python (4:20)
Local Variables and Scope
Combining Functions with Loops and Conditionals (9:31)
Best Practices for Writing Functions (10:13)
Using Built-in Python Functions
Demo - RPG Battle Simulator (5:29)

Module 2 - Section 5 - Packages and Modules

Introduction to Modular Programming in Python
What Is a Module? (8:19)
Creating Your Own Module
Key Module Import Techniques
Python’s Standard Library Overview
What Is a Package? (8:18)
Creating Your Own Package
Video - Creating a Package (5:30)
Third-Party Packages and pip3

Module 2 Labs

Lab - Simple Interest Calculator
Lab - Body Mass Index (BMI) Calculator
Lab - Bank Account Simulator
Lab - Bank Account Simulator with Functions
Lab -Theater Ticket Calculator
Lab - Build Your Own Python Program

Module 3 - Section 1 - Introduction to Data Structures

What Are Data Structures?
Lists in Python (5:08)
Demo - Lists - Course Registration (3:11)
Tuples in Python (5:01)
Demo - Tuples - Campus Location Lookup (3:38)
Indexing and Slicing
Dictionaries in Python (5:42)
Demo - Dictionaries - Inventory System (4:11)
Sets in Python (4:25)
Demo - Sets - Class Enrollment (4:12)
Choosing the Right Data Structure

Module 3 - Section 2 - File Handling

Introduction to File Handling (4:45)
Reading from a File
Writing to a File
Using "with" for Safer File Handling
Handling File Errors
Demo - Read and Write a File (3:45)

Module 3 - Section 3 - Working with Data Files

Reading and Writing CSV Files
Demo - Reading a CSV File (4:16)
Working with JSON Data
Demo - Loading and Displaying Student Profiles from JSON (4:07)
Reading XML in Python
Demo - Reading a Student Directory from an XML File (3:42)

Section 3 Labs

Lab - Course Planner Using Lists
Lab - Travel Itinerary Tracker Using Tuples
Lab - Campus Bookstore Inventory Using Dictionaries
Lab - Club Membership Analyzer Using Sets
Lab - Student Notes Manager (Text File I/O)
Lab - Game Inventory Reader (CSV File)
Lab - Character Profiles Loader (JSON File)
Lab - User Config Settings Viewer (XML File)

Module 4 - Section 1 - Object-Oriented Programming (OOP)

Introduction to Object-Oriented Programming (OOP)
Classes and Objects in Python
Video - Python Classes and Objects (4:57)
Demo - Student Object (3:08)
Encapsulation and Access Control
Demo - Student Object with Access Control (3:13)

Module 4 - Section 2 - Advanced Data Extraction and Transformation

From Messy Data to Meaningful Insights
Cleaning Strings with strip(), replace(), and split()
Extracting Data with Regular Expressions
Transforming and Normalizing Data
Demo - Cleaning Data - Student Record CSV (5:53)
Understanding ETL – Extract, Transform, Load

Module 4 - Section 3 - Python Libraries (Scientific and Data-Focused)

Power Up Your Python
NumPy – Arrays and Numerical Operations
Demo - NumPy (3:57)
Pandas - Working with DataFrames
Demo - Pandas (5:25)
Matplotlib: Visualizing Data
Demo - Combine the Power of Pandas and Matplotlib (3:51)
Video - Python Libraries - Wrapping it All Up (24:29)

Module 4 Labs

Lab - Character Tracker - OOP
Lab - Employee Manager – Working with a List of Objects
Lab - NumPy Power-Ups – Working with Game Score Arrays
Lab - Fitness Tracker Analytics – Combining NumPy and Pandas
Lab - Streaming Showdown – Visualizing Viewer Data with Matplotlib
Lab - Department Dashboard – Visualizing CSV Data with Pandas and Matplotlib

Module 5 - Section 1 - Introduction to Automation with Python

The Value of Scripting for System Administration
Common Tasks Automated with Python
Real-World Examples in Server Maintenance, Backup, and User Management

Module 5 - Section 2 - Tools and Libraries for Automation

Introduction to Python Tools and Libraries for System Tasks
Video - os, sys, and subprocess for System Monitoring (9:19)
Real-World Automation Using os and sys Modules
File System Scripting and Command Execution with shutil and subprocess
Video - psutil, datetime, and scheduling (10:09)
Using psutil for Process and System Monitoring
Demo - System Health Check (4:50)
Task Scheduling with time, datetime, and schedule
Automating User and Process Management
Demo - Scheduled Process Watchdog (macOS & Windows) (4:52)

Module 5 - Section 3 - Logging and System Monitoring

Writing Logs with the logging Module
Video - Logging and System Monitoring (5:39)
Creating System Health Reports
Building Alerting Logic (Threshold Checks)
Demo - System Resource Alert Monitor (4:25)

Module 5 - Section 4 - Automating Network Tasks

Checking Internet Connectivity
Video - Local and Network IP Addresses (5:34)
Retrieving Local and External IP Addresses
Network Port Scanning with Python
Demo - Network Tools Dashboard (4:10)

Module 5 - Section 5 - Scheduling and Automating Tasks

Understanding Scheduled Tasks and Cron Jobs
Video - Scheduling and Automation (6:41)
Creating a Python Script for Scheduled Cleanup or Monitoring
Demo - Python script for scheduled folder monitoring (3:20)

Module 5 - Section 6 - Expanded Network Analysis

What is psutil?
Demo - psutil Network Inspector (3:20)

Module 5 - Section 7 - Introduction to Network Subnetting

Understanding IP Addresses and Binary Representation
Video - IP Address and Subnetting (6:50)
What is Subnetting and Why Does It Matter?
Subnetting Examples and Calculations
Demo - Subnet Host Calculator (3:40)
Demo - Subnet Calculator using Python’s ipaddress Module (3:06)

Module 5 Labs

Lab - Subnet Planning Assistant
Lab - Network Subnetting Calculator
Lab - System Resource Monitor
Lab - Directory Watcher – File System Monitoring Tool
Lab - Process Checker and Terminator
Lab - Scheduled System Task Launcher
Lab - Module Challange - System Automation Tool

Module 6 - Section 1 - Introduction to Web Interaction in Python

Overview of How Python Can Communicate with the Web
Video - Introduction to Web Interaction in Python (11:37)
Common Use Cases - Data Scraping, Automation, and API Calls
Key Libraries for Web Interaction in Python
Demo - Web Data Explorer (3:31)

Module 6 - Section 2 - HTTP Basics

What is HTTP? (GET, POST, Status Codes, and Headers)
Inspecting Traffic Using Browser Developer Tools
Safe and Responsible Web Interaction

Module 6 - Section 3 - Making HTTP Requests

Installing and Importing the requests Library
Video - Making HTTP Requests (7:17)
Sending GET and POST Requests
Demo - GET and POST - Request Runner (3:31)
Reading Response Content (Text, JSON, HTML)
Demo - Weather Data Explorer (with 3-Day Forecast) (4:29)

Module 6 - Section 4 - Consuming Public APIs

What is an API?
Video - API Overview (8:18)
Reading API Documentation
Using Query Parameters in GET Requests
Demo - Advice Slip API (3:55)
Authentication and API Keys
Parsing Nested JSON Data
Demo - Crypto Tracker (3:47)

Module 6 - Section 5 - Web Scraping with BeautifulSoup

What is Web Scraping?
Video - introduction to Web Scaping (10:42)
When to Use Scraping vs. APIs
Installing and Importing BeautifulSoup and requests
Web Scraping with BeautifulSoup
Demo - Books To Scape (2:32)
Identifying Tags, Classes, and Attributes
Code Example - Parsing the Meerkat Publication Site for a List of Books

Module 6 - Section 6 - Challenges, Ethics, and Best Practices

Respecting robots.txt
Avoiding Excessive Requests
Citing Data Sources

Module 6 Labs

Lab - Website Status Checker
Lab - Activity Suggestion Tool Using the Bored API
Lab - Random Cat Facts Explorer (API)
Lab - Scraping Currency Exchange Rates
Lab - Scraping Top Free eBooks from Project Gutenberg
Lab - Global Book Discovery Dashboard
Lab - Web Scrape College Clubs
Lab - Custom Web Scraping Challenge

Module 7 - Section 1 - Handling Exceptions in Python

What Is Exception Handling?
Video - Overview of Exception Handling (8:18)
Using try, except, else, and finally
Common Python Exceptions and How to Handle Them
Demo - Error Handling (3:09)

Module 7 - Section 2 - Customizing Error Responses

Raising Exceptions Intentionally
Video - Custom Exceptions (5:25)
Creating Custom Exception Classes
Demo - Custom Exception Classes (3:51)

Module 7 - Section 3 - Validating Assumptions with assert

Using assert Statements
Video - Assert Statements (3:16)
Demo - Debugging with assert (3:29)

Module 7 - Section 4 - Writing Defensive Code

Defensive Programming Techniques
Video - Defensive Programming (5:32)
Best Practices for Robust Code
Demo - Defensive Programming in Action (5:06)

Module 7 - Section 5 - Logging and Debugging

Introduction to the logging Module
Video - Logging (5:06)
Using Logging for Debugging
Demo - Logging (4:48)

Module 7 Labs

Lab – Exception Handling Calculator
Lab – Exception Handling and Assertions Calculator
Lab - Creating and Using Custom Exceptions: Character Level Validator
Lab – Logging in Python: Login Event Tracker
Lab - Logging File Operations - Error Tracking with Context
Lab - Challenge – Secure Student Enrollment System

Module 8 - Section 1 - Introduction to Data Processing

What is Data Processing?
Video - Introduction to Data Processing (7:56)
Types of Data: Structured vs. Unstructured
Common Data Sources: CSV, Excel, APIs, and More
Overview of the Data Pipeline: Load → Clean → Transform → Analyze

Module 8 - Section 2 - Data Cleaning with Pandas

Cleaning Data with Pandas: The Basics
Handling Missing Data in Pandas
Removing Duplicates and Fixing Inconsistencies
Converting Data Types and Parsing Dates
Demo - Cleaning and Preparing Customer Data with Pandas (4:44)

Module 8 - Section 3 - Exploratory Data Analysis (EDA)

Summary Statistics and Descriptive Measures
Video - EDA (10:17)
Grouping and Aggregation
Sorting and Filtering
Basic Correlations and Trends
Demo - Exploring Customer Insights with Pandas (4:18)

Module 8 - Section 4 - Visualizing Data with Matplotlib and Seaborn

Video - Data Visualization (6:23)
Line Plots, Bar Charts, and Scatter Plots
Histograms and Boxplots
Customizing Plots: Labels, Titles, and Legends
Demo - Visualizing Customer Data with Matplotlib (4:54)
Using Seaborn for Enhanced Visualization
Demo - Enhancing Visualizations with Seaborn (4:03)

Module 8 - Section 5 - Introduction to Machine Learning

What is Machine Learning?
Video - Overview of Machine Learning (9:35)
Supervised vs. Unsupervised Learning
Introduction to scikit-learn
Splitting Data into Training and Testing Sets
Linear Regression and K-Nearest Neighbors (KNN)
Demo - Predicting Annual Income with Linear Regression (5:41)
Demo - Predicting Newsletter Signup with K-Nearest Neighbors (KNN) (5:47)

Module 8 - Section 6 - Ethics and Responsible Data Use

Bias in Data and Algorithms
Privacy Concerns and Responsible Data Handling
The Importance of Transparency and Reproducibility
Congratulations – You Did It!

Module 8 Labs

Lab - Cleaning Customer Data
Lab - Customer Spending Trends by Year
Lab - Identifying High-Value Customers
Lab (Challenge) - Exploring Trends and Correlations
Lab (Challenge) Predicting High-Value Customers with K-Nearest Neighbors
Assignment: Applying Data Analysis to a Real-World Use Case

Assignment: Applying Data Analysis to a Real-World Use Case

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