From Messy Data to Meaningful Insights

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
What is Python
Who Uses Python?
Why Use Python?
History of Python
Python Extensions
Video - Welcome to Python (2:48)
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!” (3:24)
Writing Clean Python: The Power of Comments and Indentation
Basic Concepts in Python

Module 1 - Section 3 - Variables and Data Types

Variables in Python: Storing and Using Data
Variable Naming Conventions in Python
Data Types: The Foundation of Every Value
Video: Variables, Data Types, and Naming Conventions (3:11)
Data Type Conversion in Python
Strings in Python: Working with Text
String Formatting in Python: Inserting Data into Text
Video - Strings and String Formatting (4:05)
Escape Characters in Python: Inserting Special Symbols in Strings
Demo – Variables and Data Types (4:23)

Module 1 - Section 4 - Operators

Introduction to Operators
Arithmetic Operators
Comparison Operators
Logical Operators
Assignment Operators
Video - Python Operators (2:58)
Bitwise Operators
Identity Operators and Membership Operators
Demo - Operators (4:06)

Module 1 - Section 5 - Math Modules

The Math Module in Python
Power and Logarithmic Functions in Python's Math Module
Trigonometric Functions in Python
Power, Roots, and Logarithmic Functions
Rounding and Number Representation
Video - Math Modules in Python (6:01)
Math Constants in Python
Demo - Math Module (4:00)

Module 1 - Section 6 - Input

Getting Input from the User in Python
Converting Input to Numbers
Common Input Errors and Handling Bad Input
Using Input in a Program
Video - Basic Input (4:05)
Validating Input
Demo - Simple Survey (2:20)
Video - Validating Input (3:31)

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
Understanding if Statements
If-Else Statements
Nested if-else Statements
Demo - Dragon Quest - Nested if/else (2:22)

Module 2 - Section 2 - Loops

Introduction to Repetition and Why We Need Loops
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 (4:03)

Module 2 - Section 3 - Exception Handling

What Are Exceptions and Why Do They Happen?
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
Defining and Calling a Function
Parameters and Arguments
Return Values
Returning Multiple Values from a Function in Python
Local Variables and Scope
Combining Functions with Loops and Conditionals
Best Practices for Writing Functions
Using Built-in Python Functions
Demo - RPG Battle Simulator (5:31)

Module 2 - Section 5 - Packages and Modules

Introduction to Modular Programming in Python
What Is a Module?
Creating Your Own Module
Key Module Import Techniques
Python’s Standard Library Overview
What Is a Package?
Creating Your Own Package
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

Module 3 - Section 1 - Introduction to Data Structures

What Are Data Structures?
Lists in Python
Demo - Lists - Course Registration (3:11)
Tuples in Python
Demo - Tuples - Campus Location Lookup (3:39)
Indexing and Slicing
Dictionaries in Python
Demo - Dictionaries - Inventory System (4:11)
Sets in Python
Demo - Sets - Class Enrollment (4:12)
Choosing the Right Data Structure

Module 3 - Section 2 - File Handling

Introduction to File Handling
Reading from a File
Writing to a File
Using "with" for Safer File Handling
Handling File Errors
Demo - Read and Write a File (3:46)

Module 3 - Section 3 - Working with Data Files

Reading and Writing CSV Files
Demo - Reading a CSV File (4:15)
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
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)

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
Real-World Automation Using os and sys Modules
File System Scripting and Command Execution with shutil and subprocess
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
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
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
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
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
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
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?
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?
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

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 - Custom Web Scraping Challenge

Module 7 - Section 1 - Handling Exceptions in Python

What Is Exception Handling?
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
Creating Custom Exception Classes
Demo - Custom Exception Classes (3:51)

Module 7 - Section 3 - Validating Assumptions with assert

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

Module 7 - Section 4 - Writing Defensive Code

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

Module 7 - Section 5 - Logging and Debugging

Introduction to the logging Module
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?
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
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

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?
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

From Messy Data to Meaningful Insights

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