Modern Data Wrangling with AI and Python – Beginner to Pro

Course Curriculum

Chapter 1: Welcome

Lecture 1: Welcome to the course

Lecture 2: Meet Gerhard

Lecture 3: Is this course for you?

Lecture 4: What will you get out of this course?

Lecture 5: What's in this course?

Chapter 2: Getting Started

Lecture 1: Introduction

Lecture 2: Visual Studio Code – Installing and getting Started

Lecture 3: Adding Extensions to VSC

Lecture 4: GitHub copilot

Lecture 5: Create a Github account

Lecture 6: GitHub Copilot chat

Lecture 7: GitHub Copilot continued

Lecture 8: Testing if Copilot is working

Lecture 9: Github copilot help content

Lecture 10: Anaconda

Lecture 11: Installing Github Desktop

Lecture 12: The GitHub Desktop Interface

Lecture 13: The repository for this course

Lecture 14: GIT (optional)

Lecture 15: Summary

Chapter 3: Data Exploration – What can you tell me about the data?

Lecture 1: Introduction

Lecture 2: A spreadsheet program

Lecture 3: The challenge with spreadsheets

Lecture 4: Follow along with me

Lecture 5: Welcome to Python (and AI)

Lecture 6: Markdown and Code fields in Jupyter

Lecture 7: What is Pandas?

Lecture 8: Read a file in Pandas and display it

Lecture 9: Describe the data

Lecture 10: Do more with Copilot, Jupyter and Python

Lecture 11: Summary

Chapter 4: Enough Python to start your journey

Lecture 1: Introduction

Lecture 2: Libraries

Lecture 3: More Pandas

Lecture 4: Objects

Lecture 5: Getting help from Large Language Models

Lecture 6: Methods

Chapter 5: My journey to data wrangling

Lecture 1: Summary

Lecture 2: My journey

Chapter 6: Structured Data – Tables

Lecture 1: Introduction

Lecture 2: Structured Data

Lecture 3: Structured Data in Python

Lecture 4: CSV files as structured data

Lecture 5: Excel data as structured data

Lecture 6: General methods for structured data – Excel and CSV

Lecture 7: SQL Tables

Lecture 8: SQL data in Python

Lecture 9: Summary

Chapter 7: More on Python – functions, properties and some other goodies

Lecture 1: Introduction

Lecture 2: Functions

Lecture 3: Function signatures

Lecture 4: Function bodies

Lecture 5: Using fucnctions

Lecture 6: A function in action

Lecture 7: Properties

Lecture 8: Properties in code

Lecture 9: For loops

Lecture 10: For loops in code

Lecture 11: Another example of for loops

Lecture 12: Getting help – Docstrings and signatures

Lecture 13: Getting help – online documentation

Lecture 14: Getting help – LLMs

Lecture 15: Getting help – Github copilot

Lecture 16: Summary

Chapter 8: Unstructured Data

Lecture 1: Introduction

Lecture 2: Installing Tabula in condas

Lecture 3: Tables in PDF documents

Lecture 4: Extracting a table from a PDF with Python

Lecture 5: Accessing particular cells in a dataframe

Lecture 6: Rename the columns of a dataframe

Lecture 7: Rename columns 2 and 3 of the dataframe

Lecture 8: Rename the remainder of the columns and concatenate strings

Lecture 9: Delete rows from a dataframe

Lecture 10: Split values in a column

Lecture 11: Drop columns in a dataframe

Lecture 12: PDFs with text

Lecture 13: Extract text from PDFs, using Python

Lecture 14: Summary

Chapter 9: Web services or Application Web Interfaces(APIs)

Lecture 1: Introduction

Lecture 2: Web services or Application Programming Interfaces(APIs)

Lecture 3: The cat facts API

Lecture 4: HTTP response status codes

Lecture 5: JSON payloads

Lecture 6: More on JSON

Lecture 7: Import API call into Postman

Lecture 8: Making an API call in Postman

Lecture 9: Generate code in Postman

Lecture 10: Execute Postman code in a Jupyter Notebook

Lecture 11: Querying JSON objects in Python

Lecture 12: Accessing lists and nested values in JSON

Lecture 13: Converting JSON to Dataframes