Modern Data Wrangling with AI and Python - Beginner to Pro

Install Visual Studio Code, VSC Copilot, anacondas or Conures, and git to set up the course tools and enable AI-assisted coding.

learn how to enable GitHub Copilot from the extensions, start a 60-day free trial by logging into GitHub and entering credentials and a credit card, and decide whether to continue.

Install GitHub Desktop, create a local repository copy of the course code, then open it in Visual Studio Code to access the project you will study.

Download git for Mac or Windows to manage code versions in a central repository. Learn how to use git in later sections.

Access the course source code and open it in Visual Studio Code to explore the data in the exploration folder, where a Jupyter notebook contains the first section's code.

Pandas is the package used to read csv files and files that resemble spreadsheets. Learn how to run code, select a kernel, and handle a missing location when reading.

Structured data uses tabular formats with rows and columns, like sheets and csv files, with referable coordinates. The lecture contrasts structured, unstructured (PDF), and semi-structured data and explains extraction differences.

Learn how SQL databases store data in tables, with albums table columns album id, title, and artist id, and query by selecting from albums and limiting to five rows.

Explore how functions and object methods enable reusable code in Python, and see how to apply a function across multiple Excel files to count sheets.

Import the library inside the function, access workbook data, assign the path for bank personal loan modeling, retrieve the number of sheets, and print the sheet count.

Create a list of numbers from 1 to 10 with range and list methods, then print them using a for loop.

Explore unstructured data such as pdfs, images, and videos; learn to import pdf data into python, extract tables into dataframes, access elements, rename, split, and drop columns, and extract text.

Learn how to extract table data from PDF documents using Python, and understand how table data falls along structured, semi-structured, and unstructured categories.

Learn to rename dataframe columns by concatenating first and last names into a new column, keep rows intact, and comment out unwanted drops while running code to apply consistent names.

Rename columns in a dataframe using zero-based indexing: extract values from the second and third columns and first two rows, combine them, reset the index, and verify the rename.

Explore how GitHub Copilot completes code and enforces consistent names. Describe the data frame to reveal meaningful values reflecting one year and compute statistics.

Explore unstructured data techniques, including extracting tables from PDFs to pandas dataframes and extracting text with Python. Reference and manipulate dataframe values, then transition to semi-structured data and APIs.

Learn to call the cat facts api using a random fact endpoint, make get requests, and interpret the response body and length, using curl and a display add-on.

Begin by making a get request in Postman, explore http methods such as get, post, patch, and delete, and view the json payload returned by a cat facts api.

Learn to access nested json data in Python by using square bracket notation to retrieve the glossary title from a json payload.

Discover how APIs enable internet-wide communication, with examples like cat API and Google API, and how JSON payloads become pandas dataframes for analysis.

Learn how to create and manipulate Python lists, store numbers 1 to 5, iterate with for loops, append items, view contents with print, and explore list methods in the documentation.

Review the theoretical aspects of lists, dictionaries, and datatypes in Python, showing how lists group items and dictionaries use keys for lookups. Learn about data types and lambdas.