Course curriculum

  • 1

    Module 01: Course Introduction

    • Segment - 01 - Introduction
    • Segment - 02 - Python Installation
    • Segment - 03 - Start With Google Colaboratory Environment
    • Segment - 04 - Google Colabs and GPU
    • Segment - 05 - Google Colab Packages
  • 2

    Module 02: Get Your Data Into Google Drive

    • Segment - 06 - Mount Your Drive
    • Segment - 07 - Opening a Jupyter Notebook
    • Segment - 08 - Accessing Data Within the Drive
    • Segment - 09 - Upload Data From a Local Drive
    • Segment - 10 - Install New Packages
  • 3

    Module 03: Welcome to the Web

    • Segment - 11 - What is Webscraping?
    • Segment - 12 - Lets Rummage Inside a Webpage
    • Segment - 13 - What is HTML?
    • Segment - 14 - Accessing the Different HTML Components
  • 4

    Module 04: Let's Start Scraping

    • Segment - 15 - Shall We Start With Soup?
    • Segment - 16 - Simple Webscraping-Parse in an HTML
    • Segment - 17 - Another Way of Reading in HTML Webpages
    • Segment - 18 - Tackling Tables-Part 1
    • Segment - 19 - When We Have More Than 1 Table
    • Segment - 20 - Extract Tables Into Pandas - Part 1
    • Segment - 21 - Extract Tables Into Pandas - Part 2
    • Segment - 22 - A Quicker Way to Extract Tabular Data
    • Segment - 23 - Get Table Names
    • Segment - 24 - Pandas and HTML Tables
  • 5

    Module 05: Lets Scrape Some Non-Wikipedia Pages

    • Segment - 25 - Scrape a Simple Non-Wiki Table
    • Segment - 26 - A Ghastly Wiki Table
    • Segment - 27 - IPO Listings
    • Segment - 28 - Making the IPO Listings Usable
    • Segment - 29 - Some Housekeeping
    • Segment - 30 - Hello to Airbnb
    • Segment - 31 - Exploring Amazon Bestsellers
    • Segment - 32 - Extract Amazon Bestsellers in a Dataframe
    • Segment - 33 - Mumbai House Prices
  • 6

    Module 06: Preprocessing and Cleaning the Scraped Data

    • Segment - 34 - What Are Pandas?
    • Segment - 35 - Basic Data Cleaning With Pandas
    • Segment - 36 - Cleaning the Scraped Data
    • Segment - 37 - String Manipulation To Get a Neater Table
    • Segment - 38 - Another Way of Tweaking
    • Segment - 39 - More Data Cleaning - Part 1
    • Segment - 40 - More Data Cleaning - Part 2
    • Segment - 41 - Geocoding the London Boroughs
    • Segment - 42 - Exporting Data
    • Segment - 43 - Fuzzy Strings
    • Segment - 44 - Basic Housekeeping Prior To Fuzzy Joining
    • Segment - 45 - Let's Get Fuzzy
    • Segment - 46 - Merge Datasets Based on Geolocations
  • 7

    Module 07: Analytics and Visualization- Some Examples

    • Segment - 47 - Explore the IPOs
    • Segment - 48 - Sector Performance
    • Segment - 49 - Quickly Scour The Mumbai Real Estate Trends