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Module 1P - Foundations of Data Science in Python | 5,7,8 Aug 2019

Topics Covered

Session 1 - Programming in python

Session 2 - Data Wrangling

Session 3 - Exploratory Data Analysis

Session 4 - Data Science in the Real World

  • Data science work flow | Case study: conversion rate analysis - ipynb code

Session 5 & 6 - Mini-projects

  • Mini project 1 - Movie
  • Mini project 2 - Student Grade
  • Mini project 3 - Camera

Steps to follow:

Step 1 - Logging into coding environment

Step 1a - Logging into Google Colab

** If you don't have a google account, skip to Step 1b **

Step 1b - Jupyter Notebook in Browser

** If the notebook disconnects/ dies often, consider Step 1c. Many students have reported that that connection is not stable**

  • Go to https://jupyter.org/try
  • Click on "Try Jupyter with Python". This will launch an online jupyter notebook using binder

Step 1c - Jupyter Notebook Installation

  • If you want to install jupyter notebook into your computer,

Step 2 - Opening a notebook

  • Click File > New Python 3 Notebook to create a new .ipynb file online
  • Wondering what's a notebook?
    • Think of it like a word document where you can write your python code, run and see the outputs, add comments and notes, insert images, etc. in one place.

Miscallaneous

Download Github folder

  • To download entire github repository, click on Clone or Download > Download Zip Download github repo

  • To download a specific folder in github repo,