Schedule

• learnpython.org
• The Python Tutorial (official Python documentation)
• Data Scientist - The Sexiest Job of the 21st Century
• The 4 Biggest Trends In Big Data And Analytics
• Types Of Data

• Top 12 Cheat Sheet for Data Science in Python
• "Numpy Quickstart Tutorial" by DataCamp
• "Introduction to Numpy" from the Numpy User Guide
• "10 minutes to Pandas" by Pandas
• "Pandas From The Ground Up" by DataCamp
• "Modern Pandas" by Tom Augspurger
• Numpy User Guide
• Pandas User Guide

• Examples of Normal Distribution and Probability In Every Day Life
• Introduction to probability, statistics, and random processes
• scipy.stats Tutorial
• Pandas - Calculate Statistics
• Real Python - Statistics in Python

• Women & children first! Titanic - ML from disaster
• The Ultimate Python Seaborn Tutorial
• Introduction to Seaborn - An official introduction to Seaborn, covering its strengths and how to get started.
• Python Seaborn Tutorial for Beginners - This tutorial by Real Python covers the basics and some advanced features of Seaborn.
• Data Visualization Using Seaborn - An article on Towards Data Science that walks you through Seaborn's features with examples.
• Comprehensive Guide to Seaborn in Python - A DataCamp tutorial that offers a deep dive into Seaborn for data visualization.

HW1 out [colab link] (due Feb 7)

• The Monty Hall Problem - A Study - American Statistical Association
• Simulation of the Monty Hall Problem - Better Explained
• The Monty Hall Problem Explained - Mathworld
• Understanding the Monty Hall Problem - Scientific American

HW1 due

• Markdown Cheat Sheet
• HackMD - Collaborative Markdown Knowledge Base

Proj-Part A [HackMD link] [Toy example] (due Mar 06)

• ChatGPT
• Prompt examples
• The Ultimate AI Tool Kit
• Awesome ChatGPT Prompts
• copilot | GitHub

Proj P-A due

• Chapter 7 | Introduction to Probability, Statistics, and Random Processes
• Law of Large Numbers and the Central Limit Theorem (With Python)
• Everything You Need To Know About Law of Large Numbers and Central Limit Theorem | Medium
• LLN and CLT | Quantitative Economics with Julia

• A/B Testing (Wikipedia)
• What is A/B Testing? (Optimizely)
• A/B Testing Guide (HubSpot)
• A/B Testing with Google Optimize (Google Analytics)
• A/B Testing Resources (VWO)

HW2 out [colab link] (due Mar 27)

• Null Hypthesis
• Proof - Relationship between normal distribution and t-distribution
• Two Sample T-test with math proof (northeastern)
• How to understand degrees of freedom?

HW2 due

• Python for Data Analysis
• A/B Testing - The Most Powerful Way to Turn Clicks into Customers
• Statistical Methods for A/B Testing
• Experimentation for Data Science
• A/B Testing with Python

HW3 out [colab link] (due Apr 17)

• A/B Testing in Practice
• A/B Testing using Python (creminz GitHub Repository)
• A/B Testing Tutorial with Python (DataCamp)
• nbviewer.js

Proj-Part B [HackMD link] (due May 01)

HW3 due