Meet with Python 🐍

Python – A Quick Intro

In this module, you will get a quick review on Python Language. We will not going in depth but we will try to discuss some important components of Python Language. Please note, this is not meant to be a comprehensive overview of Python or programming in general 

Hands-on : Environment Setup and  Jupyter Notebook Intro. 
Hands-on : Python Code Along
Hands-on : Python Review Exercise
Python Packages Cheat Sheet

Google Colab Notebook (Python Crash Course)

A quick review exercise (download the zip file-unzip-use .pynb file)

Mindmap for Python

I wanted to point out some helpful links for practice. Don’t worry about being able to do these exercises, I just want you to be aware of the links so you can visit them later. 

Basic Practice:

http://codingbat.com/python

More Mathematical (and Harder) Practice:

https://projecteuler.net/archives

List of Practice Problems:

http://www.codeabbey.com/index/task_list

A SubReddit Devoted to Daily Practice Problems:

https://www.reddit.com/r/dailyprogrammer

A very tricky website with very few hints and touch problems (Not for beginners but still interesting)

http://www.pythonchallenge.com/

Open Jupyter Notebook

Dance with Python 🐍

Data Analysis🧐 & Visualization 📊

Module 1: Python for Data Analysis (Pandas )

Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

Hands-on : Using Python Pandas Library 

 

Module 2: Data Visualization/EDA/Data Analysis (Seaborn) 

In this part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression.

Hands-on : Using Python Seaborn Visualization Library

Code Along for Python Pandas (Goolge Colab link)

Dataset : Loan_Approval_Data

Exercise: 1 for Pandas 

Exercise 2 for Pandas

From above data, you need to find below business questions:

  • Question 1: What was the best month for sales? How much was earned that month?
  • Question 2: Which state sold the most product?
  • Question 3: What time should we display advertisements to maximize likelihood of customer’s buying product?
  • Question 4: What products are most often sold together?

Solution Link:

Code Along for Python Seaborn (Goolge Colab link)

Capstone Project 

  • Download .ipynb file from here [01-911 Calls Data Capstone Project] ( It’s a zip file . You need to Unzip and use) 
  • Dataset you can download from here [911] ( It’s a zip file . You need to Unzip and use) 
  • Solution Colab Link is here