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
Google Colab Notebook (Python Crash Course)
- Click [here]
A quick review exercise (download the zip file-unzip-use .pynb file)
- Click [Python_Starter_Exercises.ipynb]
- Click [python_starter_exercises.py]
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:
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)
Open Jupyter Notebook
- Please follow this link to know about how to open Jupyter Notebook from your Local Machine (in your specified directory)
- For a complete User Manual check out the Bryn Mawr College Computer Science Guide.
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)
- Click here
Dataset : Loan_Approval_Data
Exercise: 1 for Pandas
- Download .ipynb file from here [SF_Salaries_Exercise.ipynb] ( It’s a zip file . You need to Unzip and use)
- Dataset you can download from here [Salaries]
Exercise 2 for Pandas
- Dataset you can download from here [Sales_Data]
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:
- Click here
Code Along for Python Seaborn (Goolge Colab link)
- Click here
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