Data Science Handbook

Welcome to the Data Science Handbook! This repository contains a collection of notes, code examples, and resources on various topics in data science, including data exploration, data manipulation, data visualization, web scraping, feature engineering, feature selection, and Scipy in Python.

Table of Contents

1. Data Exploration Techniques in Python
2. Data Manipulation Using pandas
3. Data Wrangling in Python
4. Exploratory Data Analysis (EDA)
5. Data Visualization with Matplotlib and Seaborn
6. Web Scraping with Beautiful Soup
7. Feature Engineering
8. Feature Selection, Regression, Factor Analysis, Principal Component Analysis, Eigenvalues and PCA
9. Handling Missing Values and Outlier Values in a Dataset in Python
10. Label Encoding
11. Various Functionality of Data Objects in Python
12. Types of Plots
13. Numpy Cheatsheet
14. Scipy
15. Data Science Project
16. Code Templates and Resources
17. Exercises and Practice Problems

Author

The Data Science Handbook was created by Prateek Tripathi. Prateek is a data science enthusiast who loves to think in terms of matrices and is always looking for new ways to apply data science to real-world problems. You can reach out to him at apkadost888@gmail.com.

To get started, I recommend the following path:

1. Begin by reading through the notes on data exploration techniques and exploratory data analysis (EDA). These will introduce you to the basics of analyzing and understanding your data.
2. Next, move on to the notes on data manipulation using pandas and data wrangling in Python. These will teach you how to clean, transform, and prepare your data for further analysis.
3. Explore the notes on data visualization using matplotlib and seaborn, and learn how to create different types of plots to visualize and communicate your findings.
4. If you need to gather data from the web, check out the notes on web scraping with beautiful soup.
5. Move on to the notes on feature engineering and feature selection, and learn how to create and select the most important features in your data.
6. Finally, read through the notes on scipy, which covers a variety of useful tools and techniques for scientific computing in Python

Additional Resources

1. DataCamp - Online courses and interactive tutorials on data science and programming
2. Kaggle - A platform for data science competitions, projects, and resources
3. Towards Data Science - A publication featuring articles, tutorials, and insights on data science @

Python for Data Science Handbook

A comprehensive guide to Python for data science I hope you find the Data Science Handbook useful in your journey to learn more about this exciting field!