Data Science Live Book

Abstract

1. Introduction

Most of the written R code can be used in real scenarios! I worked on the funModeling R package at the same time, so it is used many times in the book.

It's a playbook with full of data preparation receipts.

I.e. in the missing values chapter you'll find how to input and convert these values into something useful for both, analysis and predictive modeling.

2. Objective

An open source book to learn data science, data analysis and machine learning, suitable for all ages!

This project demonstrates how TeX, Machine Learning can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/pablo14/data-science-live-book.git
cd data-science-live-book

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by pablo14 and published on GitHub under the Creative Commons Attribution Share Alike 4.0 International. Please follow the license terms and credit the original author when you use or modify this code.