Learnr

Abstract

1. Introduction

The one common issue which novice entrants in data science have always asked me is, "How to begin their data analysis journey?". My answer has always been the same, "Tell a data powered story!". But, one will be in a position to tell a "story" only if he/she knows the nuts and bolts of the story. If you are a novice in statistics or applied mathematics, I recommend to check out the resources folder, first.

The repository contains data, code, resources and plots folders.

Ask your question on Stack Overflow or the R-SIG-Finance mailing list (you must subscribe to post).

2. Objective

Exploratory, Inferential and Predictive data analysis. Feel free to show your :heart: by giving a star :star:

This project demonstrates how R 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/duttashi/learnr.git
cd learnr

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 duttashi and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.