Abstract
The Data Analysis Workshop is an open-source Data Science project. A New Interactive Approach to Learning Data Analysis. The Data Analysis Workshop is a comprehensive guide that shows you how to analyze your data and gain insights into your business. Starting with the basics of data analysis, including data visualization and exploratory data analysis, this book takes you through the complete spectrum of techniques, such as time series analysis and categorical data analysis. It is built using Jupyter Notebook, Python. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
The Data Analysis Workshop is a comprehensive guide that shows you how to analyze your data and gain insights into your business. Starting with the basics of data analysis, including data visualization and exploratory data analysis, this book takes you through the complete spectrum of techniques, such as time series analysis and categorical data analysis. It is the ideal companion on your journey to becoming an expert data analyst.
This is the repository for The Data Analysis Workshop, published by Packt. It contains all the supporting project files necessary to work through the course from start to finish.
2. Objective
A New Interactive Approach to Learning Data Analysis
This project demonstrates how Jupyter Notebook, Python 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.
- Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
- pip for dependencies
- Python 3.8 or later
- pip / virtualenv for dependencies
- VS Code, PyCharm or Jupyter Notebook
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/PacktWorkshops/The-Data-Analysis-Workshop.git
cd The-Data-Analysis-Workshop- Install Python on Windows/Mac/Linux
- Install Anaconda on Windows/Mac/Linux
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.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by PacktWorkshops 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.
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