interview-prep-Data-Science

Interview preparation of data science and machine learning

Data Science Interview Questions Repository

Welcome to the Data Science Interview Questions repository! This project aims to compile a comprehensive and well-organized collection of interview questions that cover all the essential topics in data science. Whether you're preparing for a job interview or looking to brush up on your knowledge, this repository will be a valuable resource.

Project Overview

This repository contains a curated list of interview questions organized by key topics within data science, including but not limited to:

• NumPy: Fundamental questions on array manipulation and numerical operations.
• Pandas: Essential questions on data manipulation, analysis, and handling.
• Machine Learning: Core questions on algorithms, models, and practical scenarios.
• Statistics: Important questions covering statistical concepts and their application.
• Python: General programming questions relevant to data science.

Each set of questions is organized in order of importance, with the most critical and frequently asked questions listed first. Additionally, scenario-based and practical questions are provided to test your understanding in real-world applications.

Table of Contents

python

statistics

numpy

pandas

matplotlib

seaborn

machine learning

deep learning

computer vision

nlp

gen ai

How to Use This Repository

1. Preparation: Use this repository as a guide to prepare for data science interviews. Start with the most important questions in each section and gradually work your way down.
2. Practice: Solve the practical and scenario-based questions to enhance your problem-solving skills.
3. Contribution: Feel free to contribute by adding new questions or improving existing ones. Follow the contribution guidelines here.

Contributing

Contributions are welcome! If you have additional questions, corrections, or improvements, please fork the repository and submit a pull request. Ensure that your contributions align with the structure and format of the existing content.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Special thanks to all the contributors who have helped in curating this list.
• Thanks to the data science community for providing valuable feedback and suggestions.

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Happy studying and good luck with your interviews!