Data Science Library

Data Science Library is a repository containing Materials, Books, Guides and References for Data Science Technologies, Techniques and Interviews.

Note : The resources in this repository have been collected and consolidated from various Internet Sources and Social Medias. The credit for each resource belongs to the respective publishers and authors. Please use the resources wisely to avoid Copyright issues.

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Name		Name	Last commit message	Last commit date
Latest commit History 84 Commits
Introduction to Statistics - Coursera Notes		Introduction to Statistics - Coursera Notes
.gitignore		.gitignore
120 Data Science Interview Questions.pdf		120 Data Science Interview Questions.pdf
41 Essential Machine Learning Interview Questions.pdf		41 Essential Machine Learning Interview Questions.pdf
500+ Data Engineering Interview Questions and Answers.pdf		500+ Data Engineering Interview Questions and Answers.pdf
60 Days DSA Challenge.pdf		60 Days DSA Challenge.pdf
96 Things Every Cloud Engineer Should Know.pdf		96 Things Every Cloud Engineer Should Know.pdf
AI for Data Science.pdf		AI for Data Science.pdf
Advanced Applied Deep Learning.pdf		Advanced Applied Deep Learning.pdf
Algorithms notes for Professionals.pdf		Algorithms notes for Professionals.pdf
Analytics in a Big Data World.pdf		Analytics in a Big Data World.pdf
Apache Spark with Python.pdf		Apache Spark with Python.pdf
Applied Natural Language Processing with Python.pdf		Applied Natural Language Processing with Python.pdf
Approaching Almost Any Machine Learning Problem.pdf		Approaching Almost Any Machine Learning Problem.pdf
Art and Practice of Data Science Pipelines.pdf		Art and Practice of Data Science Pipelines.pdf
Azure DevOps.pdf		Azure DevOps.pdf
Azure Fundamentals Quick Reference Guide.pdf		Azure Fundamentals Quick Reference Guide.pdf
Azure Machine Learning.pdf		Azure Machine Learning.pdf
Big Data and Business Analytics comes of age.pdf		Big Data and Business Analytics comes of age.pdf
Brief Introduction to Neural Networks.pdf		Brief Introduction to Neural Networks.pdf
Clean Code Handbook - Leetcode.pdf		Clean Code Handbook - Leetcode.pdf
Cloud Application Architectures.pdf		Cloud Application Architectures.pdf
Cloud Design Patterns.pdf		Cloud Design Patterns.pdf
Cognitive Biases.pdf		Cognitive Biases.pdf
Complete Data Structures and Algorithms Guide.pdf		Complete Data Structures and Algorithms Guide.pdf
DS Interview q and a.pdf		DS Interview q and a.pdf
Data Analytics and AI.pdf		Data Analytics and AI.pdf
Data Collection guide.pdf		Data Collection guide.pdf
Data Engineering Cookbook.pdf		Data Engineering Cookbook.pdf
Data Pipelines Pocket Reference.pdf		Data Pipelines Pocket Reference.pdf
Data Science Cheatsheet.pdf		Data Science Cheatsheet.pdf
Data Science Questions and Answers.pdf		Data Science Questions and Answers.pdf
Data Science from Scratch by Steven Cooper.pdf		Data Science from Scratch by Steven Cooper.pdf
Data Structures and Algorithms.pdf		Data Structures and Algorithms.pdf
Deep Learning From Basics to Practice.pdf		Deep Learning From Basics to Practice.pdf
Docker Cookbook.pdf		Docker Cookbook.pdf
Elements of Statistical Learning.pdf		Elements of Statistical Learning.pdf
Executive Guide to Data Science and AI.pdf		Executive Guide to Data Science and AI.pdf
Facebook joins Google in HPC Computing Architectures for Big Data.pdf		Facebook joins Google in HPC Computing Architectures for Big Data.pdf
Fundamentals of Data Visualization.pdf		Fundamentals of Data Visualization.pdf
Introducing MLOps.pdf		Introducing MLOps.pdf
Introduction to 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴.pdf		Introduction to 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴.pdf
Kubernetes Up & Running.pdf		Kubernetes Up & Running.pdf
Lean AI.pdf		Lean AI.pdf
Learning to Love Data Science .pdf		Learning to Love Data Science .pdf
ML.pdf		ML.pdf
Machine Learning Interview Cheat Sheets.pdf		Machine Learning Interview Cheat Sheets.pdf
Machine Learning Operations (MLOps) Overview, Definition, and Architecture.pdf		Machine Learning Operations (MLOps) Overview, Definition, and Architecture.pdf
Machine Learning Yearning.pdf		Machine Learning Yearning.pdf
Machine Learning for Everyone.pdf		Machine Learning for Everyone.pdf
Mathematics for Machine Learning.pdf		Mathematics for Machine Learning.pdf
Maths for ML.pdf		Maths for ML.pdf
Medium and Hard Data Analyst SQL Interview Questions.pdf		Medium and Hard Data Analyst SQL Interview Questions.pdf
Neural Networks and Deep Learning.pdf		Neural Networks and Deep Learning.pdf
Power BI for Intermediates.pdf		Power BI for Intermediates.pdf
Practical Statistics for Data Scientists.pdf		Practical Statistics for Data Scientists.pdf
Practitioner's Guide to MLOps.pdf		Practitioner's Guide to MLOps.pdf
Principles of Data Analysis.pdf		Principles of Data Analysis.pdf
Project Manger’s Guide to Kanban.pdf		Project Manger’s Guide to Kanban.pdf
README.md		README.md
SQL Notes.pdf		SQL Notes.pdf
SQL for Data Science.pdf		SQL for Data Science.pdf
Statistical methods for data science and machine learning.pdf		Statistical methods for data science and machine learning.pdf
Statistics cheat sheet.pdf		Statistics cheat sheet.pdf
Storytelling with Data.pdf		Storytelling with Data.pdf
Supervised Machine Learning.pdf		Supervised Machine Learning.pdf
Tableau Reference.pdf		Tableau Reference.pdf
The Analytics Setup Guidebook.pdf		The Analytics Setup Guidebook.pdf
The DevOps Handbook		The DevOps Handbook
The Site Reliability Workbook.pdf		The Site Reliability Workbook.pdf
The Ultimate Guide to Basic Data Cleaning.pdf		The Ultimate Guide to Basic Data Cleaning.pdf
Top 50 ML Interview Questions.pdf		Top 50 ML Interview Questions.pdf
Ultimate guide to data science interviews.pdf		Ultimate guide to data science interviews.pdf

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