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𝗢'𝗥𝗲𝗶𝗹𝗹𝘆'𝘀 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗲𝗕𝗼𝗼𝗸 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 – ₱395 (148 eBooks in PDF/EPUB format) ℹ️ O’Reilly’s Data Engineering, Analytics, and Data Science Collection brings together the publisher’s entire eBook catalog across data engineering, analytics, and data science, grouped under its Data / Data Science category. It covers the field from foundational principles to large-scale systems, spanning data pipelines, analytics engineering, visualization, governance, streaming, lakehouse and warehouse architectures, and machine learning within data workflows. The collection continuously expands as new titles and updated editions are released. 📋 𝗕𝗼𝗼𝗸 𝗟𝗶𝘀𝘁: 1. 97 Things About Ethics Everyone in Data Science Should Know - Collective Wisdom from the Experts 2. 97 Things Every Data Engineer Should Know - Collective Wisdom from the Experts 3. Advanced Analytics with PySpark - Patterns for Learning from Data at Scale Using Python and Spark 4. Advanced Analytics with Spark - Patterns for Learning from Data at Scale 5. Advancing into Analytics - From Excel to Python and R 6. Agile Data Science 2.0 - Building Full-Stack Data Analytics Applications with Spark 7. Amazon Redshift - The Definitive Guide - Jump-Start Analytics Using Cloud Data Warehousing 8. Analytical Skills for AI and Data Science - Building Skills for an AI-Driven Enterprise 9. Analytics Engineering with SQL and dbt - Building Meaningful Data Models at Scale 10. Analyzing the Analyzers - An Introspective Survey of Data Scientists and Their Work 11. Apache Hudi: The Definitive Guide - Building Robust, Open, and High-Performing Data Lakehouses 12. Apache Iceberg - The Definitive Guide - Data Lakehouse Functionality, Performance, and Scalability on the Data Lake 13. Applied Text Analysis with Python - Enabling Language-Aware Data Products with Machine Learning 14. Architecting Data and Machine Learning Platforms - Enable Analytics and AI-Driven Innovation in the Cloud 15. Augmented Analytics - Enabling Analytics Transformation for Data-Informed Decisions 16. Automating Data Quality Monitoring 17. Bad Data Handbook - Cleaning Up The Data So You Can Get Back To Work 18. Beautiful Data - The Stories Behind Elegant Data Solutions 19. Beautiful Visualization - Looking at Data through the Eyes of Experts 20. Behavioral Data Analysis with R and Python - Customer-Driven Data for Real Business Results 21. Bioinformatics Data Skills - Reproducible and Robust Research with Open Source Tools 22. Blueprints for Text Analytics Using Python - Machine Learning-Based Solutions for Common Real World (NLP) Applications 23. Building Knowledge Graphs - A Practitioner's Guide 24. Building Medallion Architectures: Designing with Delta Lake and Spark 25. Building Real-Time Analytics Systems - From Events to Insights with Apache Kafka and Apache Pinot 26. Cloud Native Data Center Networking - Architecture, Protocols, and Tools 27. ColorWise - A Data Storyteller's Guide to the Intentional Use of Color 28. Communicating Data with Tableau - Designing, Developing, and Delivering Data Visualizations 29. Cost-Effective Data Pipelines - Balancing Trade-Offs When Developing Pipelines in the Cloud 30. Creating a Data-Driven Organization - Practical Advice from the Trenches 31. Data Algorithms with Spark 32. Data Analysis with Open Source Tools - A Hands-On Guide for Programmers and Data Scientists 33. Data Analytics with Hadoop - An Introduction for Data Scientists 34. Data Curious - Applying Agile Analytics for Better Business Decisions 35. Data Governance - The Definitive Guide People, Processes, and Tools to Operationalize Data Trustworthiness 36. Data Management at Scale 37. Data Mesh - Delivering Data-Driven Value at Scale 38. Data Modeling with Microsoft Power BI 39. Data Pipelines Pocket Reference - Moving and Processing Data for Analytics 40. Data Quality Engineering in Financial Services - Applying Manufacturing Techniques to Data 41. Data Quality Fundamentals - A Practitioner's Guide to Building Trustworthy Data Pipelines 42. Data Science - The Hard Parts - Techniques for Excelling at Data Science 43. Data Science at the Command Line - Obtain, Scrub, Explore, and Model Data with Unix Power Tools 44. Data Science for Business - What You Need to Know about Data Mining and Data-Analytic Thinking 45. Data Science from Scratch - First Principles with Python 46. Data Science on AWS - Implementing End-to-End, Continuous AI and Machine Learning Pipelines 47. Data Science on the Google Cloud Platform - Implementing End-to-End Real-Time Data Pipelines From Ingest to Machine Learning 48. Data Science with Java - Practical Methods for Scientists and Engineers 49. Data Visualization with Python and JavaScript - Scrape, Clean, Explore, and Transform Your Data 50. Data Wrangling with Python - Tips and Tools to Make Your Life Easier 51. Database Internals - A Deep Dive into How Distributed Data Systems Work 52. Deciphering Data Architectures - Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh 53. Delta Lake Up and Running - Modern Data Lakehouse Architectures with Delta Lake 54. Designing Data-Intensive Applications - The Big Ideas Behind Reliable, Scalable, and Maintainable Systems 55. Doing Data Science - Straight Talk from the Frontline 56. DuckDB - Up and Running - Fast Data Analytics and Reporting.epub 57. Embedded Analytics - Integrating Analysis with the Business Workflow 58. Essential Math for Data Science - Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics 59. Ethics of Big Data - Balancing Risk and Innovation 60. Football Analytics with Python & R - Learning Data Science Through the Lens of Sports 61. Foundations for Analytics with Python - From Non-Programmer to Hacker 62. Fundamentals of Data Engineering - Plan and Build Robust Data Systems 63. Fundamentals of Data Observability 64. Fundamentals of Data Visualization - A Primer on Making Informative and Compelling Figures 65. Fuzzy Data Matching with SQL - Enhancing Data Quality and Query Performance 66. Google BigQuery - The Definitive Guide Data Warehousing, Analytics, and Machine Learning at Scale 67. Graph Databases - New Opportunities for Connected Data 68. Graph-Powered Analytics and Machine Learning with TigerGraph - Driving Business Outcomes with Connected Data 69. Graphing Data with R - An Introduction 70. Hadoop - The Definitive Guide - Storage and Analysis at Internet Scale 71. Hands-On Data Visualization - Interactive Storytelling From Spreadsheets to Code 72. Hands-On Entity Resolution - A Practical Guide to Data Matching with Python 73. Hands-On Healthcare Data - Taming the Complexity of Real-World Data 74. Hands-On Salesforce Data Cloud - Implementing and Managing a Real-Time Customer Data Platform 75. HBase The Definitive Guide - Random Access to Your Planet-Size Data 76. Head First Data Analysis - A learner's guide to big numbers, statistics, and good decisions 77. High Performance MySQL - Proven Strategies for Operating at Scale 78. Interactive Data Visualization for the Web - An Introduction to Designing with D3 79. Introduction to Machine Learning with Python - A Guide for Data Scientists 80. Lean Analytics - Use Data to Build a Better Startup Faster (Lean Series) 81. Learning and Operating Presto - Fast, Reliable SQL for Data Analytics and Lakehouses 82. Learning Apache Drill - Query and Analyze Distributed Data Sources with SQL 83. Learning Data Science - Data Wrangling, Exploration, Visualization, and Modeling with Python 84. Learning Google Analytics - Creating Business Impact and Driving Insights 85. Learning Microsoft Power Bi - Transforming Data Into Insights 86. Learning R - A Step-by-Step Function Guide to Data Analysis 87. Learning Ray - Flexible Distributed Python for Machine Learning 88. Learning Snowflake SQL and Scripting - Generate, Retrieve, and Automate Snowflake Data 89. Learning Spark - Lightning-Fast Data Analytics 90. Learning SQL - Generate, Manipulate, and Retrieve Data, 3rd Edition 91. Learning to Love Data Science 92. Machine Learning and Data Science Blueprints for Finance - From Building Trading Strategies to Robo-Advisors Using Python 93. Machine Learning Design Patterns - Solutions to Common Challenges in Data Preparation, Model Building, and MLOps 94. Machine Learning Pocket Reference - Working with Structured Data in Python 95. Making Data Visual - A Practical Guide to Using Visualization for Insight 96. MapReduce Design Patterns - Building Effective Algorithms and Analytics for Hadoop and Other Systems 97. Mastering Azure Analytics - Architecting in the Cloud with Azure Data Lake, HDInsight, and Spark 98. Mastering Spark with R - The Complete Guide to Large-Scale Analysis and Modeling 99. Mining the Social Web - Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More 100. Modern Business Analytics - Increasing the Value of Your Data with Python and R 101. Modern Data Analytics in Excel - Using Power Query, Power Pivot, and More for Enhanced Data Analytics 102. MongoDB - The Definitive Guide - Powerful and Scalable Data Storage 103. Network Security Through Data Analysis - From Data to Action 104. Oracle SQL Plus - The Definitive Guide 105. Parallel R - Data Analysis in the Distributed World 106. Practical Data Privacy 107. Practical Fraud Prevention - Fraud and AML Analytics for Fintech and eCommerce, Using SQL and Python 108. Practical Lakehouse Architecture 109. Practical Linear Algebra for Data Science - From Core Concepts to Applications Using Python 110. Practical Python - Data Wrangling and Data Quality 111. Practical Statistics for Data Scientists - 50+ Essential Concepts Using R and Python 112. Practical Synthetic Data Generation - Balancing Privacy and the Broad Availability of Data 113. Predictive Analytics for the Modern Enterprise 114. Puppet Types and Providers - Extending Puppet with Ruby 115. Python and R for the Modern Data Scientist - The Best of Both Worlds 116. Python Data Science - Handbook Essential Tools for Working with Data 117. Python for Data Analysis - Data Wrangling with pandas, NumPy, and Jupyter, 3rd Edition 118. Python for Excel - A Modern Environment for Automation and Data Analysis 119. Python for Finance - Mastering Data-Driven Finance 120. Python for Geospatial Data Analysis - Theory, Tools, and Practice for Location Intelligence 121. R Cookbook - Proven Recipes for Data Analysis, Statistics, and Graphics 122. R for Data Science - Import, Tidy, Transform, Visualize, and Model Data 123. R Graphics Cookbook - Practical Recipes for Visualizing Data 124. Semantic Modeling for Data - Avoiding Pitfalls and Breaking Dilemmas 125. Software Engineering for Data Scientists - From Notebooks to Scalable Systems 126. Spark - The Definitive Guide - Big Data Processing Made Simple 127. SQL Cookbook - Query Solutions and Techniques for All SQL Users 128. SQL for Data Analysis - Advanced Techniques for Transforming Data into Insights 129. Stream Processing with Apache Flink - Fundamentals, Implementation, and Operation of Streaming Applications 130. Tableau Prep - Up & Running - Self-Service Data Preparation for Better Analysis 131. Tableau Strategies - Solving Real, Practical Problems with Data Analytics 132. Text Mining with R - A Tidy Approach 133. The Cloud Data Lake 134. The Data Science Design Manual 135. The Enterprise Big Data Lake - Delivering the Promise of Big Data and Data Science 136. The Enterprise Data Catalog - Improve Data Discovery, Ensure Data Governance, and Enable Innovation 137. The Practitioner's Guide to Graph Data - Applying Graph Thinking and Graph Technologies to Solve Complex Problems 138. The Self-Service Data Roadmap - Democratize Data and Reduce Time to Insight 139. Think Bayes - Bayesian Statistics in Python 140. Think Data Structures - Algorithms and Information Retrieval in Java 141. Think Python - How to Think Like a Computer Scientist 142. Think Stats - Exploratory Data Analysis 143. Think Stats: Exploratory Data Analysis, 3rd Edition 144. Thinking with Data - How to Turn Information into Insights 145. Time Series Databases - New Ways to Store and Access Data 146. Training Data for Machine Learning - Human Supervision from Annotation to Data Science 147. Understanding Compression - Data Compression for Modern Developers 148. Unifying Business, Data, and Code - Designing Data Products with JSON Schema 💬 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 𝘂𝘀 𝘁𝗼 𝗮𝘃𝗮𝗶𝗹.