Open Access Books

1. Foundational Programming Skills (Python and R)

Begin with these books to build programming fundamentals, essential for data engineering, data science, and analytics.

1. Python for Everybody - https://books.trinket.io/pfe/index.html
2. Automate the Boring Stuff - https://automatetheboringstuff.com/
3. Python datasciencebook - https://python.datasciencebook.ca/
4. R for Data Science - https://r4ds.had.co.nz/
5. Data Science - R Basics - https://rafalab.dfci.harvard.edu/dsbook/

2. Practical Programming and Data Manipulation

Expand your programming skills by exploring libraries and frameworks for data manipulation.

1. Modern Polars - https://kevinheavey.github.io/modern-polars/
2. Python for Geocomputation - https://py.geocompx.org/
3. Hands-On Programming with R - https://rstudio-education.github.io/hopr/
4. R Packages - https://r-pkgs.org/

3. Data Engineering Foundations

Develop a solid understanding of data engineering principles, design patterns, and tools.

1. Fundamentals of Data Engineering - https://redpanda.com/guides/fundamentals-of-data-engineering
2. Modern Data Engineering Playbook - https://www.thoughtworks.com/en-cl/what-we-do/data-and-ai/modern-data-engineering-playbook
3. Data Engineering Design Patterns (DEDP) - https://www.dedp.online/about-this-book.html
4. The Data Engineering Cookbook - https://github.com/project303/The-Data-Engineering-Cookbook-
5. Data Engineering Vault: A Second Brain Knowledge Network - https://www.ssp.sh/brain/data-engineering/

4. Database and Data Management

Learn about databases, data models, and data management practices.

1. Database Technology Overview - https://halvorsen.blog/documents/technology/database/database.php
2. Erwin in Database Design - https://halvorsen.blog/documents/technology/database/erwin.php
3. Data Management in R - https://dmbook.org/

5. Data Visualization and Communication

Focus on effective data visualization and storytelling with data.

1. Data Visualization - A Practical Introduction - https://socviz.co/index.html?#preface
2. Interactive Data Visualization - https://jjallaire.github.io/visualization-curriculum/
3. Telling Stories with Data - https://tellingstorieswithdata.com/

6. Statistical Thinking and Data Science Techniques

Build statistical knowledge and explore techniques used in data science.

1. Exploratory Data Analysis with R - https://bookdown.org/rdpeng/exdata/
2. The Art of Data Science - https://bookdown.org/rdpeng/artofdatascience/

7. Advanced Topics in Machine Learning and Forecasting

Deepen your knowledge by studying advanced machine learning, forecasting, and time series analysis.

1. Elements of Statistical Learning - https://hastie.su.domains/ElemStatLearn/
2. Pattern Recognition and Machine Learning - https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/
3. Mathematics for Machine Learning - https://mml-book.github.io/
4. Machine Learning - a Probabilistic Perspective - https://probml.github.io/pml-book/book0.html
5. Time Series Analysis with R - https://vlyubchich.github.io/tsar/
6. Forecasting: Principles and Practice with R - https://otexts.com/fpp2/

8. Specialized Areas and Use Cases

Explore books focused on specific domains and practical applications of data analysis.

1. Spatial Data Science - https://www.paulamoraga.com/book-spatial/index.html
2. Cookbook for R Polars - https://ddotta.github.io/cookbook-rpolars/
3. NFL Analytics with R - https://bradcongelio.com/nfl-analytics-with-r-book/
4. mlr3book - https://mlr3book.mlr-org.com/
5. Pandas for Everyone - https://wesmckinney.com/book/

9. Web Scraping and Data Collection

Learn how to automate data collection through web scraping techniques.

1. Web Scraping with R - https://steviep42.github.io/webscraping/book/