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The Complete Collection of Data Science Books – Part 1

Read the best books on Programming, Statistics, Data Engineering, Web Scraping, Data Analytics, Business Intelligence, Data Applications, Data Management, Big Data, and Cloud Architecture.


The Complete Collection of Data Science Books - Part 1
Image by Author


Editor's note: For the full scope of Data Science Books included in this 2 part series, please see The Complete Collection of Data Science Books – Part 2.


Books in this modern age have completely changed. Instead of words on paper, you can read books on your smartphone, desktop, and tablets. Some books are website-based, where you can explore chapters, search terms, or even play video tutorials when reading a book. These documentation-style books enhance the reading experience and make it quite fun to test coding examples.

In this two-part series, I will share the best books on all of the subfields of data science. You can buy the hard copy or simply get access to the online version or download the PDF/EPub/Kindle. There are some books that are website-based and can be accessed for free. 

In the first part, we'll be reviewing books on:

  1. Programming
  2. Statistics
  3. Data Analytics
  4. Business Intelligence
  5. Data Engineering
  6. Web Scraping
  7. Data Applications
  8. Data Management
  9. Big Data
  10. Cloud Architecture




If you are a beginner, learning programming should be first on your list. At the start, you will choose between Python, R, and Julia, but I will highly recommend you to start with Python. After that, learn SQL and Scala to advance in your career. 



















Statistics are the backbones of modern data science and machine learning developments. Without it, you cannot understand algorithms or conduct research. Instead of learning everything, I will suggest you learn the basics and then learn as you go. 


Photo by Karolina Grabowska


Data Analytics


Some of the tools mentioned in these books makes data analytics pieces of cake. It is not about writing code to generate data visualization. It is about understanding the data with the help of graphs and visual representation. 


Business Intelligence


Business Intelligence tools are the most important part of modern business. You will learn how to create reports, track performance, develop dashboards, scrap data, and manage data sources. 


Data Engineering


Building data pipelines, planning data management strategies, processing the data and providing secure access to various team members. Data engineers also work on scalable and flexible storage systems.


Modern Infrastructure
Modern Infrastructure | Image by Author


Web Scraping


Web scraping has become a core part of data scientists and analysts jobs. Even in a technical interview or test, you have to show some skill in understanding the parsing of the HTML data using BeautifulSoup and Selenium. It also gives you the ability to create fully automated systems. 


Data Applications


After creating a machine learning model or performing intensive data analysis, it is time to create a web application so that you can share your project with other team members. You can create an API or web app by using FastAPI, Flask, Streamlit, and Django. 


Data Management


Your data team is expanding and you are gathering more data with time. It is time to work on managing your data using distributed databases, data warehouses, data lakes, and tools. These tools will allow you to scale your current data systems. 


Big Data


Our conventional database systems are not made to collect petabytes of daily data. These books will help you learn scalable, easy-to-understand approaches to big data systems that can be built and run by a small team. You will also learn about technologies like Hadoop, Storm, and NoSQL databases.


The four stage of maturity | Image from The Enterprise Big Data Lake


Cloud Architecture


Even though learning about cloud architecture is not the core skill of data scientists, it is getting popular in data communities. AI-based companies want machine learning, MLOps, and data engineers to understand Kubernetes, Docker, API integrations, distributed computing, monitoring compute, and Hybrid cloud solutions. 


Closing Thoughts 


The data science books teach you about all technical concepts with the help of code examples. You are not just reading books for research, you are building your skills. Most books will encourage you to code as long, so that you understand the concept better by debugging the issues. 

If you are a data science enthusiast, just like me, you want to keep learning. So, in the next part we will learn about the best books on machine learning, deep learning, computer vision, NLP, MLOps, robotics, IoT, AI products management, data science for executives, and data science super books.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.