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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

 

Programming

 

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. 

 

Python

 

• Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming
• Fluent Python, 2nd Edition
• Introducing Python: Modern Computing in Simple Packages
• High Performance Python: Practical Performant Programming for Humans

 

R

 

• Beginning R: The Statistical Programming Language
• R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
• Efficient R Programming: A Practical Guide to Smarter Programming
• R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics

 

Julia

 

• Think Julia: How to Think Like a Computer Scientist
• Beginning Julia Programming: For Engineers and Scientists
• Hands-On Design Patterns and Best Practices with Julia: Proven solutions to common problems in software design for Julia 1.x

 

SQL

 

• Learning SQL: Generate, Manipulate, and Retrieve Data
• SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights
• Practical SQL, 2nd Edition: A Beginner's Guide to Storytelling with Data

 

Scala

 

• Scala Cookbook: Recipes for Object-Oriented and Functional Programming
• Programming Scala: Scalability = Functional Programming + Objects
• Scala for the Impatient

 

Statistics

 

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. 

• Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
• Think Bayes: Bayesian Statistics in Python
• Think Stats: Exploratory Data Analysis
• Naked Statistics: Stripping the Dread from the Data

 

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. 

• Data Analytics Made Accessible: 2022 edition eBook
• Advancing into Analytics: From Excel to Python and R
• Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures
• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

 

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 Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
• Business Intelligence, Analytics, and Data Science: A Managerial Perspective: Sharda, Ramesh, Delen, Dursun, Turban, Efraim: 9780134633282: Books - Amazon
• Mastering Tableau 2021: Implement advanced business intelligence techniques and analytics with Tableau
• The Definitive Guide to DAX: Business Intelligence for Microsoft Power BI, SQL Server Analysis Services, and Excel 

 

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.

• Fundamentals of Data Engineering: Plan and Build Robust Data Systems
• Data Pipelines Pocket Reference: Moving and Processing Data for Analytics
• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
• Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python

 

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. 

• Web Scraping with Python: Collecting More Data from the Modern Web
• Hands-On Web Scraping with Python: Perform advanced scraping operations using various Python libraries and tools such as Selenium
• Practical Web Scraping for Data Science: Best Practices and Examples with Python
• Getting Structured Data from the Internet: Running Web Crawlers/Scrapers on a Big Data Production Scale

 

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. 

• Getting Started with Streamlit for Data Science: Create and deploy Streamlit web applications from scratch in Python
• Building Data Science Applications with FastAPI: Develop, manage, and deploy efficient machine learning applications with Python
• Flask Web Development: Developing Web Applications with Python
• Web Development with Django: Learn to build modern web applications with a Python-based framework

 

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. 

• Data Management at Scale: Best Practices for Enterprise Architecture
• MASTER DATA MANAGEMENT AND DATA GOVERNANCE
• Database Administration: The Complete Guide to Practices and Procedures: Mullins, Craig S.
• Database Internals: A Deep Dive into How Distributed Data Systems Work

 

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.

• Big Data: Principles and best practices of scalable realtime data systems eBook 
• Big Data Demystified: How to use big data, data science and AI to make better business decisions and gain competitive advantage
• Data Strategy: How to Profit from a World of Big Data, Analytics and Artificial Intelligence: Marr, Bernard
• The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science

 

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. 

• Kubernetes: Up and Running: Dive into the Future of Infrastructure
• Security and Microservice Architecture on AWS: Architecting and Implementing a Secured, Scalable Solution
• Design Patterns for Cloud Native Applications: Patterns in Practice Using APIs, Data, Events, and Streams eBook
• Cloud Without Compromise: Hybrid Cloud for the Enterprise

 

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.