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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 1.

 

The data science books have been an influential part of my data science journey. The Deep Learning for Coders with Fastai and PyTorch has made me think outside the box about deep neural networks and how we approach almost any machine learning issue. I am in love with NLP books and how they come with GitHub repositories, Jupyter notebooks exercise, and easy to explore options. Data Science at the Command Line is one of the books that are now available online (documentation style) with the ability to search terms, navigation, and copy the code directly to test the example. It provides an interactive reading experience for free. 

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. Some books are website-based and can be accessed for free. 

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

1. Machine Learning
2. Deep Learning
3. Computer Vision
4. Natural Language Processing
5. MLOps
6. Robotics
7. IoT
8. AI Products Management
9. Data Science for Executives
10. Data Science Super Books

 

Machine Learning

 

It is the most popular term in the field of data science. Most data professionals have to perform some kind of machine learning task, even if it is developing a simple linear regression model. These books will teach you the  basic and advanced concepts with code examples on the most popular frameworks. 

• The Hundred-Page Machine Learning Book: Burkov, Andriy
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• Introduction to Machine Learning with Python: A Guide for Data Scientists
• AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence

 

Deep Learning 

 

After simple machine learning we dive into the world of deep neural networks. It is the sub field of machine learning, and it is evolving the world rapidly. From computer vision to intelligent chat bots. You are interacting with them on a daily basis. These books will teach you how to create your first deep learning model and introduce you to the subfield of deep learning technologies. 

• Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
• Deep Learning with Python, Second Edition
• Deep Learning: A Visual Approach
• Deep Learning from Scratch: Building with Python from First Principles

 

Detailed training loop | Deep Learning for Coders with Fastai and PyTorch

 

Computer Vision

 

Computer vision is high in demand, and with the help of deep learning, this field is dominating the world. You can find it in warehouse management, robots, self-driving cars, facial recognition, generative art, and even in modern weapons. 

• Deep Learning for Vision Systems
• Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images
• Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning, 3rd Edition
• Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications

 

Natural Language Processing 

 

Learn how to create machine translation, automatic speech recognition, summarizer, text & audio classification, and conversation bot. Natural Language Processing is a whole new world in data science. You are interacting with audio, visual, and text data to make sense of context and words. With the introduction of transformers, this field has seen a real boost in research and development. We are now training models with 176 billion parameters - bigscience. 

• Natural Language Processing with Transformers: Building Language Applications with Hugging Face
• Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems
• Real-World Natural Language Processing: Practical applications with deep learning
• Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand

 

The transformers timeline | Natural Language Processing with Transformers

 

MLOps

 

You will learn to create machine learning pipelines, deploy the application on the cloud, maintain multiple databases, and learn to automate all of the processes. Machine learning operations are driven by developing operations where engineers automate processes, monitor metrics, and manage multiple systems. If you want to become future-proof, invest your money and time in learning MLOps. 

• Introducing MLOps: How to Scale Machine Learning in the Enterprise
• Practical MLOps: Operationalizing Machine Learning Models
• MLOps Engineering at Scale
• Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

 

Robotics

 

It is not a core part of data science, but it has been part of artificial intelligence for a long period. You can learn to train and develop your machine learning model on Raspberry Pi using Python or create edge applications. Robotics is the future, and if you want to stay relevant, I will highly recommend you to at least learn the basics. 

• Learn Robotics Programming: Build and control AI-enabled autonomous robots using the Raspberry Pi and Python, 2nd Edition
• Robotic Process Automation Projects: Build real-world RPA solutions using UiPath and Automation Anywhere
• The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems
• Artificial Intelligence for Robotics: Build intelligent robots that perform human tasks using AI techniques

 

IoT

 

The Internet of things is everywhere. These are smartphones, smartwatches, sensors on the wall, and even your digital fridge. We are surrounded by these sensors that are collecting and generating large amounts of data every hour. You will learn to build server-side applications with Rust and integrate them with Raspberry PI and the cloud system. You will also learn about smart cities, IoT security, and Tensorflow Lite on microcontrollers. 

• Internet of Everything and Big Data: Major Challenges in Smart Cities (Internet of Everything (IoE))
• Practical IoT Hacking: The Definitive Guide to Attacking the Internet of Things
• Rust for the IoT: Building Internet of Things Apps with Rust and Raspberry P
• TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

 

Image from Practical IoT Hacking

 

AI Products Management

 

You cannot put any MBA graduate to manage data teams. This business person needs to understand how these systems work and how to manage the data. AI product manager involves in procuring and processing data, creating strategies for labeling the data and understanding the business issue and purpose solution. To become a successful AI manager, you will need both business understanding and technical expertise. 

• Applying Artificial Intelligence to Project Management
• Demystifying AI for the Enterprise
• Risk Intelligence: How Artificial Intelligence can transform Risk Management (The Future of ERM Book 2) eBook
• Managing AI in the Enterprise: Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations

 

Data Science for Executives

 

The non-technical books for higher managers and executives who are responsible for making decisions based on ROI and growth potential. You will learn how other companies are getting better at managing data projects and how to leverage machine learning to drive business. 

• Data Science for Executives: Leveraging Machine Intelligence to Drive Business ROI
• The Decision Maker's Handbook to Data Science: A Guide for Non-Technical Executives, Managers, and Founders
• How to Lead in Data ScienceCompeting in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World
• How to Lead in Data Science

 

Data Science Super Books

 

These books cover all parts of data science, from statistics to advanced machine learning algorithms. You will review data science interviews, understand how to manage data, and learn all the basics to get started. 

One book to rule them all. 

• Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street
• Becoming a Data Head: How to Think, Speak and Understand Data Science, Statistics and Machine Learning
• Data Science from Scratch: First Principles with Python
• Python Data Science Handbook: Essential Tools for Working with Data

 

Closing Thoughts

 

Data science is not just statistics and coding. We need to understand business problems and come up with optimum solutions. Not everything is solved by machine learning. We also need to comprehend how MLOps and other integrated systems are essential for the success of the data application. 

In the previous part, we have reviewed books on Programming Languages, Statistics, Data Engineering, Web Scraping, Data Analytics, Business Intelligence, Data Applications, Data Management, Big Data, and Cloud Architecture. 

 

"I will highly recommend you to bookmark both pages so, instead of searching books online, you can have access to the best book in the specific field of data science."

 
 
 
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.