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Ultimate Guide: Best Books for Data Science with Ratings for All Levels

Ultimate Guide: Best Books for Data Science with Ratings for All Levels

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The Data Science domain is dynamic and staying current is mandatory, Some indispensable books can make you up-to-date and expert whether you are just starting out or Pro as each book is assessed with its individual attributes with ratings(1 to 5 stars).

As you know, Data Science is a discipline that thrives on continuous learning and there is a vast sea of Book recommendations for Data science that encompasses the basics of Python to deep learning, but the books here are chosen due to their practicality, clarity of concepts and experience level (Beginner to Advance).

Understanding Subjects like Statistics, Machine Learning, Deep Learning and Neural Networks make a data scientist validated in the data science field, Learning them from proficient sources can make things/concepts/skills very easy. Therefore better to learn from the people who have mastered the domain with practical knowledge.

Having said that, if you still know some books that are useful for Data scientists, I encourage you to mention them in the comments, as many people love to learn from different sources to enhance their knowledge and skills. Also, if you have any other kind of recommendation regarding data science, feel free to share that as well.

So, whether you are new to the realm of Data Science or looking to polish your skills for advancement in your career, Below are the books that could be the perfect match for your Journey. Let’s Dive in:

Beginner Level:

This Level Includes all the basics that you need to get started with data science or advanced data analytics.

  1. **“Python for Data Analysis” by Wes Mckinney**

Ratings: 4.5/5 (★★★★½)

Python for Data Analysis” is a highly valued book for people getting started in data science or data analysis. Mckinney’s clear and concise explanations make it accessible for people with minimal programming expertise.

The book begins by introducing fundamental tools and libraries for data analysis in Python. One of the standout features is its emphasis on real-world applications, which makes some complex topics more digestible and approachable for readers.

Python for Data Analysis covers topics and tools like Numpy, Pandas and Matplotlib for visualization. In short, this book makes readers proficient in tackling real-world data by harnessing some insights from that data.

It is a gem for people getting started in data analytics, data science and even data engineering as it covers all the basics needed for these domains.


  • Clarity (5/5): This book excels in explaining complex concepts in a simple and clear manner.

  • Practicality (4/5): Offers hands-on examples and real-world use cases.

  • Coverage (4/5): Covers essential data manipulation and analysis techniques using Python.

  • Applicability (5/5): Ideal for beginners who want to start their journey with Python in data science.

2. “Python Data Science Handbook” by Jake VanderPlas

Ratings: 5/5 (★★★★★)

A trusty data science companion for beginners in the world of Python.

This book covers all the concepts of data analysis plus some basics of machine learning using Scikit-Learn. Jake VanderPlas make readers write the code with practical examples using Pandas for data wrangling, Numpy for efficient manipulation of ndarrays, Matplotlib for stunning visualizations and when you are ready then gives the reader the basic insights for machine learning and statistics using Scikit-Learn.

It truly gives the proper roadmap for moving forward in the Data Science world. VanderPlas not only teach you to code but also “why” you should code this, so it does not feel like copying from him.

So, even if you are a newbie or a pro, the Python Data Science Handbook will benefit you either way.


  • Clarity (5/5): Offers a clear and concise explanation of data science concepts using Python.

  • Practicality (5/5): Contains numerous practical examples and code snippets.

  • Coverage (5/5): Covers various data science topics, including data manipulation, visualization, and machine learning.

  • Applicability (5/5): Ideal for those who want to learn data science using Python.

3.**Data Science for Business” by Foster Provost and Tom Fawcett**

Ratings: 4.5/5 (★★★★½)

A must-read for anyone who wants to get insights into businesses with a data-driven approach. Since Data Scientists often predict outcomes using Machine learning, Deep learning and Neural Networks, this book is a gold mine for learning Business problems with data science Solutions.

Data Science for Business is not about complex algorithms and technical jargon, it’s about understanding how data is manipulated/used to make smart business decisions. Foster and Tom break down, how to ask the right questions and use data to solve business queries.

The best part is that everything is explained in simple plain English and no complex formulas. It’s like having an interesting conversation with a data-savvy friend or colleague.

So whether you are a data scientist, business intelligence analyst or just curious about the businesses that drive themselves using the data-centric approach.


  • Relevance (5/5): Focused on the business aspect of data science.

  • Clarity (4/5): Provides a clear understanding of the fundamental concepts.

  • Real-world Examples (5/5): Features case studies and practical scenarios.

  • Beginner-friendly (4/5): Great for those new to the field.

Intermediate Level

At the intermediate level, You need to cover most of the Statistics and Machine Learning.

4. “Essential Math for Data Science” by Thomas Nield

Ratings: 5/5 (★★★★★)

Math is to data scientists as Kryptonite is to Superman.

But worry not, you already have the solution from Thomas Nield in the form of “Essential Math for Data Science”. This book is like a math tutor for data scientists which breaks those headache-inducing concepts into a bite-sized, digestible food that you can chew easily.

Nield makes your math skills proficient by diving into Linear Algebra, calculus, probability and statistics with plain English, and practical real-world examples that make the math come alive.

So conquer the power of data by learning the wilderness of Essential Math Skills for Data Science.


  • Clarity (5/5): Thomas Nield’s “Essential Math for Data Science” is a beacon of clarity, making complex mathematical concepts crystal clear.

  • Practicality (5/5): It focuses on the math that’s directly applicable to data science, ensuring you learn what truly matters.

  • Engaging (5/5): Nield’s engaging writing style turns math into an enjoyable journey, not a daunting task.

  • Hands-On (5/5): Packed with practical examples and exercises, it ensures you can apply what you’ve learned.

5. “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron

Ratings: 5/5 (★★★★★)

Consider it a friendly mentor, who walks you through the garden of Machine Learning from start to end. You will start with foundations like Linear regression and decision trees, and then you will venture into deep learning with TensorFlow and Keras.

The good thing is, it is not all theory as there are hands-on coding examples to get your hands dirty. This book is clear, concise and loaded with real-world insights as if someone is guiding you like he has been there, done that and more.

Therefore, If you want to dive into Machine Learning first time or the 10th time to test your capabilities, this book will certainly be helpful to you every time as it puts your knowledge to work bit by bit.

  • Hands-on (5/5): Provides practical exercises and coding examples.

  • Up-to-date (5/5): Covers the latest machine learning libraries.

  • Conceptual Clarity (5/5): Explains complex topics in an approachable way.

  • Comprehensive (4/5): Suitable for those looking to deepen their machine-learning skills.

6. “Machine Learning Yearning” by Andrew NG

Ratings: 5/5 (★★★★★)

Andrew NG is the Mastermind behind Coursera’s machine learning course. So, you can have an idea, this book is going to be your personal trainer for Machine Learning.

Andrew’s book is a treasure trove for machine learning enthusiasts who want practical advice and insights from one of the brightest minds in the field. Ng dives deep into building machine learning algorithms that are related to real-world applications by whispering the secrets of Machine learning in the ear.

You will learn from how to set goals for ML projects to debugging and fine-tuning, just like a GPS that guides you through a Journey to make sure you are on the right path.

To take your machine learning skills to the next level, Machine Learning Yearning is a must-have in your knowledge.


  • Expert Insights (5/5): Written by one of the pioneers of machine learning.

  • Practical Guidance (5/5): Offers practical advice for building and deploying machine learning systems.

  • Project Focus (5/5): Emphasizes project management and decision-making in machine learning.

  • Up-to-date (5/5): Covers the latest real-world applications of machine learning.

Advance level

Books at this level need to be Bibles of Data Science so let’s get started:

7. “Deep Learning” by Yoshua Bengio, Ian Goodfellow, and Aaron Courville

Ratings: 5/5 (★★★★★)

This book is authored by Titans of Deep Learning — Bengio, Goodfellow and Courville. They take you onto the mysteries of Deep Neural Networks by teaching you the basics of neural networks to the most cutting-edge techniques of deep learning.

They cover it all: convolution, recurrent, feedforward deep networks and more. But the amazing thing about this book is, that all of the concepts are explained in a very warm and effective manner that makes everything clear as a crystal ball.

Without any doubt “Deep Learning” is your neural enlightenment to unravel the power of neural networks and deep learning techniques.


  • Expert-level Content (5/5): People looking to master deep learning.

  • Comprehensive (5/5): Covers the entire spectrum of deep learning.

  • Theory and Practice (5/5): Balances mathematical hardness with practical implementation.

  • Challenging (5/5): Ideal for already experienced data scientists seeking cutting-edge knowledge.

8. “Pattern Recognition and Machine Learning” by Christopher M. Bishop

Ratings: 5/5 (★★★★★)

Beware, This is not for the faint-hearted.

This is the book that can make you a data detective as it makes you a pioneer in pattern recognition and advanced machine learning.

Bishop takes you into the multiverse of algorithms and models that make machines smart.

It is the wilderness of being a human that make us smarter than machines.

You will explore topics like Bayesian networks, Support vector machines and neural networks. Still, it feels like chatting with a brilliant friend who is making it all understandable.

So, this book could be a secret weapon of yours in your journey of data science which will help you understand what works behind the scenes to uncover the patterns of data and predictions.


  • Advanced Topics (5/5): Delves into advanced machine learning and pattern recognition.

  • Mathematical Rigor (5/5): Requires a strong mathematical background.

  • In-depth (5/5): A reference for those wanting to understand the intricacies of ML and pattern recognition.

  • Challenging Exercises (5/5): Not for the faint-hearted, but immensely rewarding.

These books are considered most profound at their levels for all aspiring and pro data scientists. The books mentioned above not only the theoretical aspects of data science, machine learning, neural networks and deep learning but also the practical aspects.

Moreover, if you need a complete course outline for your data science journey, it will also be published soon.

More is coming for you so follow Data Scian and clap if you have made it this far.

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