Top 10 Best Data Science With Python Books [2025]
Data science is inviting industry, and many students and professionals are looking forward to learning programming languages to get into it. Python is one such language that is highly demanding in data science. It takes a lot of analysis, statistical knowledge, and a solid foundation of computer programming basics. Writing in Python in combination with data science takes a lot of finesse. Hence, here are the top data science with Python books to read –
- Data Science Using Python And R ( Get this book )
- Data Science From Scratch: First Principles With Python ( Get this book )
- Data Science Projects With Python: A Case Study Approach To Gaining Valuable Insights From Real Data With Machine Learning ( Get this book )
- Data Science And Machine Learning: Mathematical And Statistical Methods ( Get this book )
- Python For Data Science: The Ultimate Beginners’ Guide To Learning Python Data Science Step By Step ( Get this book )
- Data Science With Python And Dask ( Get this book )
- Practical Statistics For Data Scientists: 50+ Essential Concepts Using R And Python ( Get this book )
- Python Data Science Handbook: Essential Tools For Working With Data ( Get this book )
- Intro To Python For Computer Science And Data Science: Learning To Program With AI, Big Data, and The Cloud ( Get this book )
- Data Science For Beginners: 4 Books In 1: Python Programming, Data Analysis, Machine Learning. A Complete Overview To Master The Art Of Data Science From Scratch Using Python For Business ( Get this book )
Let us explore each book with some of its main learning and lessons –
#1 – Data Science Using Python And R
By Chantal D. Larose and Daniel T. Larose
The book came out in 2019 and is a detailed guide on using data science techniques and programming languages Python, R, and data mining.
Book Review
Data science is a booming subject in today’s times, and this book covers its entirety, from basics to advanced level knowledge. The book will surely be a one-stop solution to readers’ queries and doubts. It has a total number of 500 exercises for readers to indulge in. In addition, the book provides hands-on information on various analyses and data science calculations.
Key Takeaways
- The book covers data preparation, decision trees, model evaluation, exploratory data analysis, preparing to model the data, etc.
- It is written keeping in mind the perspective of a general reader.
- It has an exercise at the end of every chapter.
#2 – Data Science From Scratch
First Principles With Python
By Joel Grus
This book was published in 2019 and consisted of data science libraries, modules, toolkits, and frameworks.
Book Review
The book starts with the basics and lets the reader absorb every bit of information before moving to advanced levels of learning. Next, the author provides core data science tools and hacking skills one needs to know to become a data scientist. Finally, he helps dig out answers and get hold of all the data an individual comes across in data mining.
Key Takeaways
- The book talks about the basic fundamental data science tools and algorithms.
- It explores machine learning, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering.
- Readers with an aptitude for mathematics and programming skills, the author will push them forward to excel in it.
#3 – Data Science Projects With Python
A Case Study Approach To Gaining Valuable Insights From Real Data With Machine Learning
By Stephen Klosterman
The book came out in 2021 and is written in a case study format to cover all the arguments and opinions. The author shares some valuable insights on machine learning as well.
Book Review
The book is a project in itself with predictive models and data sets. It progresses with practical exercises and includes many processes and calculations concerning data science projects, machine learning, data exploration, etc. Using a case study approach; the author shares many machine learning models, algorithmic fairness, XGBoost, and SHAP interaction values.
Key Takeaways
- The book teaches machine learning skills and offers real data insights.
- It motivates the reader to think critically and use the data to test a hypothesis.
- The author shares knowledge of python packages, Matpotlib, and scikit-learn.
#4 – Data Science And Machine Learning
Mathematical And Statistical Methods
By Dirk P. Kroese, Zdravko Botev, Thomas Taimre, and Radislav Vaisman
The book was released in 2019 and compiled analytical insights from the four authors on machine learning and data science.
Book Review
The book is a product of all the hard work and experience gained by the authors over time. It is a detailed account of machine learning techniques, which is good for graduate-level and undergraduate-level students. The authors present major theorem proofs and derivations. They have kept the writing very clear, explaining the statistics to denote the variations in machine learning algorithms.
Key Takeaways
- The book presents mathematical and statistical methods in a comprehensive and accessible manner to all sorts of readers, from amateurs to professionals.
- It holds many detailed examples and long-form exercises on data science and machine learning.
- The authors significantly focus on mathematical understanding of the concept.
#5 – Python For Data Science
The Ultimate Beginners’ Guide To Learning Python Data Science Step By Step
By Ethan Williams
The book was published in 2019 and is treated as a beginner’s guide to learning python programming and the basics of data science.
Book Review
The book is good for anyone learning the Python programming language, irrespective of the level they operate. The author states that to master the language, one must keep practicing and access relevant study materials. He also believes that the only limitation is aspiration; other possibilities can be explored with consistency.
Key Takeaways
- The book introduces several Python libraries, including Pandas, NumPy, Pandas, Seaborn, and Matplotlib.
- It encourages readers to practice the basic data science techniques and Python language.
- The author believes that data science and python programming opportunities are endless.
#6 – Data Science With Python And Dask
By Jesse Daniel
The book was published in 2019 and dictated how to build projects to streamline large datasets.
Book Review
The book makes readers use their abilities to explore massive data sets and work with them. If a reader wants to create machine learning models with the help of Dask-ML, this book is the perfect pick. In addition, the author talks about the use of AWS and Docker to build interactive visualizations and clusters.
Key Takeaways
- The book can be understood as an analytical tool.
- It encourages readers to use the Dask framework and data frames to streamline the process.
- The author lets readers implement their algorithms.
- The book explains the deploying and packaging process of Dask apps.
#7 – Practical Statistics For Data Scientists
50+ Essential Concepts Using R And Python
By Peter Bruce, Andrew Bruce, and Peter Gedeck
The book came out in 2017 and is prominently focused on statistics and mathematical training for data science analysis.
Book Review
The book is quite popular for featuring Python examples with a perspective of applying several methods and techniques involving practical guidance. As a result, the readers can learn how to avoid data misuse and only consider relevant and important information in data science analysis. Those acquainted with Python and R can easily relate, and the book can bridge the gaps between doubts and queries.
Key Takeaways
- The book explains the importance of data analysis.
- It dictates the classification and categorization of data sets.
- The authors stress the statistical significance and practical guidance in data science analysis.
#8 – Python Data Science Handbook
Essential Tools For Working With Data
By Jake Vanderplas
The book was released in 2016 and is a handbook for people who constantly need to acquire knowledge and references regarding data science techniques and strategies.
Book Review
The author wrote the book as a comprehensive guide so that readers willing to learn Python can have access to it anytime with straight on-point examples and techniques. People interested in the Python language come across several difficulties and errors while programming, this book help eliminate such issues.
Key Takeaways
- The book explains different types of data and machine learning models.
- It is often considered a must-have for scientific computing in Python.
- It comes in handy for scientists acquainted with reading and writing Python code.
#9 – Intro To Python For Computer Science And Data Science
Learning To Program With AI, Big Data, And The Cloud
By Paul Deitel and Harvey Deitel
The book was published in 2019. It provides a flexible approach to learning Python programming and context to machine learning, cloud space, and AI.
Book Review
The book holds real-world data sets and AI technologies. This one aspect helps students have an in-depth understanding of business projects belonging to different industries. In addition, the book covers new topics and applications associated with data science. Finally, it contains a collection of over one hundred exercises, examples, and projects.
Key Takeaways
- The book allows instructors to adapt the text to many different majors.
- It goes hand in hand with the ACM/IEEE CS and other computer curriculum initiatives.
- The book also accounts for many case studies allowing students to engage in Python programming education.
#10 – Data Science For Beginners
4 Books In 1: Python Programming, Data Analysis, Machine Learning. A Complete Overview To Master The Art Of Data Science From Scratch Using Python For Business
By Andrew Park
The book released in 2020 is a collection of four books in one. The key topics include Python programming, machine learning, and data analysis.
Book Review
Data science is becoming more exciting and inviting to people from different fields; data scientists require a lot of knowledge and therefore are expensive to hire and difficult to retain. This book is the bundle for programmers, project managers, and software engineers who always want to walk in parallel with the technology. In addition, Python programming is becoming a must-have language skill, and this book can help readers start their journey from scratch.
Key Takeaways
- The book helps readers learn Python from the very beginning.
- It is very insightful with the perspective of improving business performance.
- The book helps identify new skills and ultimately increases an individual’s income.
- The author gives out five steps of data analysis.
