If you are a person who loves the smell of a textbook and you are looking for the best book for data science with Python – look no further.
We have created a list of the five best books out there.
What Is Data Science and Python?
Data science is an interdisciplinary field that uses scientific methods to extract knowledge from data but also applies this knowledge across a broad range of fields. Data science is closely related to data mining, machine learning, big data, and computational statistics.
Python is a general-purpose programming language used by data scientists as well as software developers. It is one of the most popular programming languages. This is just one of the reasons to get a hold of one of these books and learn it.
What’s great about Python is that it can automate all the tasks that data engineers need to do. Additionally, Python has Python analytics and visual data libraries, which are important and useful for data scientists.
Because Python is so handy for data scientists, it is one of the most used programming languages in data science, especially suitable for beginners.
For all these reasons, the best books for data science deal with Python as well. While there are many great videos and courses online on this topic, a book will give you a more in-depth approach.
Also, most of these books are lengthy and would take hours and hours of video or audio material to cover such a broad topic. Some of these books cover so much that they could be used for more than one college major.
Another great thing about these five best books for data science is that if you are a professional who deals with these topics daily, it’s great to have the best books on data science on hand. It will help you navigate straight to the topic of interest.
These comprehensive books aim to teach the fundamental concepts of Python. All while using practical examples that are easy to understand.
Now it’s time to see our selection of the five best data science books for Python:
Have you ever come across the series For Dummies?
Then you know why this book is at the top of our list. The For Dummies series is a non-intimidating approach for beginners and readers who are new to the topic.
This particular book about Python for data science is a must-have for anyone dabbling in the world of data science. The book is full of graphics, which makes it both easier to absorb and easier to remember.
If you are a visual learner, which means you need pictures, charts, pies, and graphs to remember things, this book is perfect for you.
Python for Data Science For Dummies is a book for readers who are new to data analysis. The book discusses the basics of Python data analysis programming and statistics.
This book also covers Google Colab, which developers use to write Python code in the cloud. This book includes also includes the statistical background needed for data science such as:
- Random distributions
- Hypothesis testing
- Confidence intervals
- Building regression models for prediction
Python for Data Science for Dummies is divided into six parts, and each part immerses the reader more in the world of data science.
The chapters include:
- Visualising Information
- Wrangling Data
- Learning from Data
- The Part of Tens which covers ten essential data resources and ten data challenges you should take
The book also has icons that help locate material of interest, such as icons for tips, warnings, or important information to remember. The book itself is organized like a notebook – with important passages in bold letters, tables, colors, examples, and bullet lists.
As the authors say, the goal of this book is to take the scary factor away from data science and show that data science is not only doable but can also be fun.
The authors want you to know that you don’t need to be a computer science genius to master data science tasks. That’s where this book comes to help.
If you are a data scientist or are familiar with data science, this book can help you tackle day-to-day issues. It’s a book that you can keep on your desk and refer to anytime you need help with things like:
- Manipulating, transforming, and cleaning data
- Visualizing different types of data
- Using data to build statistical or machine-learning models
Even though this book covers a variety of tools, it can get dry for someone who is just starting.
Some readers refer to it as a dictionary-style staple. Consider it as a manual you reach for when you have a concrete task rather than a student textbook that you read from cover to cover.
Tools that this handbook includes are divided into five chapters:
- IPython and Jupyter: Provides an environment for data scientists using Python
- NumPy: Deals with efficient storage and manipulation of dense data arrays in Python
- Pandas: Features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
- Matplotlib: Includes capabilities for a flexible range of data visualizations in Python
- Scikit-Learn: For efficient and clean Python implementations of the most important and established machine learning algorithms
Another thing that you want to consider is that this book comes in paperback edition only. So if you want something to have on your Kindle on the go, this might not be for you.
This book is for someone who has had some form of introduction to programming, algebra, statistics, and probability but who wants to learn about data science.
As much as the author deals with this topic and does a great job of giving a crash course, this book is not for someone who doesn’t have a history in some sort of STEM subject.
If this is your first contact with these subjects you will find this book quite overwhelming, so don’t let the name fool you. However, if you have already had a taste of data science, you will find this to be an easy read.
Are you starting in machine learning as well?
Then, this might be a good fit for you as it covers this topic as well, but more simply than the Handbook, which is suitable for more experienced readers.
What you might find off-putting is that this book is monochrome, so if you are a visual learner you might want to consider a Kindle version that is available. The Kindle version is in color, which might be helpful when it comes to coding.
The topic is divided into bite-size chapters, the book consists of 27 chapters. Each chapter is fewer than 15 pages, which makes it perfect for someone who wants to go through it bit by bit.
This book consists of:
- A crash course in Python
- The basics of linear algebra, statistics, and probability
- How to collect, explore, clean, munge, and manipulate data
- Basics of machine learning
- Models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Natural language processing, network analysis, MapReduce, and databases
4. Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data, and The Cloud
If you are a student, you will love this book!
It’s an introductory-level Python programming and data science course. It is presented in a precise manner and also very student-oriented in a way that helps you learn progressively and quickly.
If you like to be engaged while reading, this might be the book for you. It has a nice feel to it, the print is high quality, there is plenty of space on the margins where you can take notes, and it’s the most colorful data science book on our list.
The book provides the most current coverage of topics and applications and it is paired with extensive traditional supplements. This book is suitable for students who want to use modern data science to work on different projects in areas like business, industry, government, and academia.
This book contains hundreds of examples, exercises, and case studies for an engaging and entertaining introduction to programming with hands-on experience with data science. It is a good balance of theory and fundamentals while still putting it into practice.
Intro to Python for Computer Science is a data science book that can be helpful for different majors, and the fact that the book is modular makes it easy to navigate for all of them.
What might not work for you if you are not a student who has access to a teacher or an instructor but rather professional learning on your own, is that the exercises that come after the chapter examples do not have a solution included.
Keep in mind that you will be left with a substantial number of exercises without an option to check if what you have done is correct or not.
While this book is attractive and engaging for students and beginners, it might not be for someone who is more advanced and is looking for something extensive in data science and Python.
5. Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data
This book is intended for beginners, students, recent college graduates, and professionals who want to learn hands-on data science techniques in Python. And it’s also for those who want to go into data science from a different career path.
It requires very little prior knowledge, yet extensively covers the basics of the subject as well as practical application. However, an experienced reader can use the book by directly diving into the area of interest, whereas a beginner would want to read all the early chapters thoroughly.
A great thing in this book is the Test Your Knowledge Sections, which are short but challenging tests to help you test where you stand with the chapter content.
This book is the newest of the books listed, published at the end of last year. The book also covers machine learning and natural language processing (NLP) which has gained popularity in recent years.
What you can learn from this book:
- How to use Python data science packages
- How to prepare data for data science work
- Data modeling and essential machine learning algorithms
- How to evaluate model performance
- How to compare different machine-learning methods
- How to create automated data science reports through Python
Almost all of the best books for data science that we have listed above come in the Kindle version as well. This makes them perfect on the go but also for quick searches or when you travel.
Best Book for Data Science with Python – FAQ
Which books are the most beginner friendly?
The most beginner-friendly book on the list is Python for Data Science For Dummies.
Which book is best for college and grad students?
The best book for higher education students is Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data, and The Cloud.
Which book is the most up-to-date?
The most recently published book is Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data.
Which books would be best for someone who wants to familiarize themselves with Machine Learning?
Even though online courses and classes help engage more actively while learning, books have a special role.
They are the most systematic and complete guide for anyone who wants to dive deep into the world of data science. Also, they are the most authoritative sources and necessary for those at a higher level of education.
Taking your skills to a new level is most efficient when different methods are combined. And utilizing the best books for data science is one of them.
Our top choice for a data science book for Python would be Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data, and The Cloud. It’s beginner-friendly, yet extensive, very appropriate for the students, and visually pleasing.
If you want something more basic, try Data Science from Scratch: First Principles with Python which is a great option for someone who is somewhat familiar with data science.
However, you can’t go wrong with any of these books. They are all treasure boxes filled with valuable tools for students, teachers, professionals, and enthusiasts.
Claudio Sabato is an IT expert with over 15 years of professional experience in Python programming, Linux Systems Administration, Bash programming, and IT Systems Design.
With a Master’s degree in Computer Science, he has a strong foundation in Software Engineering and a passion for Robotics with projects that include Raspberry Pi and Arduino platforms.