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8 Best Python Data Science Books [Beginners and Professionals]

HomeBlogData Science8 Best Python Data Science Books [Beginners and Professionals]

8 Best Python Data Science Books [Beginners and Professionals]

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Satish T

Published

15th Sep, 2023

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8 Best Python Data Science Books [Beginners and Professionals]

Python could be a high-level, useful programming language that allows faster work. It supports a range of programming paradigms, as well as procedural, object-oriented, and practical programming, also as structured programming. Thanks to its intensive customary library, it's often remarked as a "batteries included" language.

Python was designed by Dutch computer programmer Guido van Rossum in the late 1980s. It was placed into effect in December 1989.

Many aspiring data scientists are working hard to earn a Certificate in Data Science with Python since Python is widely used in artificial intelligence for robots and voice assistants like Alexa, Siri, and Google Assistant, among others. For those interested in studying this programming language, several best books for python data science are accessible. Let's have a look at some of the top ones.

Top 8 Python Data Science Books for 2023

Python is one of the programming languages that is most commonly utilized in the field of data science. There are many books on Python for data science accessible; in this article, we'll look at the top 8 of such Python books for data science as rated by Goodreads users.

1. Automate the Boring Stuff with Python (Practical Programming for Total Beginners)

data science books automate the boring stuff with python

Al Sweigart's book "Automate the boring stuff with Python," was released for the first time on November 25, 2014. More than 500,000 copies were sold, and 2,462 readers gave this book a rating of 4.3 out of 5 on the Goodreads website. In comparison to other books on Python for Data Science, this one gets the highest rating which is the best book for data science with python. This book's publisher is "No Starch Press," and the second edition was released on November 12, 2019.

“Automate the boring stuff with Python” is the best python data science book for beginners with no prior programming expertise on how to write programs that run quickly and efficiently while simultaneously performing tedious, repetitive tasks. Everyone has experienced the problem of swiftly doing tedious tasks on their computer, such as updating repetitive data in cells of Excel sheets. This book offers practical programming solutions to these problems.

After learning the fundamentals of Python programming from this book, you'll be able to write Python programs that perform useful and spectacular feats of automation with ease. The following are some of the most important advantages of this book:

It explains how to use the Python interactive shell to experiment with coding, as well as expressions, the most fundamental sort of Python command.
Describes how your applications may read text files and store data in folders on your computer's hard drive.
Demonstrates how Python is significantly quicker than a human user in copying, moving, renaming, and deleting huge amounts of files. Additionally, it teaches how to compress and decompress files.
Demonstrates how to create software programs that can automatically download web pages and extract their contents. It's known as web scraping.
It explains how to manipulate Excel spreadsheets programmatically, so you don't have to read them. This is beneficial when you have to evaluate hundreds or thousands of pages.

2. Python Data Science Handbook: Tools and Techniques for Developers

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The "Python Data Science Handbook Essential Tools for Working With Data" is the best book to learn python for data science, written by Jake Vander Plas and released by O'Reilly Media, Inc. November 2016 saw the publication of the first copy. On the Goodreads website, readers awarded this book a rating of 4.3 out of 5 (524 ratings).

Python's modules for storing, manipulating, and deriving insights from data make it a top tool for beginners in programming and researchers. You can master several crucial Python data science technologies from the Python data science handbook, including Pandas, Matplotlib, NumPy, Scikit-Learn, Machine Learning, IPython, etc.

Learning the essential Python tools that were previously discussed is one of this book's main advantages. Let’s study them further below:

Machine learning: Tools for machine learning are algorithmic uses of artificial intelligence that enable systems to learn and advance without a lot of human input.
Matplotlib: Contains Python skills for a wide range of data visualizations.
Pandas: Includes the DataFrame for effective labeled/columnar data processing in Python.
NumPy: Contains the Ndarray in Python for fast storing and handling of dense data arrays
Scikit-Learn: For Python implementations that are efficient and clean of the most significant and well-established machine learning algorithms.
IPython: Provides Python-based computational platforms for data scientists.

3. Python Crash Course: A Hands-On, Project-Based Introduction to Programming

Eric Matthes wrote "Python Crash Course: A Hands-On, Project-Based Introduction to Programming," published by No Starch Press. The first edition was launched on February 25, 2015, and the second edition was issued on May 3, 2019. Readers gave this book a rating of 4.36 out of 5 on the GoodReads website. According to the publisher, the third version will be launched in December 2022, and pre-orders are already available.

Python Crash Course is a solid introduction to Python programming that moves quickly. It will have you writing programs, troubleshooting problems, and constructing usable objects in no time. This is the best-selling programming language data science python handbook in the world. The following are some of the key benefits of this book:

Data may be used to create interactive infographics.
Describes how to Handle faults and errors so that you may fix your own programming issues.
Explains how to build, tweak, and reliably deploy web apps online.
Shows how to create 2D games that respond to key inputs and mouse actions and rise in difficulty.

4. Python for Data Analysis - Data Wrangling with Pandas, NumPy, and IPython

data science books python for data analysis

The book "Python for Data Analysis - Data Wrangling with Pandas, NumPy, and IPython'' by Wes McKinney was published by O'Reilly Media, Inc. The first version was launched on 30 December 2011, and the second edition was published in October 2017. This book is rated 4.16 out of 5 stars on the GoodReads website.

In this book, you will learn how to apply the most basic data science tools and algorithms from scratch. It will guide you to build analytical skills and programming knowledge to expertise in Data Science Coding Bootcamp. If you have a natural aptitude for mathematics and some programming abilities, this book will guide you through the arithmetic and statistics at the heart of data science, as well as the hacking skills required to start as a data scientist. Here are some of the major benefits this book will provide for readers:

The "Two-Language" Problem is addressed in this book.
Describes how to install and configure operating systems such as Windows, Apple's macOS, and Linux.
Identify and work with both typical and erratic time series data.
With complete, detailed examples, readers can learn how to address real-world data analysis challenges.
Learn about the fundamental and advanced aspects of Numeral Python (NumPy).
Learning how to use IDEs and text editors, as well as installing and upgrading Python packages like Python 2 and 3.

5. Think Python - How To Think Like a Computer Scientist

The book "Think Python - How to Think Like a Computer Scientist" is the best python for data science book by Allen B. Downey and was published by O'Reilly Media, Inc. The first version was launched in August 2012, and the second edition was updated in December 2015 for Python 3. 1482 readers rated this book 4.36 out of 5 stars on the GoodReads website.

Allen B. Downey developed this book in response to his dissatisfaction at watching so many students struggle with this topic. There are numerous large books with a lot of superfluous java information but very little practical programming help. So the author covered all those gaps in this book with useful information on python. All supporting code in the second edition of "Think Python" has been updated to Python 3. This practical introduction introduces you to the language's fundamental ideas before advancing to functions, loops, data formats, and object-oriented programming. Let’s look at some key benefits of this book below:

This book gives you basic knowledge on the way of the program, running Python, and arithmetic operators.
Debugging of syntactic, runtime, and semantic errors.
Analysis of basic Python operations and search algorithms.
Learning to do numerous operations such as math functions, composition, the flow of execution, and stack diagrams, among others.

6. Learning Python

The book "Learning Python" was written by Mark Lutz and David Ascher, and it was published by O'Reilly Media, Inc. The first version was launched in April 1999, and the second edition was released in December 2003. This book is rated 3.99 out of 5 stars by 2995 readers on the GoodReads website.

Programmers are given all the knowledge they need in the first part of this book, including information on operators, types, classes, statements, functions, exceptions, and modules. For aspiring data scientists or programmers, the learning of python book also includes additional context, such as updated summaries of object-oriented programming and syntax highlighting, talks of program starting and configuration choices, new discussion of documentation sources, and more.

Both writers have covered more advanced material, showing how real programs and the libraries that go along with them may be utilized to carry out common tasks in Python. Let’s look at such key benefits covered in this book:

Each chapter has a collection of activities that will put your Python knowledge to the test and measure your comprehension.
This book focuses on the detailed core language.
Learn how to use Python for database development, component integration, GUIs, and systems programming.
Programming for games, images, XML, and artificial intelligence using Python, etc.
Understanding of Python's technological strengths.

7. Introduction to Machine Learning with Python: A Guide for Data Scientists

data science books introduction to machine learning with python

O'Reilly Media, Inc. published the book "Introduction to Machine Learning with Python: A Guide for Data Scientists," written by Andreas C. Müller and Sarah Guido. This book was released in October 2016. This book is rated 4.35 out of 5 stars by 456 readers on the GoodReads website.

For Ph.D. and graduate students studying machine learning, there are several books accessible, but not for people wishing to learn this independently. These days, no degree is necessary to use Python for machine learning. Therefore, the authors of this book wrote it to aid those without a Ph.D. or any other undergraduate degree who wish to apply machine learning. This book contains 8 chapters. The following are the themes discussed in the book that will be useful to readers:

Explains how to chain models and encapsulate your process using pipelines.
Explains the significance of how machine-learned data is represented and what parts of the data should be taken into consideration.
Contains references to more sophisticated subjects and provides a high-level summary.
For data scientists, researchers, and scientists working on commercial applications, the techniques provided in this book are particularly helpful.
It discusses the most popular machine learning algorithms in use today and examines their benefits and drawbacks.

8. Data Science from Scratch: First Principles with Python

data science books, data science from scratch

O'Reilly Media, Inc. published the book "Data Science from Scratch - First Principles with Python" written by Joel Grus. This book was released on 14 April 2015. This book is rated 3.91 out of 5 stars by 955 readers on the GoodReads website.

If you have an aptitude for mathematics and some programming abilities, this book will guide you through the arithmetic and statistics at the heart of data science, as well as the hacking skills required to start as a data scientist. The current tangled torrent of data has left many questions unanswered. For all those complex questions, this book offers solutions. Here are some of these main advantages:

Explains how to investigate recommendation systems, NLP, MapReduce, Network analysis, and databases.
Explains the basics of machine learning.
Teaches Python crash course.
Data collection, exploration, cleaning, munging, and manipulation

Preparation Tips for Python for Data Science

Preparation for Python needs continuous practice and choosing the best book for python data science for learning Python. Let's look at some Python preparation suggestions below.

Begin your Python preparation with an understanding of the fundamentals or foundations of the language, such as data types, strings, properties, functions, classes, and file inputs/outputs.
Start putting your theoretical knowledge into practice right away by working on small Python projects. The greatest way to become an accomplished Python programmer is through hands-on training.
Study the Scikit-Learn, Pandas, Matplotlib, and NumPy libraries, which are the key four Python data science libraries.
Make a data science portfolio while you are learning Python. Consider the Machine Learning, Data Cleaning, and Data Visualization projects.
Learn advanced Python data science techniques and put them to use in real-world micro projects.

More Ways to Learn Python for Data Science

There are various modes for learning Python. Let's study below some of the useful ways to learn Python for data science.

Choose the best books on python for data science for learning Python, then devotedly read and practice.
Read various blog posts, articles, and Python tutorials.
Enroll in online courses to get a Python data science certification. The finest training resource is KnowledgeHut, where you may earn a Data Science Certificate.
Utilize internet search engines or reference books to find solutions to the programming-related issues you encounter.
Students can choose a data science course for their undergraduate or graduate studies, where they get a python data science textbook for studying.

Conclusion

The best data science Python books, as we have seen above, are the most popular and well-reviewed by readers online. Those who want to get trained well and become experts in Python programming should read the above-listed books for python for data science in order to get certified as the best data scientist. By having a KnowledgeHut’s Certificate in Data Science, you may develop the skills needed to design Data Science and Machine Learning models and draw the attention of organizations.

Frequently Asked Questions (FAQs)

1. Which is the best book to learn data science Using Python?

The best book for learning python for data science to recommend for studying Python-based data science is "Learning Python." Since its first publication in 1999, five versions have been made available, most recently in 2013.

2. How do I start learning Python for data science?

Python is a simple language to learn for data science. Learn Python's fundamentals first; for this, pick the best book to learn data science with python that focuses solely on the foundations.

3. Is Python or R better for data science?

Both Python and R are superior for studying data science, but the option must be made by the learner based on the type of data science desired. R is a useful tool for statistical computation and analysis, even if Python is easier to use and understand.

Satish T

Author

Satish T writes on project management and the many approaches used in projects across different sectors. He honed his fundamental writing talents in article production after discovering that the creation of content is essential when describing any product. Satish's areas of interest are fact-finding research, Search Engine Optimization, and skill development.

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