While there are many online courses to learn Python for Machine learning and Data science, books are still the best way for in-depth learning and significantly improving your knowledge. Python is a universal language that is used by both data engineers and data scientists and probably the most popular programming language, as well. All the Data Scientists I have spoken to, and many in my friend circle just love Python, mainly because it can automate all the tedious operational work that data engineers need to do. To make the deal even sweeter, Python also has algorithms, analytics, and data visualization libraries like Matplotlib, which is an essential data scientist.
In both roles, the need to manage, automate, and analyze data is made easier by only a few lines of code. It's such an essential part of a Data Scientist's day-to-day job that almost all the people I have spoken to recommended the "Automate The Boring Stuff With Python" book.
In the past, I have shared some of the best books to learn Python, but they were for general programming. I haven't shared a single book that teaches Python from the Data Scientist's point of view, which is what I'll do in this article.
Even though a couple of books on my previous list of Python books are still good to learn Python for Data Scientist because fundamental remains same and that's why they also made to this list as well, but books like Python For Data Analysis are indeed a gem and must-read for any Data Scientist who uses Python.
Some of you might be thinking a list of 5 books is too small, and many great Python books have not been included in this list, but I do this purposefully. I like to share a short but practical list because sometimes too many suggestions can confuse people.
Having said that, if you know a Python book which a Data scientist should read, then feel free to share it with us in the comments. Many experienced developers and Data scientists like to learn from many sources, and those suggestions can be beneficial.
In both roles, the need to manage, automate, and analyze data is made easier by only a few lines of code. It's such an essential part of a Data Scientist's day-to-day job that almost all the people I have spoken to recommended the "Automate The Boring Stuff With Python" book.
In the past, I have shared some of the best books to learn Python, but they were for general programming. I haven't shared a single book that teaches Python from the Data Scientist's point of view, which is what I'll do in this article.
Even though a couple of books on my previous list of Python books are still good to learn Python for Data Scientist because fundamental remains same and that's why they also made to this list as well, but books like Python For Data Analysis are indeed a gem and must-read for any Data Scientist who uses Python.
Some of you might be thinking a list of 5 books is too small, and many great Python books have not been included in this list, but I do this purposefully. I like to share a short but practical list because sometimes too many suggestions can confuse people.
Having said that, if you know a Python book which a Data scientist should read, then feel free to share it with us in the comments. Many experienced developers and Data scientists like to learn from many sources, and those suggestions can be beneficial.
7 Python Books Every Data Scientist Should Read in 2024
Without wasting any more of your time, here is my list of Python books, which I believe every Data Scientist should read. The list also highlights the critical reason why Data scientists should learn Python? Not just libraries but the automation of tedious tasks and Data operation Python provides is immensely helpful for any Data Scientist dealing with real-world data.1. Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter 3rd Edition
This is the first specialized Python book on Data Analysis and Data Science you should read in 2024. This Python book will cover all the basics a Data Scientist or Data engineer should know, like data aggregations and time series.In this updated third edition, tailored for Python 3.10 and pandas 1.4, McKinney, the creator of the Python pandas project, offers a hands-on approach enriched with practical case studies. This book serves as an invaluable resource for both Python analysts new to the language and Python programmers venturing into data science and scientific computing.
You will explore the latest versions of essential tools such as pandas, NumPy, and Jupyter, gaining proficiency in the Jupyter notebook and IPython shell for exploratory computing. The book covers basic and advanced features of NumPy, introduces data analysis tools in the pandas library, and guides users through loading, cleaning, transforming, merging, and reshaping data.
With a focus on practicality, McKinney demonstrates how to create insightful visualizations with matplotlib, utilize the pandas groupby facility for dataset summarization, and analyze regular and irregular time series data.
Thorough, detailed examples throughout the book provide a practical understanding of solving real-world data analysis problems. Data files and supplementary material are conveniently available on GitHub, enhancing the overall learning experience.
This is probably the best book for manipulating, processing, cleaning, and crunching data in Python and learning Pandas for real work. You can also combine this book with an online course like Learning Python for Data Analysis and Visualization on Udemy, which will not only give you tons of code to analyze, visualize and present data but also show you how to do it properly.
This is probably the best book for manipulating, processing, cleaning, and crunching data in Python and learning Pandas for real work. You can also combine this book with an online course like Learning Python for Data Analysis and Visualization on Udemy, which will not only give you tons of code to analyze, visualize and present data but also show you how to do it properly.
2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd Edition
The "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 3rd Edition" by Aurélien Géron is a highly acclaimed and bestselling guide that demystifies the complexities of deep learning for programmers of all levels. Leveraging recent breakthroughs in deep learning, this book empowers even those with limited knowledge of the technology to implement intelligent systems using simple and efficient tools. The updated third edition explores a variety of techniques, from basic concepts like simple linear regression to advanced topics such as deep neural networks.
Using concrete examples, minimal theory, and Python frameworks like Scikit-Learn, Keras, and TensorFlow, Géron provides an intuitive understanding of machine learning concepts. The book is filled with numerous code examples and exercises, allowing readers to apply their knowledge and build practical skills.
If you would prefer learning about Tensorflow, then this is one of the best Python books currently available in the market. Even though it is slightly lighter on machine learning but the second half of the book is fully dedicated to neural networks. It also covers topics like convolutional neural networks, autoencoders, drop-out, and other vital issues to deep learning with Tensorflow.
Another library, which I recommend is called Pandas. It's an extremely powerful tool and if you are dealing with data, you will need that. I strongly suggest every Data Scientist and Machine learning programmer learn Pandas to sanitize data before applying it to their model.
Another library, which I recommend is called Pandas. It's an extremely powerful tool and if you are dealing with data, you will need that. I strongly suggest every Data Scientist and Machine learning programmer learn Pandas to sanitize data before applying it to their model.
Whether you're a novice or an experienced programmer, this edition equips you with the tools and techniques needed to navigate the dynamic field of intelligent systems. If you need a course to learn that then you should check out Data Analysis with Pandas and Python course from Udemy to learn Pandas, another important Python library for Data analysis.
One of the key skills for Data scientists to acquire is the Data Visualization skill and thankfully Python has so many powerful libraries like Pandas, MatPlotLib, and Seaborn which you can use for your different data visualization needs.
This book will teach you how to use Pandas for data manipulation and how to use core plotting python libraries like MatPlotLib and Seaborn, and also show you to take advantage of declarative and experimental libraries like Altair.
This is one of the rare Python books which covers 9 essential Python libraries like Pandas, MatplotLib, Seaborn, Bokeh, Altair, GGPlot, GeoPandas, and VisPy. Overall a great Python book to learn Data Visualization for both beginners and intermediate Python developers.
If you want, you can also combine this book with the 2024 Python Data Analysis & Visualization Masterclass by Colt Steele on Udemy. It's a nice, up-to-date course for Data Analyst and Data Scientist to learn Data Visualization in 2024.
4. Data Science from Scratch: First Principles with Python 2nd Edition
The "Data Science from Scratch: First Principles with Python 2nd Edition" by Joel Grus is a comprehensive guide that emphasizes not only the mastery of data science tools but also the foundational ideas and principles that underpin them.Updated for Python 3.6, this edition provides hands-on experience by implementing tools, algorithms, and models from scratch, offering a deeper understanding of their workings. Aimed at individuals with a mathematical aptitude and some programming skills, Joel Grus covers essential topics such as Python programming, linear algebra, statistics, probability, and the core principles of data science.
The book also guides readers through the entire data science workflow, including data collection, exploration, cleaning, manipulation, and the fundamentals of machine learning.
With new material on deep learning, statistics, and natural language processing, this edition is designed to help navigate the complexities of today's data landscape, making it an invaluable resource for those aspiring to become proficient data scientists.
If you want, you can also combine this with the IBM Data Science Professional Certificate on Coursera, a great collection of online courses for aspiring data scientists form IBM experts. This training program is also available with the Coursera Plus.
If you want, you can combine with an online course like Python for Data Science and Machine Learning Bootcamp by Jose Portilla on Udemy, which also teaches Python with real-world problems to get the best of both worlds.
5. Automate The Boring Stuff With Python
This is the first book I have read on Python, and I have recommended it to a countless number of developers, and the best part is, none of them have said that this book is not helpful.
With all my experience learning Python for scripting and Data science, this is the best book to learn Python, and every Data Scientist should learn Python from this book.
The book not only covers python basics but also provides simple automation tips that will help in your day-to-day tasks. This is especially good for Data scientists and Business analysts who are involved in Data analysis and deal with a large amount of data.
If you want, you can combine with an online course like Python for Data Science and Machine Learning Bootcamp by Jose Portilla on Udemy, which also teaches Python with real-world problems to get the best of both worlds.
6. Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning 2nd Edition
The "Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning 2nd Edition" by Kyle Gallatin and Chris Albon is a valuable resource offering over 200 self-contained recipes designed to tackle various challenges encountered in machine learning projects.
Aimed at Python enthusiasts familiar with libraries like pandas and scikit-learn, this practical guide covers a spectrum of tasks, from loading data and training models to utilizing neural networks. The recipes provide hands-on solutions, each accompanied by ready-to-use code that can be tested with toy datasets.
This updated edition also goes beyond theoretical concepts, offering practical insights into constructing functional machine learning applications. The topics covered include working with different data sources, handling diverse data types such as numerical, categorical, text, images, and dates, along with aspects like dimensionality reduction, model evaluation, and selection.
Whether you are a seasoned practitioner or a newcomer to machine learning, this cookbook equips you with the essential tools to address real-world challenges in your projects.
If you want, you can combine with an online course like Python for Data Science and Machine Learning Bootcamp by Jose Portilla on Udemy, which also teaches Python with real-world problems to get the best of both worlds.
7. Python Data Science Handbook: Essential Tools for Working with Data 2nd Edition
This is another great Python book for fellow Data Scientists and Machine Learning Engineers. Data scientists can use to learn Python. This book covers essential topics like File/IO, data structures, networking, algorithms, etc. All of these topics are an excellent base for any tech-driven career, including Data Science and Machine learning.The "Python Data Science Handbook: Essential Tools for Working with Data 1st Edition" by Jake VanderPlas serves as a comprehensive and indispensable resource for researchers, scientists, and data analysts utilizing Python for data-related tasks.
This handbook consolidates key elements of the data science stack, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and more. Tailored for those already familiar with Python coding, this desk reference addresses day-to-day challenges in data manipulation, transformation, cleaning, visualization, and the construction of statistical or machine learning models.
The book is structured to cover essential tools such as IPython and Jupyter for computational environments, NumPy for efficient storage and manipulation of data arrays, Pandas for labeled data handling, Matplotlib for diverse data visualizations, and Scikit-Learn for clean implementations of crucial machine learning algorithms.
With a focus on practical applications and real-world problem-solving, this handbook stands as a must-have reference for anyone engaged in scientific computing with Python and if you want, you can also combine this with the Google's Data Analytics Professional certificate on Coursera which also teach you how to work with Data in depth.
That's all about some of the best Python books for learning Data Science and Machine Learning. I know courses are more active and engaging, and I don't suggest learning from online classes, but books also have their place. They are the most authoritative and complete source, or learning any technology and combining a good book with an excellent online course can really help you to take your skill to the next level.
If you are serious about learning Python in-depth, here are some more free and paid resources for Further Learning
- 10 Reasons to learn Python in 2024
- 5 Data Science and Machine Learning course in Python
- Top 5 Course to Learn Python for Beginners
- 10 Coursera Courses to learn Data Science and Data Visualization
- Top 5 Web Development Frameworks for Python Developers
- Top 5 Data Visualization Tools for Programmers
- Python vs. JavaScript - Which is better to start with?
- 10 Free Online courses to learn Python in depth
- 9 Data Science Courses from Harvard and IBM
- Top 8 Python libraries for Data Science and Machine Learning
- Python vs. Java - Which Programming language Beginners should learn?
- The 2024 Web Developer RoadMap
- 10 Free Python Programming Books for Programmers
- 5 Best Courses to learn Tableau for Data Analysts
- 10 Free Courses to learn Python in depth
- 10 Coursera Certifications to learn Python for Beginners
Thanks for reading this article so far. If you like these Python Data Science and Machine Learning books, then please share them with your friends and colleagues. If you have any questions or feedback, then please drop a note.
P.S. - If you prefer active learning, then you can also check out this list of 10 Data Science and Machine Learning courses for Python developers.
I bought the Python Data Science Handbook: Essential Tools for Working with Data from this list and I am amazed how less I knew about data science tooling, thanks a lot for sharing these awesome books.
ReplyDeleteHow much Data Scientist earn? any Idea? also is it possible for Java developer to switch career and become a Data Scientist? And, if they switch, do they get beginner salary or an experienced one?
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