9 Best Books on Python for Data Science
With the demand for Python skills in data science higher than ever, finding the right resources to master it is key. If you're serious about leveling up your data science game in 2024, the 9 books mentioned in this blog will help you ace the Python portion of your interview.
“Python for Data Analysis" by Wes McKinney
This “Python for Data Analysis” is the first book I recommend if you want to master Python for data science. The book is written by the creator of Pandas Library, Wes Mckinney. This book is perfect for understanding working with large datasets, Python's role in data science, and performing complex manipulations.
In this book, he clearly explains on how to use Python for complex data analysis in a way that is great read for beginners. The book has practical examples on data wrangling, cleaning and visualization. Whether you are preparing for an upcoming data science interview or just want to upgrade your python skills, this book is a great start.
“Ace the Data Science Interview” by Nick Singh
While not a book only about Python this is the ultimate one stop shop for everything you need to know about the data science interview. This book covers everything from Probability, Statistics, Machine Learning, SQL & Database Design, Python Coding Questions, & Product Sense.
Ace the Data Science Interview has 201 questions from real Data Science interviews, with full solutions for each problem. These interview questions come from companies like Facebook, Google, Amazon, Microsoft, Netflix, Stripe, Uber, Two Sigma and Citadel.
"Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili
"Python Machine Learning" is the best book available if you want to master machine learning with Python. It covers a wide range of machine learning techniques, from basic algorithms to advanced deep learning models and neural networks, all implemented in Python.
Practical examples in this book will help you understand how machine learning models are applied in real-world scenarios. It focuses on libraries such as scikit-learn, TensorFlow and Keras.
"Python for Data Science For Dummies" by Luca Massaron and John Paul Mueller
If you are a newbie in Python and Data Science, then "Python for Data Science For Dummies" by Luca Massaron and John Paul Mueller could be an amazing start. You'll find complex concepts broken down into simple, understandable explanations, which makes it easy for people who are just starting to learn Python for data science.
Unlike other technical books that overwhelm newbies with jargon, this book makes learning Python very easy, unintimidating, and straightforward. The author covers core topics such as data manipulation, machine learning, and visualization with practical examples.
"Automate the Boring Stuff with Python" by Al Sweigart
Why waste you time on repetitive tasks when, when you can automate them in Python? "Automate the Boring Stuff with Python" is a perfect resource for learning how to automate boring stuff. Whether you are working with hundreds of CSVs or extracting large amounts of data from a resource, this book tells you how to make Python your ultimate time-saving tool.
Al Sweigart covers practical examples like automating Excel reports and batch-renaming files. These are the tasks that data scientists and developers would spend days on if they start doing them manually. By the end of this book you'll be able to automate tedious tasks and also integrate the solutions into your data science pipelines.
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
If you are an aspiring data scientist, you need to master machine learning. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" will provide you with key skills and knowledge that are necessary for machine learning projects in data science. What sets this book apart is its project-based approach. In every chapter, you will find real-world projects that will help you apply what you have learned in theory.
You'll build everything from linear regression models to sophisticated neural networks, leveling up your machine-learning knowledge. The author has simplified the concepts in a way that makes them more fun. It is highly recommended as a starting point for your machine learning and deep learning journey. However it’s important to have some prior experience with Python. Plus, a basic understanding of calculus—covering topics like linear algebra, vector functions, and gradients—can be helpful.
"Python Data Science Handbook" by Jake VanderPlas
If you are passionate about working with data, the "Python Data Science Handbook" is a must-read. It's a great resource to learn tools and techniques for data science. VanderPlas covers the main libraries of Python, such as NumPy for numerical computations, Pandas for data manipulation, Matplotlib for data visualization, and Scikit-Learn for machine learning.
What’s awesome about this book is how it has a perfect blend of theory and exercises. It also has interactive Jupyter notebook format.
"Data Science from Scratch: First Principles with Python" by Joel Grus
"Data Science from Scratch: First Principles with Python" is an excellent beginner-friendly book for getting familiar with the core concepts of data science. Joel Grus is a great teacher who explains advanced concepts of data science in a simple and friendly way. He begins with the key concepts in data science such as statistics, probability, and data visualization. The focus is on building algorithms from scratch.
Either you’re a beginner or a data scientist looking to advance your understanding of data science, Joel Grus's book is for you.
"Think Stats: Exploratory Data Analysis in Python" by Allen B. Downey
Lastly, we recommend "Think Stats: Exploratory Data Analysis in Python" by Allen B. Downey. If you want to become a data scientist, it's great to have strong skills in statistics, and this book can be a great start as a beginner.
The author breaks down stats concepts into simple lesson using real datasets to help the reader understand through exploratory data analysis (EDA). His approach to writing is also very straightforward and simple which makes it easy to read for audiences of all levels.
More Python Data Science Resources
Leetcode is a great place to start when focusing on Python, but it isn’t the best resource when preparing for the data science interview. Here are 5 Leetcode Alternatives for Data Scientists. to help you gear up for your next interview.
Python Interview Questions for Data Science
If you're itching to start practicing now, DataLemur has a ton of Python interview questions ready for you to tackle. You can answer questions from top tech companies like Google, Amazon, and Microsoft, and level up your Python skills. Check out the full set of Python interview challenges at DataLemur Python Interview Questions.