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If you are a book lover like me, then you should start looking for data science books that are available to you for free. These books will teach you Python programming, the art of data science and machine learning, and introduce you to new tools and frameworks. Also, some books are built like a website so you can browse, search, and interact with the book.
Learn Python the right way is a book for beginners who are interested in learning Python but don’t know where to start. You can download the book or use a web interface to read it online. Each chapter comes with a YouTube tutorial that explains the syntax and functionality in detail.
Book cover
You can read the book, watch the tutorial, or even practice the code in the free online IDE To repeat. It covers all the basics you need to start your career in data science.
The book covers:
- Python basics and setup
- Variables, expressions and statements
- Creating the first program in Python
- functions
- conditionals
- Complex functions.
- Iteration
- String instruments
- tuples
- Event handling, Exceptions
- List, Dictionaries, Modules, Files
- Algorithms, Classes, Objects, OOP, Inheritance
- PyGame, Recursion, Queues
- Linked lists, stacks, trees
The Art of Data Science by Roger D. Peng et al. represents data analysis as an art of understanding the question, exploring the data, performing formal models, interpreting results, and communicating findings.
Book cover
Instead of focusing on statistics and coding, the book teaches you critical thinking. You will learn how to refine the questions, perform exploratory data analysis, apply linear regression or random forest, and interpret the output to provide actionable insights.
The book covers:
- Analysis Epicycles
- Pose and refine the question
- Exploratory data analysis
- Using models to explore your data
- Inference: a primer
- formal modeling
- Inference vs. Prediction: Implications for the Modeling Strategy
- Interpretation of your results
- Communication
Data science on the command line it’s my favourite, and I’ve written a detailed review about it on the KDnuggets blog. You can buy a book on Amazon or read the online version for free. The online version is interactive and comes with cool features.
Book cover
The book introduces you to essential command line tools with examples for performing all kinds of data science tasks. You can clean the data, perform data analysis and visualization, and train machine learning models, all from your terminal.
The book covers:
- Data collection
- Creating command line tools
- data cleansing
- Project management with Make
- exploring data
- Parallel Pipes
- modeling data
- Polyglot data science
Practical Machine Learning with Scikit-Learn, Keras and TensorFlow will teach you all about machine learning from the very beginning. You will learn how to build basic machine learning models for deep learning using Scikit-Learn, Keras, and TensorFlow. You will learn about classification, RNN, CNN, NLP, GAN, and reinforcement learning models.
Book cover
Before reading this book, you should understand that the book assumes that you understand the basics of Python and libraries like NumPy, pandas, and matplotlib.
The book covers:
- End-to-end machine learning project
- Classification
- Training the models
- support vector machines
- Decision tree
- joint learning
- dimensionality reduction
- RNN and CNN
- NLP
- WIN
- reinforcement learning
- Training and implementation of the scale model
Practical Deep Learning for Programmers is a print book, a web-based book, and a course that introduces you to the world of deep learning using fastai and PyTorch. It is my favorite course and book. You will learn everything about neural networks without delving into math or programming. The course is for anyone who knows the basics of the Python language.
Book cover
The book covers:
- From model to production
- data ethics
- Training a digit sorter
- Image Classification
- Other computer vision problems
- Training a state-of-the-art model
- Deep Dive into Collaborative Filtering
- Tabular modeling deep analysis
- Deep analysis of NLP: RNN
- Data transfer with the fastai mid-level API
- A language model from scratch
- Convolutional Neural Networks
- ResNets
- Deep analysis of application architectures
- The training process
- A Neural Network from the Fundamentals
- CNN Interpretation with CAM
- A fastai apprentice from scratch
All five books are great, and I will recommend these books to any beginner who is skeptical about a career in data science. Additionally, these books come with a how-to guide, code samples, and visual aids to explain complex terminology in a simple way.
I hope you like my list. If you have any recommendations, please mention them in the comments and I’ll try to add them to the list below.
abid ali awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on creating content and writing technical blogs on machine learning and data science technologies. Abid has a Master’s in Technology Management and a Bachelor’s in Telecommunications Engineering. His vision is to build an AI product using a graphical neural network for students battling mental illness.
