đź“Š Data Science Books
A curated collection of books for learning data science, machine learning, statistics, Python, data analysis, and practical AI.
Python for Data Analysis
A practical guide to using Python for data manipulation, cleaning, analysis, visualization, and working with libraries such as pandas and NumPy.
Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow
A practical introduction to machine learning with hands-on examples covering supervised learning, neural networks, deep learning, model training, and production-oriented techniques.
Deep Learning with Python
An accessible practical introduction to deep learning concepts, neural networks, computer vision, natural language processing, and modern deep learning workflows.
AI-Assisted Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
A practical introduction to the statistical concepts that data scientists use for exploratory analysis, experimentation, probability, regression, and machine learning.
Data Science for Business
Explains the fundamental principles of data science and how analytical thinking can be applied to solve business problems and make better data-driven decisions.
An Introduction to Statistical Learning
A widely used introduction to statistical learning covering regression, classification, resampling methods, tree-based methods, support vector machines, and unsupervised learning.
Understanding Machine Learning From Theory To Algorithms
A comprehensive introduction to the theoretical foundations of machine learning. The book covers learning theory, optimization, linear prediction, support vector machines, neural networks, decision trees, and other fundamental machine learning concepts.
Sampling Techniques
A classic reference on statistical sampling methods. The book covers probability sampling, simple random sampling, stratified sampling, systematic sampling, cluster sampling, multistage sampling, and practical techniques for designing and analyzing sample surveys.
Introduction to Probability Theory
A foundational introduction to probability theory covering the basic principles of probability, random variables, probability distributions, mathematical expectation, limit theorems, and related concepts. It provides a strong mathematical foundation for students studying statistics, data science, mathematics, and machine learning.
Statistics, 4th Edition
A comprehensive introduction to statistics covering data analysis, probability, sampling, statistical inference, regression, and experimental design. The book emphasizes intuitive understanding of statistical concepts and their practical applications.
A First Course in Probability, Tenth Edition
A widely used introduction to probability theory covering fundamental concepts such as probability spaces, conditional probability, independence, random variables, probability distributions, mathematical expectation, limit theorems, and statistical applications. It provides a strong foundation for students studying probability, statistics, data science, and machine learning.
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