15 Best Free Data Science eBook

"Python Data Science Handbook" by Jake VanderPlas: This book covers essential Python libraries and tools for data science, including NumPy, Pandas, Matplotlib, and Scikit-Learn.

"R for Data Science" by Garrett Grolemund and Hadley Wickham: This book introduces the R programming language and its applications in data science, focusing on data manipulation, visualization, and modeling.

"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: This comprehensive book provides an in-depth understanding of deep learning concepts, algorithms, and applications.

"Bayesian Methods for Hackers" by Cameron Davidson-Pilon: This book introduces Bayesian statistical methods in an accessible manner, using Python and PyMC.

"Data Science at the Command Line" by Jeroen Janssens: Learn how to perform various data science tasks using the command line interface, including data cleaning, analysis, and visualization.

"The Art of Data Science" by Roger D. Peng and Elizabeth Matsui: This book covers the entire data science process, from formulating questions to communicating results effectively.

"Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani: This book provides an introduction to statistical learning methods, with applications in regression, classification, and clustering.

"Probabilistic Programming & Bayesian Methods for Hackers" by Cameron Davidson-Pilon: Dive deeper into probabilistic programming and Bayesian methods using Python and PyMC.

"Mining of Massive Datasets" by Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman: This book covers algorithms and techniques for processing large-scale datasets, including data mining and machine learning methods.

"Data Science for Business" by Foster Provost and Tom Fawcett: Learn how to apply data science techniques to solve real-world business problems, focusing on predictive modeling and analytics.

"Think Stats" by Allen B. Downey: This book introduces statistical concepts using Python, with a focus on practical data analysis and visualization.

"Data Jujitsu: The Art of Turning Data into Product" by DJ Patil: This eBook provides insights into effectively leveraging data for product development and decision-making.

"A First Course in Design and Analysis of Experiments" by Gary W. Oehlert and Timothy C. Matisziw: Learn about experimental design and statistical analysis techniques for designing and interpreting experiments.

"Data Science for Marketing Analytics" by Tommy Blanchard, Debasish Behera, and Pranshu Bhatnagar: This book explores the application of data science techniques in marketing analytics, covering topics such as customer segmentation and predictive modeling.

"Data Science for Internet of Things" by David Taieb and Gabor Szathmari: Explore the intersection of data science and IoT, learning how to collect, analyze, and derive insights from IoT data.