This is the online list of the same curated data science e-books that you’ll find in the DAT Linux Control Panel “Extras” tab, as well as enhanced features to filter and favourite e-books with a PRO tools license. To report an error or suggest a book, email datlinux AT protonmail DOT com.
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Books found: 0
📔️ A Business Analyst’s Introduction to Business Analytics - Fleischhacker [
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📔️ A Crash Course in Geographic Information Systems (GIS) using R - Branion-Calles [
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📔️ A First Course in Linear Algebra - Beezer [
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📔️ A First Course on Statistical Inference - Peralta, García-Portugués [
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📔️ Agile Data Science with R - Edwin Thoen [
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📔️ A Little Book of R for Bioinformatics 2.0 - Avril Coghlan [
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📔️ Analysing Data using Linear Models: With applications in R - van den Berg [
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📔️ Analysing Quantitative Data with R - University of Warwick [
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📔️ Analyzing US Census Data: Methods, Maps, and Models in R - K. Walker [
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📔️ Analytics with Power BI and R - Etaati [
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📔️ An Introduction to Bayesian Thinking - Clyde, Çetinkaya-Rundel, et al [
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📔️ An Introduction to Data Analysis - Michael Franke [
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📔️ An Introduction to Earth and Environmental Data Science - Ryan Abernathey [
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📔️ An Introduction to Spatial Data Analysis and Statistics: A Course in R - Paez [
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📔️ An Introduction to Statistical Learning 2ed - James, Witten, et al [
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📔️ Answering questions with data: Introductory Statistics for Psychology Students - Navarro, Suzuki [
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📔️ Applied Statistics with R - David Dalpiaz [
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📔️ A Whirlwind Tour of Python - Jake VanderPlas [
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📔️ Bayes Rules! An Introduction to Applied Bayesian Modeling - Johnson, Ott, Dogucu [
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📔️ Beyond Multiple Linear Regression - Roback & Legler [
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📔️ Big Data Analytics - Ulrich Matter [
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📔️ Big Data and Social Science - Foster, Ghani, et al [
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📔️ Authoring Books and Technical Documents with R Markdown - Yihui Xie [
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📔️ Computational and Inferential Thinking: The Foundations of Data Science - 2ed. Adhikari, DeNero, & Wagner [
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📔️ Computational Genomics with R - Altuna Akalin [
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📔️ Data analysis with Python (Summer 2021) - University of Helsinki [
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📔️ Data Analytics: A Small Approach - Huang, Deng [
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📔️ Data Mining and Machine Learning - Zaki, Meira, et al [
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📔️ Data Science: A First Introduction - Timbers, Campbell, & Lee [
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📔️ Data Science for Psychologists - Hansjörg Neth [
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📔️ Data Science for Startups - Ben G Weber [
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📔️ Data Science in Education Using R - Estrellado, Bovee, et al [
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📔️ Data Science in Julia for Hackers - Carrone, Nicolini, & Obst Demaestri [
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📔️ Data Science Live Book - Pablo Casas [
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📔️ Data Science Practice - Stephenson [
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📔️ Data Visualization: A Practical Introduction - Kieran Healy [
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📔️ Data Visualization with R - Rob Kabacoff [
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📔️ Deep Learning - Goodfellow, Bengio, & Courville [
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📔️ Designing and Building Data Science Solutions - Van Otten [
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📔️ Dive into Deep Learning - Zhang, Aston, et al [
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📔️ Efficient R Programming - Gillespie & Lovelace [
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📔️ Engineering Production-Grade Shiny Apps - Fay, Rochette, et al [
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📔️ Essentials of Geographic Information Systems [
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📔️ Explanatory Model Analysis - Biecek & Burzykowski [
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📔️ Exploratory Data Analysis with R - Roger D. Peng [
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📔️ Exploring Enterprise Databases with R: A Tidyverse Approach - Smith, Yang, et al [
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📔️ Exploring, Visualizing, and Modeling Big Data with R - Bulut & Desjardins [
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📔️ Feature Engineering and Selection - Kuhn & Johnson [
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📔️ Financial Data Science - Riordan [
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📔️ Forecasting: Principles and Practice - Hyndman & Athanasopoulos [
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📔️ Fundamentals of Data Visualization - Claus O. Wilke [
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📔️ Geocomputation with Python - Dorman, Graser, et al [
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📔️ Geocomputation with R - Lovelace, Nowosad, & Muenchow [
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📔️ Geographic Data Analysis - University of Oregon [
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📔️ Geographic Data Science with Python - Rey, Arribas-Bel, Wolf [
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📔️ Geographic Data Science with R - Wimberly [
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📔️ ggplot2: elegant graphics for data analysis - Wickham, Navarro, & Pedersen [
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📔️ Hands-On Data Visualization - Dougherty & Ilyankou [
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📔️ Hands-On Machine Learning with R - Boehmke & Greenwell [
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📔️ Hands-On Programming with R - Garrett Grolemund [
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📔️ Happy Git and GitHub for the useR - Jennifer Bryan [
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📔️ Interpretable Machine Learning - Christoph Molnar [
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📔️ Introduction to Applied Linear Algebra - Boyd & Vandenberghe [
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📔️ Introduction to Cultural Analytics & Python - Melanie Walsh [
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📔️ Introduction to Data Science: Data Analysis and Prediction Algorithms with R - Irizarry [
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📔️ Introduction to Environmental Data Science - Jerry Davis [
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📔️ Introduction to GIS - Víctor Olaya [
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📔️ Introduction to Inferential Statistics - Trussler, Radley [
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📔️ Introduction to Modern Statistics - Çetinkaya-Rundel & Hardin [
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📔️ Introduction to Probability for Data Science - Stanley H. Chan [
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📔️ Introduction to Spatial Data Programming with R - Dorman [
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📔️ Introductory Statistics - Saylar Academy [
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📔️ Intro to GIS and Spatial Analysis - Manuel Gimond [
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📔️ IPython Cookbook 2ed - Cyrille Rossant [
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📔️ ISLR2 Labs (as a book) [
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📔️ Julia Data Science - Storopoli, Huijzer & Alonso [
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📔️ Jupyter Guide to Linear Algebra - Vanderlei [
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📔️ Learning Data Science - Lau, Gonzalez, & Nolan [
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📔️ Learning From Data - Forssén [
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📔️ Learning statistics with R: A tutorial for psychology students and other beginners - Navarro [
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📔️ Linear Algebra for Data Science with examples in R - Shaina Race Bennett [
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📔️ Linear Algebra - WikiBooks [
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📔️ Little Book of R for Time Series - Coghlan [
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📔️ Machine Learning and Big Data - Alkaseer [
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📔️ Machine Learning for Data Streams - Bifet, Gavaldà, et al. [
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📔️ NUMPY BY EXAMPLE - A Beginner's Guide to Learning NumPy - DAT Linux [
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📔️ Mastering Shiny - Wickham [
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📔️ Mastering Software Development in R - Peng, Kross [
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📔️ Mastering Spark with R - Luraschi, Kuo, & Ruiz [
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📔️ Mining of Massive Datasets - Leskovec, Rajaraman, Ullman [
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📔️ Applied Machine Learning Using mlr3 in R - Becker, Binder, et al [
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📔️ Modern Applied Data Analysis - University of Georgia [
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📔️ Modern Data Science with R: 3ed - Baumer, Kaplan, & Horton [
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📔️ Modern Data Visualization with R - Kabacoff [
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📔️ Modern R with the tidyverse - Rodrigues [
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📔️ Modern Statistics with R - Måns Thulin [
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📔️ Natural Language Processing with Python - Bird, Klein, Loper [
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📔️ Network Data Science - Pedigo [
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📔️ Neural Networks and Deep Learning - Nielson [
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📔️ Pattern Recognition and Machine Learning - Bishop [
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📔️ Practical Data Science - Clark [
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📔️ Probabilistic Programming: Bayesian Methods for Hackers - Davidson-Pilon [
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📔️ Probability, Statistics, and Data: A Fresh Approach Using R - Speegle & Clair [
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📔️ Pro Git - Chacon & Straub [
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📔️ Python Data Science Handbook - Jake VanderPlas [
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📔️ Python for Data Analysis, 3E - Wes McKinney [
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📔️ QGIS for Transport Research - Lovelace, Morgan, & Philips [
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📔️ R Companion to Statistics: Data Analysis and Modelling - Maarten Speekenbrink [
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📔️ R Cookbook - Long & Teetor [
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📔️ Regression and Other Stories - Gelman, Hill, & Vehtari [
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📔️ Reproducible Machine Learning for Credit Card Fraud detection: Practical handbook - Borgne, Siblini , et al [
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📔️ R for Data Analysis - French [
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📔️ R for Data Science - Wickham & Grolemund [
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📔️ R for Geographic Data Science - Stefano De Sabbata [
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📔️ R for Health Data Science - Harrison, Pius [
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📔️ R Graphics Cookbook 2ed - Winston Chang [
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📔️ R Markdown: The Definitive Guide - Xie, Allaire, & Grolemund [
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📔️ R Programming for Data Science - Roger D. Peng [
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📔️ R Programming - WikiBooks [
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📔️ R Programming: Zero to Pro - Feng & Zhu [
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📔️ Scipy Lecture Notes - scipy-lectures.org [
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📔️ Social Media Mining: An Introduction - Zafarani, Abbasi, Liu [
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📔️ Spatial Data Science - Pebesma & Bivand [
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📔️ Spatial Modelling for Data Scientists - Rowe & Arribas-Bel [
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📔️ Spatial Statistics for Data Science: Theory and Practice with R - Moraga [
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📔️ Speech and Language Processing - Jurafsky & Martin [
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📔️ STA237: Probability, Statistics and Data Analysis I - University of Toronto [
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📔️ STAT 414: Introduction to Probability Theory - Penn. State [
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📔️ STAT 500: Applied Statistics - Penn. State [
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📔️ STAT 501: Regression Methods - Penn. State [
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📔️ STAT 505: Applied Multivariate Statistical Analysis - Penn. State [
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📔️ STAT 508: Applied Data Mining and Statistical Learning - Penn. State [
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📔️ STAT 510: Applied Time Series Analysis - Penn. State [
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📔️ STAT 545: Data wrangling, exploration, and analysis with R - Bryan [
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📔️ Statistical Analysis Handbook - Dr Michael J de Smith [
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📔️ Statistical Inference via Data Science - Ismay & Kim [
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📔️ Statistical Learning with Sparsity: The Lasso and Generalizations - Hastie, Tibshirani, Wainwright [
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📔️ Statistical Methods for Data Science - Elizabeth Purdom [
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📔️ Statistical Thinking for the 21st Century - Russell A. Poldrack [
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📔️ Advanced Data Science - Irizarry [
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📔️ Statistics: Data analysis and modelling - Maarten Speekenbrink [
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📔️ Supervised Machine Learning for Text Analysis in R - Hvitfeldt & Silge [
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📔️ Telling Stories with Data: With Applications in R and Python - Alexander [
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📔️ Text Analysis with R - Engel & Bailey [
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📔️ Text Mining for Social Scientists - Lennert [
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📔️ Text Mining with R - Silge & Robinson [
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📔️ Text Mining with Tidy Data Principles - Silge [
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📔️ The Data Engineering Cookbook - Andreas Kretz [
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📔️ The Data Science Interview Book [
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📔️ The Data Validation Cookbook - van der Loo & ten Bosch [
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📔️ The Hitchhiker's Guide to Python - Reitz & Schlusser [
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📔️ The Turing Way: Handbook to reproducible, ethical and collaborative data science - Arnold, Bowler et al [
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📔️ Think Bayes 2 - Allen B. Downey [
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📔️ Think Stats: Exploratory Data Analysis in Python - Allen B. Downey [
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📔️ Exploratory Data Analysis in R - Gimond [
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📔️ Introduction to Power BI - Monash Bioinformatics Platform [
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📔️ Learning Data Visualization with Tableau - Hayley Boyce [
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