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Making Sense of Stream Processing: Behind Apache Kafka.

Regression Models for Data Science in R.

Global Optimization Algorithms: Theory and Application.

Genetic Programming: New Approaches and Successful Applications.

Exploring Math for Programmers and Data Scientists.

Inductive Logic Programming: Techniques and Applications.

Big Data, Data Mining, and Machine Learning.

Advanced Linear Models for Data Science.

R and Data Mining: Examples and Case Studies.

Knowledge-Oriented Applications in Data Mining.

Data Mining and Knowledge Discovery in Real Life Applications.

Analyzing Linguistic Data: A Practical Introduction to Statistics.

Fundamental Numerical Methods and Data Analysis.

Introduction to R – Notes on R, A Programming Environment for Data Analysis and Graphics.

Disruptive Possibilities: How Big Data Changes Everything.

Theory and Applications for Advanced Text Mining.

Executive Data Science – A Guide to Training and Managing the Best Data Scientists.

Data Jujitsu: The Art of Turning Data into Product.

Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery.

Data Science: Theories, Models, Algorithms, and Analytics.

Linear Regression Using R: An Introduction to Data Modeling.

Oracle Database Notes for Professionals.

Introduction to Data Science: Data Analysis and Prediction Algorithms with R.

Advanced Statistics From an Elementary Point of View.

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition.

Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference.

Modeling with Data: Tools and Techniques for Scientific Computing.

Data Visualization: A Practical Introduction.

Statistical Learning with Sparsity: The Lasso and Generalizations.

Automate the Boring Stuff with Python: Practical Programming for Total Beginners.

21 Recipes for Mining Twitter Data with rtweet.

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Think Python: How to Think Like a Computer Scientist.Genetic Algorithms in Search, Optimization, and Machine Learning.Data Mining and Analysis: Fundamental Concepts and Algorithms.Think Stats: Exploratory Data Analysis in Python.Modern Data Science with R, 2nd edition.Exploring, Visualizing, and Modeling Big Data with R.Statistics in Plain English, Third Edition.Model-Based Clustering and Classification for Data Science.Statistical rethinking with brms, ggplot2, and the tidyverse: Second edition.Interactive web-based data visualization with R, plotly, and shiny.Supervised Machine Learning for Text Analysis in R.

Modern Statistics with R: From wrangling and exploring data to inference and predictive modelling.The Elements of Statistical Learning: Data Mining, Inference, etc.Efficient R Programming: A Practical Guide to Smarter Programming.

Spatial Data Science: With applications in R.

Statistical Inference Via Data Science: A ModernDive Into R and the Tidyverse.

Data-Intensive Text Processing with MapReduce.

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data.Introduction to Probability for Data Science.Principles and Techniques of Data Science.Computational and Inferential Thinking: The Foundations of Data Science, 2nd Edition.A Beginner’s Guide to Clean Data: Practical advice to spot and avoid data quality problems.Probability, Statistics, and Data: A Fresh Approach Using R.Building Secure and Reliable Systems – Best Practices for Designing, Implementing, and Maintaining Systems.R Cookbook: Proven Recipes for Data Analysis, Statistics and Graphics, 2nd Edition.GGPlot2: Elegant Graphics for Data Analysis, 2nd Edition.R Graphics Cookbook: Practical Recipes for Visualizing Data, 2nd Edition.Data Science at the Command Line, 2nd Edition.An Introduction to Statistical Learning, 2nd Edition.

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Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code.100+ Free Statistics, Data Mining, Python, Mathematics, Data Visualization, SQL & Data Analytics Books Are As Follows.Some of the Free And Best Data Science Books For Beginners, Intermediate and Advanced Enthusiast (Our Favorite from the List).