Top Data Science BooksWhat is Data Science?
There is much debate among scholars and practitioners about what data science is, and what it isn’t. Does it deal only with big data? What constitutes big data? Is data science really that new? How is it different from statistics and analytics?
One way to consider data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics.
Choosing a data science book is one of the important steps to properly learn from the experts in the field. It doesn’t have to be labeled as a data science book as it can relate to one of its many branches.
As the field of Data Science continues to heat up fast, there are an increasing number of options to gain an education in this area.
in this post we will highlight the best Data Science books to learn from...
Lets start with the books list , keep in mind order doesn't mean anything
Data Science from Scratch: First Principles with Python
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.
- Get a crash course in Python
- Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science
- Collect, explore, clean, munge, and manipulate data
- Dive into the fundamentals of machine learning
- Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
Publisher: O'Reilly Media; 1 edition (April 30, 2015)
Data Mining: The Textbook
- Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems.
- Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data.
- Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor.
Deep Learning (Adaptive Computation and Machine Learning series)
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Hardcover: 800 pages
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Hardcover: 745 pages
Publisher: Springer; 2nd edition (2016)
Data Mining and Analysis: Fundamental Concepts and Algorithms
Hardcover: 562 pages
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Hardcover: 426 pages
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
- Explore the machine learning landscape, particularly neural nets
- Use scikit-learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the TensorFlow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets
- Apply practical code examples without acquiring excessive machine learning theory or algorithm details
Publisher: O'Reilly Media; 1 edition (April 9, 2017)
Applied Predictive Modeling
The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process. The data sets and corresponding code are available in the book's companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive.
Publisher: Springer; 2013 edition (September 15, 2013)
Data Mining: Practical Machine Learning Tools and Techniques
The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.
- Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects
- Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
- Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business.
- Accessible, including the basics of essential concepts of probability and random sampling
- Examples with R programming language and JAGS software
- Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis)
- Coverage of experiment planning
- R and JAGS computer programming code on website
- Exercises have explicit purposes and guidelines for accomplishment
- Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs
Publisher: Academic Press; 2 edition (November 17, 2014)