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Best Data Science Books

Understanding data preparation, the importance of big data, and automated procedures all contribute to the future of data science.

New learners need to understand basic algorithms and tools in order to evaluate data, investigate trends, and make informed judgments. The recommended data science books can help newcomers learn even if they have no prior experience. Professionals must also advance their expertise and use advanced algorithms in real-world circumstances. This article recommends Best Data Science Books including topics like mathematics, probability, statistical learning, programming, and machine learning to help you fully grasp the discipline.

Top Data Science Books for Beginners
1- An Introduction to Data Science
2- NoSQL Databases
3- Python for Everybody
4- Probability and Statistics Cookbook
5- The Elements of Data Analytic Style
6- The Data Science Handbook
7- Data Driven: Creating a Data Culture
8- Neural Networks and Deep Learning
9- Deep Learning
10- A Programmer’s Guide to Data Mining
11- Automate the Boring Stuff with Python: Practical Programming for Total Beginners
12- A Little Book of R for Time Series
13- Think Python 2nd Edition
14- Probabilistic Programming & Bayesian Methods for Hackers
15- Learning Statistics with R
16- Open Intro Statistics
17- D3 Tips and Tricks
18- Intro Stat with Randomization and Simulation
19- The Art of Data Science
20- R Programming for Data Science
21- Linear Algebra, Theory And Applications
22- Real-World Active Learning
23- School of Data Handbook
24- Python for You and Me
25- Python Programming
26- Cassandra Tutorial as a PDF
27- Advanced R
28- Mining of Massive Datasets
29- Data Mining and Analysis: Fundamental Concepts and Algorithms
30- Understanding Machine Learning: From Theory to Algorithms
31- Elementary Applied Topology
32-Bayesian Reasoning and Machine Learning
33-Social Media Mining an Introduction
34- Learn Python, Break Python
35- Hadoop Illuminated
36- A Course in Machine Learning
37- Think Stats: Exploratory Data Analysis in Python
38- Data Mining Algorithms In R
39- Python Practice Book
40- R Programming
41- Spatial Epidemiology Notes: Applications and Vignettes in R
42- An Introduction to Statistical Learning with Applications in R
43- Elementary Differential Equations
44- Interactive Data Visualization for the Web
45- Learn Python the Hard Way
46- Graph Databases
47- The LION Way: Machine Learning plus Intelligent Optimization
48- Disruptive Possibilities: How Big Data Changes Everything
49- KB – Neural Data Mining with Python Sources
50- Linear Algebra
51- Reinforcement Learning: An Introduction
52- Learning with Python 3
53- Programming Computer Vision with Python
54- Theory and Applications for Advanced Text Mining
55- The Little MongoDB Book
56- Think Bayes: Bayesian Statistics Made Simple
57- Data Jujitsu: The Art of Turning Data into Product
58- A First Course in Linear Algebra
59- Probabilistic Models in the Study of Language
60- Extracting Data from NoSQL Databases
61- Building Data Science Teams
62- Data Mining with Rattle and R
63- The R Inferno
64- Programming Pig
65- A First Encounter with Machine Learning
66- Computer Vision
67- Data-Intensive Text Processing with MapReduce
68- Artificial Intelligence: Foundations of Computational Agents
69- A First Course in Design and Analysis of Experiments
70- Invent with Python
71- Natural Language Processing with Python
72- Algorithms for Reinforcement Learning
73- Dive Into Python 3
74- Machine Learning
75- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
76- Ecological Models and Data in R
77- Modeling With Data
78- Introduction to Machine Learning
79- Introduction to Machine Learning
80- Pattern Recognition and Machine Learning
81- Gaussian Processes for Machine Learning
82- Information Theory, Inference, and Learning Algorithms
83- Data Mining: Practical Machine Learning Tools and Techniques
84- R by Example
85- A Byte of Python
86- Practical Regression and Anova using R
87- Machine Learning, Neural and Statistical Classification
88- Introduction to Probability
89- Artificial Intelligence a Modern Approach, 1st Edition
90- SQL for Web Nerds
91- Machine Learning – The Complete Guide
92- Hadoop Tutorial as a PDF
93- The R Manuals
94- Linear Algebra: An Introduction to Mathematical Discourse
95- Ordinary Differential Equations
96- SQL Tutorial as a PDF

Top 5 Book categories:

Here we show the top 5 book categories.

There are 28 books about Data Mining & ML, 23 books about Learning Languages, and 7 books about SQL, NoSQL & Databases.

The most popular book categories are “Data Mining & ML ” with category (28), and the least popular book categories are “SQL, NoSQL&Databases” with category (7).

Top 10 Rated Books:

Here we show the top 10 Rated Books.

“SQL for Web Nerds” is Rated (5/5), followed by “An Introduction to Statistical Learning” rated (4.6/5)

The best-rated book is “SQL for Web Nerds” (5/5), and the least-rated book is “A Programmer’s Guide to Data Mining” rated (4.3/5).

SQL for Web Nerds

It covers fundamental database principles such as ACID and how RDBMS function; includes examples of simple and sophisticated queries, transactions, triggers, and views; and addresses challenges such as running Java inside an Oracle Server, dealing with foreign and legacy data, and normalization.

An Introduction to Statistical Learning

provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

Top 5 years by Number of Books:

Here we show the top 5 years by number of books.

In 2015 22 books were published, followed by” 2014” with (15) books.

The Top year is “2015” with (22) books, and the least year is “2009” with (6) books.

Top Publishing Authors:

Here we show top Publishing Authors.

We find that “Wikibooks” published 5 books, followed by “Allen Downey” with 3 books.

“Wikibooks” published the most number of books


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