Best Data Mining Books in 2022

13 minutes read

Data Mining, much like mining for bitcoins and cryptocurrency, it is the latest and greatest thing.


Data mining sounds esoteric, and it is in part, but data mining is very practical. Businesses use data mining for very practical processes such as credit risk management, fraud detection, to discern customer choices and preferences.


One of the most common use of data mining is to analyze millions of social media posts to decide which advertisements they would most be drawn to if given the ad is presented.


Colleges can use data mining to select the best prospects for their college or university, and even a small restaurant can utilize the process to determine what special to offer and how often.


What is data mining and how it works?


Data mining is taking as much data as possible, (often up to a billion parts) and using sophisticated software to see where the gold nuggets lie between millions more parts of the data that are not useful.


Top Rated Data Mining Books of October 2022

1
Data Science Using Python and R (Wiley Series on Methods and Applications in Data Mining)

Rating is 5 out of 5

Data Science Using Python and R (Wiley Series on Methods and Applications in Data Mining)

2
Machine Learning: 4 Books in 1: Basic Concepts + Artificial Intelligence + Python Programming + Python Machine Learning. A Comprehensive Guide to Build Intelligent Systems Using Python Libraries

Rating is 4.9 out of 5

Machine Learning: 4 Books in 1: Basic Concepts + Artificial Intelligence + Python Programming + Python Machine Learning. A Comprehensive Guide to Build Intelligent Systems Using Python Libraries

3
Hands-On Machine Learning with R (Chapman & Hall/CRC The R Series)

Rating is 4.8 out of 5

Hands-On Machine Learning with R (Chapman & Hall/CRC The R Series)

4
Introduction to Machine Learning with Python: A Guide for Data Scientists

Rating is 4.7 out of 5

Introduction to Machine Learning with Python: A Guide for Data Scientists

5
Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More

Rating is 4.6 out of 5

Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More

6
Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

Rating is 4.5 out of 5

Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

  • Morgan Kaufmann Publishers
7
Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

Rating is 4.4 out of 5

Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

8
Data Mining: The Textbook

Rating is 4.3 out of 5

Data Mining: The Textbook

  • Springer
9
Introduction to Data Mining (2nd Edition) (What's New in Computer Science)

Rating is 4.2 out of 5

Introduction to Data Mining (2nd Edition) (What's New in Computer Science)

10
Data Mining for Business Analytics: Concepts, Techniques and Applications in Python

Rating is 4.1 out of 5

Data Mining for Business Analytics: Concepts, Techniques and Applications in Python

11
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Rating is 4 out of 5

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

  • Language Published: English
  • Binding: Hardcover
12
Introduction to Data Mining and Analytics

Rating is 3.9 out of 5

Introduction to Data Mining and Analytics

13
Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies

Rating is 3.8 out of 5

Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies


What do you need for data mining?


Basically, to succeed in the DM field you need a deep background in computer languages such as Java, Python, and Perl, and an understanding of the different DM analysis tools such as SQL, NoSQL, SAS, and Hadoop.


You also need to understand exactly what the business you want needs to get ahead because without being practical and business-minded, you will not succeed.


How does Data Mining make money?


By providing solid answers to what a business needs, whether it be in making credit decisions, reducing computer fraud, or running data to define what ads that a Tik Tok users might most respond to, you provide valuable information that the company did not know they even had at their fingertips so to speak.


Note, that although Data mining can be a highly profitable business, dealing with perhaps a billion to 5 billion pieces of information is not something that can be handled by a single individual.


Therefore, the vast majority of mining of data is done by a conglomerate of business analysts. One or two individuals simply do not have the resources to hang out a shingle and expect to attract many clients.


Maybe in the case of an individual small business such as a restaurant or perhaps a limo company, yes, but certainly not a Fortune 500 company that has the resources either have full-time data analysts working full time for them, or contracting out.


How long does it take to learn data mining?


Most universities offer a certificate program that may take around 4 to 5 months to complete, and some universities offer a full 4-year degree.


Note, however, that if you dedicate around 6 hours per day to learn mining and analysis techniques, to be really useful in the career field, most people need around 2 or more years of experience to really become proficient.


Can you learn data analysis from a book?


Not really. You can gain a lot of the fundamentals of the field from books, but they are no substitute for an intensive course.


What are the top data analysis books available?


Data Science Using Python and R


Written for the general reader with no previous analytics or programming experience this hardcover book covers most of the basics, including learning Python and R programming languages.


There are over $500 exercises in this book that will give you a practical summation of the essence of data science.


Machine Learning: 4 Books in 1: Basic Concepts + Artificial Intelligence + Python Programming + Python Machine Learning. A Comprehensive Guide to Build Intelligent Systems Using Python Libraries.


This is a nearly 500-page volume of books that will enable those who are both new to data science and those who have a bit of programming language to get a great understanding of the field of data science.


Hands-On Machine Learning with R


R is one of the primary languages that data miners use to analyze statistics for data science. Understanding R is one of your best steps to being a good data analyst, and this book is certainly one to consider.


Introduction to Machine Learning with Python: A Guide for Data Scientists


If Python is your preference, this book will lead you along the way to the entrance doors of data science. For most, both R and Python are considered essential.


Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More


In case you need inspiration before seriously taking a paid course, you might want to read this book as data mining in social media now provides, and will provide in the future, many of the best data ming jobs.


Quite honestly, we do not believe any single book on data mining will replace a serious course that takes you 4 to 5 months to complete and requires you to invest up to 6 hours a day learning the essence of data science as it applies to business.


But books such as these are often a good start to fire your imagination and keep you moving toward your goal as becoming a data scientist.

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