Mastering Data Mining with Python ??? Find patterns hidden in your data: Find patterns hidden in your data

Megan Squire

$19.99 per month

2.7 (3 Ratings)

Paperback Aug 2016 268 pages 1st Edition

Megan Squire

$19.99 per month

2.7 (3 Ratings)

Paperback Aug 2016 268 pages 1st Edition

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Key benefits

Dive deeper into data mining with Python – don’t be complacent, sharpen your skills!
From the most common elements of data mining to cutting-edge techniques, we’ve got you covered for any data-related challenge
*Become a more fluent and confident Python data-analyst, in full control of its extensive range of libraries

Description

Data mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy – without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding. If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries. In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get. By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics.

Who is this book for?

This book is for data scientists who are already familiar with some basic data mining techniques such as SQL and machine learning, and who are comfortable with Python. If you are ready to learn some more advanced techniques in data mining in order to become a data mining expert, this is the book for you!

What you will learn

*Explore techniques for finding frequent itemsets and association rules in large data sets
*Learn identification methods for entity matches across many different types of data
*Identify the basics of network mining and how to apply it to real-world data sets
*Discover methods for detecting the sentiment of text and for locating named entities in text
*Observe multiple techniques for automatically extracting summaries and generating topic models for text
*See how to use data mining to fix data anomalies and how to use machine learning to identify outliers in a data set

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Sanjeev Jaiswal Sep 08, 2016

The title justifies the contents inside this book. Author has done really a commendable job. She has explained the concepts that a reader would need to explore data mining technology.I am one of the reviewer of this book and this way I got chance to read this book before it got published.User should be from Data mining domain and very fluent in Python to understand what she wants to explain. She has taken much time to write this book to explain the complex data structure and other concepts in easy manner.I would personally recommend this book for those who wants to get their hands dirty in data Mining with Python.

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Dimitri Shvorob Oct 11, 2016

The author's first ouvre, "Clean Data", was a dishonestly marketed atrocity, so when I recently came across a PDF of Prof. Squire's second book, and then saw it supported by a five-star review of the kind that many Packt books get *initially*, I decided to get involved. Not surprisingly, my opinion is not as complimentary. "Mastering Data Mining" is much better than "Clean Data", but three nasty things about "Clean Data" carry over in full. One is deceptive marketing: here, a book that's 80% about text analysis is presented as a "comprehensive guide to advance [sic] data analytics techniques". Another is the book being a recycling/repackaging of the author's past projects, as opposed to having material written for the book. Going back to the first point, if you were writing a book about data mining, you sure would go beyond text analysis, but if you are repackaging what you have, and what you have is text analysis, then that has to do. Finally, there's the bizarre coding. In "Clean Data", this was about a mess of typically un-commented code in different languages - some, like PHP, looking decidedly outmoded. (What kind of a teacher would be teaching her students PHP in 2016?) Here, it's all Python - but, first, it's a bad-beginner Python, and second and most spectacularly, Python used simply for automation. Prof. Squire insists on storing *and manipulating* data in a database - so instead of loading a dataset into Python and manipulating it using Python's vast arsenal, you witness the moronic sight of Python being used to fire SQL queries. Is teaching*this* really helping the beginners? I think not - and point readers to real data-science-with-Python books, by Raschka, Layton, and Coelho and Richert.

ConBor Oct 27, 2019

Rookie attempt at authoring a resource book. Should have just written a paper and called it a day.

Mastering Data Mining with Python ??? Find patterns hidden in your data: Find patterns hidden in your data

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