Data Mining with Python : Theory, Application, and Case Studies

Di Wu

ISBN 10: 1032598905 / ISBN 13: 9781032598901

Published by Chapman And Hall/CRC, 2024

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - This book focuses on the hands-on approach to learning Data Mining. It showcases how to use Python Packages to fulfill the Data Mining pipeline, which is to collect, integrate, manipulate, clean, process, organize, and analyze data for knowledge.

Seller Inventory # 9781032598901

Bibliographic Details

• Title: Data Mining with Python : Theory, Application, and Case Studies
• Publisher: Chapman And Hall/CRC
• Publication Date: 2024
• Language: English
• ISBN 10: 1032598905
• ISBN 13: 9781032598901
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 22D millimeters 254 millimeters width by 178 millimeters height by 22 millimeters depth
• Weight: 779 grams

About this title

Synopsis

Data is everywhere and it’s growing at an unprecedented rate. But making sense of all that data is a challenge. Data Mining is the process of discovering patterns and knowledge from large data sets, and Data Mining with Python focuses on the hands-on approach to learning Data Mining. It showcases how to use Python Packages to fulfill the Data Mining pipeline, which is to collect, integrate, manipulate, clean, process, organize, and analyze data for knowledge.

The contents are organized based on the Data Mining pipeline, so readers can naturally progress step by step through the process. Topics, methods, and tools are explained in three aspects: “What it is” as a theoretical background, “why we need it” as an application orientation, and “how we do it” as a case study.

This book is designed to give students, data scientists, and business analysts an understanding of Data Mining concepts in an applicable way. Through interactive tutorials that can be run, modified, and used for a more comprehensive learning experience, this book will help its readers to gain practical skills to implement Data Mining techniques in their work.

About the Author

Dr. Di Wu is an Assistant Professor of Finance, Information Systems, and Economics department of Business School, Lehman College. He obtained a Ph.D. in Computer Science from the Graduate Center, CUNY. Dr. Wu’s research interests are 1) Temporal extensions to RDF and semantic web, 2) Applied Data Science, and 3) Experiential Learning and Pedagogy in business education. Dr. Wu developed and taught courses including Strategic Management, Databases, Business Statistics, Management Decision Making, Programming Languages (C++, Java, and Python), Data Structures and Algorithms, Data Mining, Big Data, and Machine Learning.