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Data Mining with Python

Di Wu
€167.40
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A01=Di Wu
Author_Di Wu
Category=UMX
Category=UMZ
Category=UNF
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classification algorithms
clustering methods
Data Analysis
Data Mining
Data Mining Tutorials
Data Science
Data Science Projects
Data Wrangling
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machine learning techniques
outlier detection
practical data mining workflows
Python
regression analysis
statistical modelling

Product details

  • ISBN 9781032612645
  • Weight: 453g
  • Dimensions: 178 x 254mm
  • Publication Date: 10 Apr 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

Named an Outstanding Academic Title of 2025 by Choice.

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 Di Wu

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.

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