Data is everywhere and itâs 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: âWhat it isâ as a theoretical background, âwhy we need itâ as an application orientation, and âhow we do itâ 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.
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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.
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Section I. Data Wrangling 1. Data Collection. 2. Data Integration 3. Data Statistics 4. Data Visualization 5. Data Preprocessing Section II. Data Analysis 6. Classification 7. Regression 8. Clustering 9. Frequent Patterns 10. Outlier Detection
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Produktdetaljer
ISBN
9781032612645
Publisert
2024-04-10
Utgiver
Vendor
Chapman & Hall/CRC
Vekt
453 gr
Høyde
254 mm
Bredde
178 mm
AldersnivĂĽ
P, U, 06, 05
SprĂĽk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
390
Forfatter
Biographical note
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. Wuâs 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.