Data Mining with Python - Di Wu

Data Mining with Python

Theory, Application, and Case Studies

(Autor)

Buch | Softcover
390 Seiten
2024
Chapman & Hall/CRC (Verlag)
978-1-032-59890-1 (ISBN)
56,10 inkl. MwSt
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.
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.

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.

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

Erscheinungsdatum
Reihe/Serie Chapman & Hall/CRC The Python Series
Zusatzinfo 222 Halftones, color; 222 Illustrations, color
Sprache englisch
Maße 178 x 254 mm
Gewicht 762 g
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Mathematik / Informatik Informatik Programmiersprachen / -werkzeuge
Mathematik / Informatik Informatik Software Entwicklung
Mathematik / Informatik Informatik Theorie / Studium
ISBN-10 1-032-59890-5 / 1032598905
ISBN-13 978-1-032-59890-1 / 9781032598901
Zustand Neuware
Haben Sie eine Frage zum Produkt?
Mehr entdecken
aus dem Bereich
Datenanalyse für Künstliche Intelligenz

von Jürgen Cleve; Uwe Lämmel

Buch | Softcover (2024)
De Gruyter Oldenbourg (Verlag)
74,95
Daten importieren, bereinigen, umformen und visualisieren

von Hadley Wickham; Mine Çetinkaya-Rundel …

Buch | Softcover (2024)
O'Reilly (Verlag)
54,90