Data Mining with Python, Theory, Application, and Case Studies, Di Wu, 2024

Data Mining with Python, Theory, Application, and Case Studies, Di Wu, 2024.
    
   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 gain practical skills to implement Data Mining techniques in their work.




DESCRIPTIVE DATA ANALYSIS.
We begin with a thorough examination of data types. We categorize data into two distinct groups: non-numerical and numerical. Nonnumerical data encompasses qualitative information, such as categories or labels, while numerical data consists of quantitative values. Understanding the characteristics and significance of these data types is crucial for effective data analysis.

Central tendency measures are fundamental to statistical analysis. In this section, we delve into the heart of data summarization by introducing key measures, including the mean, median, and mode. These measures provide insights into the central or typical value within a dataset and are invaluable tools for data interpretation.

Our exploration continues with a focus on dispersion and location metrics. Dispersion measures, such as standard deviation and variance, quantify the spread or variability of data points. Location metrics, on the other hand, help pinpoint central positions within a dataset. We explore how these metrics contribute to a deeper understanding of data patterns and variability.

Contents.
List of Figures.
Foreword.
Preface.
Author Bios.
Section I Data Wrangling.
Chapter 1 Data Collection.
Chapter 2 Data Integration.
Chapter 3 Data Statistics.
Chapter 4 Data Visualization.
Chapter 5 Data Preprocessing.
Chapter 6 Classification.
Chapter 7 Regression.
Chapter 8 Clustering.
Chapter 9 Frequent Patterns.
Chapter 10 Outlier Detection.
Index.



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