Data Mining for Beginners (eBook, ePUB)
A Programmer's Guide
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Data Mining for Beginners: A Programmer's Guide delves into the world of data mining, a process of discovering patterns and trends in large volumes of data using various algorithms and techniques. This book offers a comprehensive introduction to data mining, focusing on important concepts and their implementation using Python, a popular programming language. We provide step-by-step guidance through Python code to help readers understand and apply data mining techniques. The book covers essential topics like clustering, anomaly detection, data visualization, and processing, making it easier to ...
Data Mining for Beginners: A Programmer's Guide delves into the world of data mining, a process of discovering patterns and trends in large volumes of data using various algorithms and techniques. This book offers a comprehensive introduction to data mining, focusing on important concepts and their implementation using Python, a popular programming language.
We provide step-by-step guidance through Python code to help readers understand and apply data mining techniques. The book covers essential topics like clustering, anomaly detection, data visualization, and processing, making it easier to grasp these concepts and use them in various fields.
By the end of the book, readers will be well-versed in data mining concepts and capable of implementing them with Python.
What you will learn:
. Introduction to data mining and its various concepts.
. Data visualization and processing techniques.
. The importance of statistics in data mining.
. Different data mining algorithms and their implementation in Python.
. Cluster analysis and anomaly detection using Python.
. Data Cube Technology.
. Future trends and research frontiers in data mining.
Who the book is for: This book is ideal for programmers seeking to implement data mining algorithms using Python and for students looking for a solid introduction to data mining.
We provide step-by-step guidance through Python code to help readers understand and apply data mining techniques. The book covers essential topics like clustering, anomaly detection, data visualization, and processing, making it easier to grasp these concepts and use them in various fields.
By the end of the book, readers will be well-versed in data mining concepts and capable of implementing them with Python.
What you will learn:
. Introduction to data mining and its various concepts.
. Data visualization and processing techniques.
. The importance of statistics in data mining.
. Different data mining algorithms and their implementation in Python.
. Cluster analysis and anomaly detection using Python.
. Data Cube Technology.
. Future trends and research frontiers in data mining.
Who the book is for: This book is ideal for programmers seeking to implement data mining algorithms using Python and for students looking for a solid introduction to data mining.
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