This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.
This book focuses on data mining for co-location pattern, a valid method for identifying patterns from all types of data and applying them in business intelligence and analytics. It explains the fundamentals of co-location pattern mining, co-location decision tree, and maximal instance co-location pattern mining for a step-by-step understanding.
If you want to learn how to analyze your data with R, this is your book. A broad range of real-world case studies highlights the breadth and depth of the R software. This expanded second edition delves deeper into topical explanations and updates and expands all case studies. Assuming no prior knowledge of R or data mining/statistical techniques
Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models.
This book guides the reader in the analysis of big-data by providing theoretical and practical instruments to tame the complexity of such systems. Together with support provided by the companion website, it constitutes a simple and useful handbook for data analysts.
Data Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications.This book is the ideal guide to using data science to drive insights.
Data Science in Practice is the ideal introduction to data science. With or without math skills: Here you get the all-round view that you need for your projects. This book describes how to properly question data, in order to unearth the treasure that data can be.