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Applied User Data Collection and Analysis in pdf


Download this PDF book: Applied User Data Collection and Analysis Using JavaScript and PHP 1st Edition by Kyle Goslin, Markus Hofmann

Applied User Data Collection and Analysis Using JavaScript and PHP is designed to provide the technical skills and competency to gather a wide range of user data from web applications in both active and passive methods. 

This is done by providing the reader with real-world examples of how a variety of different JavaScript- and PHP-based libraries can be used to gather data using custom feedback forms and embedded data gathering tools.

Once data has been gathered, this book explores the process of working with numerical data, text analysis, visualization approaches, statistics, and rolling out developed applications to both data analysts and users alike.

Using the collected data, this book aims to provide a deeper understanding of user behavior and interests, allowing application developers to further enhance web-application development.

Key Features:

Complete real-world examples of gathering data from users and web environments

Offers readers the fundamentals of text analysis using JavaScript and PHP

Allows the user to understand and harness JavaScript data-visualization tools

Integration of new and existing data sources into a single, bespoke web-based analysis environment

Understanding a user’s needs and requirements is one of the main driving forces for any business. Collected data provides insights into how users feel about a given feature or topic and how they work on a day to day basis. 

This data can be gathered directly from users by providing simple questions and allowing the user to select an option from a predefined list, or to enter their own text-based responses. Although asking a user for their opinion is possible, other methods can be used to gather data. Data can be collected indirectly through a web application by monitoring the interactions and behaviors a user has with it. 

The collected data can be processed to generate meaningful statistics and visualizations, providing both overviews and a deep-dive analysis of user interests and needs. Each different data type, such as numerical data and text data, comes with methods of identifying importance and relevance. 

Both quantitative and qualitative analysis approaches can be used to extract these insights. In this book, we explore the different methods available for data collection, extraction, and analysis. This book aims to describe the many different approaches that can be taken with the aim to capture as much data as possible for analysis. 

To provide a complete solution, the process of developing and deploying the developed solutions on a cloud-based server is explored with additional features such as report generation and delivery. 

An emphasis in this book is placed on the diversity of technologies exposing you to many different alternatives for data collection and analysis, providing you with the tools needed to develop a well-rounded solution.

About the Author

Dr. Kyle Goslin is currently a Lecturer in Computing at the Technological University Dublin in Ireland, specializing in web application development, information retrieval, text analysis and data visualization. 

Kyle has taught for over 10 years at third level in Ireland, teaching a wide range of web development related subjects. During this time, he has been involved in several different web-based data driven start-up companies with the aim of reducing time to market for businesses.

Kyle has contributed to several different open-source learning platforms with the aim of making education accessible to all learners by aiding both teachers and students. Kyle has developed and defended a number of different third level computing courses validated by Quality and Qualifications Ireland. 

He has published peer-reviewed articles relating to information retrieval, text analysis and learning environments. In his spare time, he is a technical reviewer for data and software development related books. He holds a Bachelor of Science (Honours) and Doctor of Philosophy from the Technological University Dublin, where he currently lectures and lives. For more information

Dr. Markus Hofmann is currently Senior Lecturer at the Technological University Dublin in Ireland where he focuses on the areas of data mining, text mining, data exploration and visualization as well as business intelligence. 

He holds a Ph.D. from Trinity College Dublin, an MSc in Computing (Information Technology for Strategic Management) from the Dublin Institute of Technology and a BA in Information Management Systems. He has taught extensively at undergraduate and postgraduate level in the fields of Data Mining, Information Retrieval, Text/Web Mining, Data Mining Applications, Data Pre-processing and Exploration and Databases.

Dr. Hofmann published widely at national as well as international level and specialized in recent years in the areas of Data/Text Mining, learning object creation, and virtual learning environments. Further he has strong connections to the Business Intelligence and Predictive Analytics sector both on an academic as well as industry level.

Dr. Hofmann has worked as technology expert together with over 30 different organizations in recent years including companies such as Intel, RapidMiner and many successful start-ups. 

Most of his involvement was on the innovation side of technology services and products where his contributions had significant impact on the success of such projects.

He is a member of the Register of Expert Panellists of Quality and Qualifications Ireland, external examiner for three third level institutes and a specialist in undergraduate and postgraduate course development. He has been internal as well as external examiner of postgraduate thesis submissions. 

He was also general, local and technical chair of national and international conferences. Dr. Hofmann is the editor two data science books published by Chapman & Hall.


Chapter 1 Introduction

Chapter 2 Active Data Collection

Chapter 3 Passive Data Collection

Chapter 4 Custom Dashboards

Chapter 5 Working with Text

Chapter 6 Text Visualization

Chapter 7 Time-Based Feedback Analysis

Chapter 8 Feedback Statistics and Overviews

Chapter 9 Hosting, Reporting, and Distribution

About the book:

Publisher ‏ : ‎ Routledge; 1st edition (April 28, 2021)

Language ‏ : ‎ English

Pages ‏ : ‎ 346 

File : PDF, 14MB


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