Exploratory Data Science A Practical Guide For Engineering And Science Students
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Exploratory Data Science: A Practical Guide for Engineering and Science Students
Author: Pasquale De Marco
language: en
Publisher: Pasquale De Marco
Release Date: 2025-03-17
"Exploratory Data Science: A Practical Guide for Engineering and Science Students" is a comprehensive and engaging introduction to the world of data analysis, probability, and statistics. Designed for students in engineering, science, and related fields, this book provides a solid foundation in the essential concepts and techniques needed to extract meaningful insights from data. With a focus on practical applications and real-world examples, "Exploratory Data Science" takes readers on a journey through the art of data exploration, visualization, and statistical inference. Learn how to uncover patterns, trends, and relationships hidden within data using graphical representations, numerical summaries, and probability distributions. Master the art of hypothesis testing to make informed decisions in the face of uncertainty. Delve into the intricacies of correlation and regression analysis to uncover relationships between variables and make predictions based on observed patterns. Explore time series analysis and forecasting to unravel patterns and trends over time. Discover the power of design of experiments and analysis of variance to optimize processes and make informed decisions. Non-parametric statistics and Bayesian statistics are also covered, providing readers with the tools to analyze data that may not conform to traditional assumptions and to incorporate prior knowledge and uncertainty into statistical models. The book concludes with a thorough exploration of statistical computing and software, equipping readers with the practical skills necessary to harness the computational power of modern statistical software packages. Written in a clear and accessible style, "Exploratory Data Science" is packed with hands-on exercises, case studies, and thought-provoking questions to reinforce understanding and encourage critical thinking. Ethical considerations in data analysis are also emphasized, ensuring that statistical methods are applied responsibly and with integrity. Whether you are a student seeking a deeper understanding of data science or a professional looking to enhance your analytical skills, "Exploratory Data Science" is the ultimate guide to unlocking the transformative potential of data. Embark on a journey of discovery and empower yourself to make informed decisions, solve complex problems, and drive innovation in your field. If you like this book, write a review!
Handbook of Research on Data Science and Cybersecurity Innovations in Industry 4.0 Technologies
Disruptive innovations are now propelling Industry 4.0 (I4.0) and presenting new opportunities for value generation in all major industry segments. I4.0 technologies' innovations in cybersecurity and data science provide smart apps and services with accurate real-time monitoring and control. Through enhanced access to real-time information, it also aims to increase overall effectiveness, lower costs, and increase the efficiency of people, processes, and technology. The Handbook of Research on Data Science and Cybersecurity Innovations in Industry 4.0 Technologies discusses the technological foundations of cybersecurity and data science within the scope of the I4.0 landscape and details the existing cybersecurity and data science innovations with I4.0 applications, as well as state-of-the-art solutions with regard to both academic research and practical implementations. Covering key topics such as data science, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, computer scientists, scholars, researchers, academicians, practitioners, instructors, and students.
A Practical Guide to Data Engineering
"A Practical Guide to Machine Learning and AI: Part-I" is an essential resource for anyone looking to dive into the world of artificial intelligence and machine learning. Whether you're a complete beginner or have some experience in the field, this book will equip you with the fundamental knowledge and hands-on skills needed to harness the power of these transformative technologies. In this comprehensive guide, you'll embark on an engaging journey that starts with the basics of data engineering. You'll gain a solid understanding of big data, the key roles involved, and how to leverage the versatile Python programming language for data-centric tasks. From mastering Python data types and control structures to exploring powerful libraries like NumPy and Pandas, you'll build a strong foundation to tackle more advanced concepts. As you progress, the book delves into the realm of exploratory data analysis (EDA), where you'll learn techniques to clean, transform, and extract insights from your data. This sets the stage for the heart of the book - machine learning. You'll explore both supervised and unsupervised learning, diving deep into regression, classification, clustering, and dimensionality reduction algorithms. Along the way, you'll encounter real-world examples and hands-on exercises to reinforce your understanding and apply what you've learned. But this book goes beyond just the technical aspects. It also addresses the ethical considerations surrounding machine learning, ensuring you develop a well-rounded perspective on the responsible use of these powerful tools. Whether your goal is to jumpstart a career in data science, enhance your existing skills, or simply satisfy your curiosity about the latest advancements in AI, "A Practical Guide to Machine Learning and AI: Part-I" is your comprehensive companion. Prepare to embark on an enriching journey that will equip you with the knowledge and skills to navigate the exciting frontiers of artificial intelligence and machine learning.