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
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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 written by Pasquale De Marco and has been published by Pasquale De Marco this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-17 with Science categories.
"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
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Author : Murugan, Thangavel
language : en
Publisher: IGI Global
Release Date : 2023-09-21
Handbook Of Research On Data Science And Cybersecurity Innovations In Industry 4 0 Technologies written by Murugan, Thangavel and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-21 with Computers categories.
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
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Author : Pedram Ariel Rostami
language : en
Publisher: Starseed AI
Release Date :
A Practical Guide To Data Engineering written by Pedram Ariel Rostami and has been published by Starseed AI this book supported file pdf, txt, epub, kindle and other format this book has been release on with Education categories.
"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.
Higher Education Handbook Of Theory And Research
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Author : Michael B. Paulsen
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-02-12
Higher Education Handbook Of Theory And Research written by Michael B. Paulsen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-12 with Education categories.
Published annually since 1985, the Handbook series provides a compendium of thorough and integrative literature reviews on a diverse array of topics of interest to the higher education scholarly and policy communities. Each chapter provides a comprehensive review of research findings on a selected topic, critiques the research literature in terms of its conceptual and methodological rigor, and sets forth an agenda for future research intended to advance knowledge on the chosen topic. The Handbook focuses on a comprehensive set of central areas of study in higher education that encompasses the salient dimensions of scholarly and policy inquiries undertaken in the international higher education community. Each annual volume contains chapters on such diverse topics as research on college students and faculty, organization and administration, curriculum and instruction, policy, diversity issues, economics and finance, history and philosophy, community colleges, advances in research methodology, and more. The series is fortunate to have attracted annual contributions from distinguished scholars throughout the world.
Data Guided Healthcare Decision Making
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Author : Ramalingam Shanmugam
language : en
Publisher: Cambridge University Press
Release Date : 2023-06-22
Data Guided Healthcare Decision Making written by Ramalingam Shanmugam and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-22 with Medical categories.
How does data evidence matter in decision-making in healthcare? How do you implement and maintain cost effective healthcare operations? Do decision trees help to sharpen decision making? This book will answer these questions, demystifying the many questions by clearly showing how to analyse data and how to interpret the results – vital skills for anyone who will go on to work in health administration in hospitals, clinics, pharmaceutical or insurance industries. Written by an expert in health and medical informatics, this book introduces readers to the fundamentals of operational decision making by illustrating the ideas and tools to reach optimal healthcare, drawing on numerous healthcare data sets from multiple sources. Aimed at an audience of graduate students and lecturers in Healthcare Administration and Business Administration courses and heavily illustrated throughout, this book includes up-to-date concepts, new methodologies and interpretations using widely available software: Excel, Microsoft Mathematics, MathSolver and JASP.
Data Science
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Author : Zhiwen Yu
language : en
Publisher: Springer Nature
Release Date : 2023-09-14
Data Science written by Zhiwen Yu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-14 with Computers categories.
This two-volume set (CCIS 1879 and 1880) constitutes the refereed proceedings of the 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023 held in Harbin, China, during September 22–24, 2023. The 52 full papers and 14 short papers presented in these two volumes were carefully reviewed and selected from 244 submissions. The papers are organized in the following topical sections: Part I: Applications of Data Science, Big Data Management and Applications, Big Data Mining and Knowledge Management, Data Visualization, Data-driven Security, Infrastructure for Data Science, Machine Learning for Data Science and Multimedia Data Management and Analysis. Part II: Data-driven Healthcare, Data-driven Smart City/Planet, Social Media and Recommendation Systems and Education using big data, intelligent computing or data mining, etc.
Mastering Health Data Science Using R
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Author : Alice Paul
language : en
Publisher: CRC Press
Release Date : 2025-07-22
Mastering Health Data Science Using R written by Alice Paul and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-22 with Medical categories.
This book provides a practical, application-driven guide to using R for public health and health data science, accessible to both beginners and those with some coding experience. Each module starts with data as the driver of analysis before introducing and breaking down the programming concepts needed to tackle the analysis in a step-by-step manner. This book aims to equip readers by offering a practical and approachable programming guide tailored to those in health-related fields. Going beyond simple R examples, the programming principles and skills developed will give readers the ability to apply R skills to their own research needs. Practical case studies in public health are provided throughout to reinforce learning. Topics include data structures in R, exploratory analysis, distributions, hypothesis testing, regression analysis, and larger scale programming with functions and control flows. The presentation focuses on implementation with R and assumes readers have had an introduction to probability, statistical inference and regression analysis. Key features: · Includes practical case studies. · Explains how to write larger programmes. · Contains additional information on Quarto. Alice Paul is an Assistant Professor of Biostatistics and Teaching Scholar, holding a Ph.D. in Operations Research from Cornell University. With six years of teaching experience at the undergraduate, master’s, and Ph.D. levels, she instructed students in diverse fields, including biostatistics, engineering, computer science, and data science at both Brown University and Olin College of Engineering.
Getting Started With Duckdb
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Author : Simon Aubury
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-06-24
Getting Started With Duckdb written by Simon Aubury and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-24 with Computers categories.
Analyze and transform data efficiently with DuckDB, a versatile, modern, in-process SQL database Key Features Use DuckDB to rapidly load, transform, and query data across a range of sources and formats Gain practical experience using SQL, Python, and R to effectively analyze data Learn how open source tools and cloud services in the broader data ecosystem complement DuckDB’s versatile capabilities Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDuckDB is a fast in-process analytical database. Getting Started with DuckDB offers a practical overview of its usage. You'll learn to load, transform, and query various data formats, including CSV, JSON, and Parquet. The book covers DuckDB's optimizations, SQL enhancements, and extensions for specialized applications. Working with examples in SQL, Python, and R, you'll explore analyzing public datasets and discover tools enhancing DuckDB workflows. This guide suits both experienced and new data practitioners, quickly equipping you to apply DuckDB's capabilities in analytical projects. You'll gain proficiency in using DuckDB for diverse tasks, enabling effective integration into your data workflows.What you will learn Understand the properties and applications of a columnar in-process database Use SQL to load, transform, and query a range of data formats Discover DuckDB's rich extensions and learn how to apply them Use nested data types to model semi-structured data and extract and model JSON data Integrate DuckDB into your Python and R analytical workflows Effectively leverage DuckDB's convenient SQL enhancements Explore the wider ecosystem and pathways for building DuckDB-powered data applications Who this book is for If you’re interested in expanding your analytical toolkit, this book is for you. It will be particularly valuable for data analysts wanting to rapidly explore and query complex data, data and software engineers looking for a lean and versatile data processing tool, along with data scientists needing a scalable data manipulation library that integrates seamlessly with Python and R. You will get the most from this book if you have some familiarity with SQL and foundational database concepts, as well as exposure to a programming language such as Python or R.
The Digital Transformation Of Product Formulation
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Author : Alix Schmidt
language : en
Publisher: CRC Press
Release Date : 2024-08-14
The Digital Transformation Of Product Formulation written by Alix Schmidt and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-14 with Technology & Engineering categories.
In competitive manufacturing industries, organizations embrace product development as a continuous investment strategy since both market share and profit margin stand to benefit. Formulating new or improved products has traditionally involved lengthy and expensive experimentation in laboratory or pilot plant settings. However, recent advancements in areas from data acquisition to analytics are synergizing to transform workflows and increase the pace of research and innovation. The Digital Transformation of Product Formulation offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. In this book, you will read a variety of industrial, academic, and consulting perspectives on how to go about transforming your materials product design from a twentieth-century art to a twenty-first-century science. Presents a futuristic vision for digitally enabled product development, the role of data and predictive modeling, and how to avoid project pitfalls to maximize probability of success Discusses data-driven materials design issues and solutions applicable to a variety of industries, including chemicals, polymers, pharmaceuticals, oil and gas, and food and beverages Addresses common characteristics of experimental datasets, challenges in using this data for predictive modeling, and effective strategies for enhancing a dataset with advanced formulation information and ingredient characterization Covers a wide variety of approaches to developing predictive models on formulation data, including multivariate analysis and machine learning methods Discusses formulation optimization and inverse design as natural extensions to predictive modeling for materials discovery and manufacturing design space definition Features case studies and special topics, including AI-guided retrosynthesis, real-time statistical process monitoring, developing multivariate specifications regions for raw material quality properties, and enabling a digital-savvy and analytics-literate workforce This book provides students and professionals from engineering and science disciplines with practical know-how in data-driven product development in the context of chemical products across the entire modeling lifecycle.
Making Sense Of Data Ii
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Author : Glenn J. Myatt
language : en
Publisher: John Wiley & Sons
Release Date : 2009-03-04
Making Sense Of Data Ii written by Glenn J. Myatt and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-03-04 with Mathematics categories.
A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences. Following a comprehensive introduction that details how to define a problem, perform an analysis, and deploy the results, Making Sense of Data II addresses the following key techniques for advanced data analysis: Data Visualization reviews principles and methods for understanding and communicating data through the use of visualization including single variables, the relationship between two or more variables, groupings in data, and dynamic approaches to interacting with data through graphical user interfaces. Clustering outlines common approaches to clustering data sets and provides detailed explanations of methods for determining the distance between observations and procedures for clustering observations. Agglomerative hierarchical clustering, partitioned-based clustering, and fuzzy clustering are also discussed. Predictive Analytics presents a discussion on how to build and assess models, along with a series of predictive analytics that can be used in a variety of situations including principal component analysis, multiple linear regression, discriminate analysis, logistic regression, and Naïve Bayes. Applications demonstrates the current uses of data mining across a wide range of industries and features case studies that illustrate the related applications in real-world scenarios. Each method is discussed within the context of a data mining process including defining the problem and deploying the results, and readers are provided with guidance on when and how each method should be used. The related Web site for the series (www.makingsenseofdata.com) provides a hands-on data analysis and data mining experience. Readers wishing to gain more practical experience will benefit from the tutorial section of the book in conjunction with the TraceisTM software, which is freely available online. With its comprehensive collection of advanced data mining methods coupled with tutorials for applications in a range of fields, Making Sense of Data II is an indispensable book for courses on data analysis and data mining at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who are interested in learning how to accomplish effective decision making from data and understanding if data analysis and data mining methods could help their organization.