Data Science In Practice
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Data Science In Practice
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Author : Tom Alby
language : en
Publisher: CRC Press
Release Date : 2023-09-22
Data Science In Practice written by Tom Alby and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-22 with Mathematics categories.
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. You will get to know the relevant analysis methods, and will be introduced to the programming language R, which is ideally suited for data analysis. Associated tools like notebooks that make data science programming easily accessible are included in this introduction. Because technology alone is not enough, this book also deals with problems in project implementation, illuminates various fields of application, and does not forget to address ethical aspects. Data Science in Practice includes many examples, notes on errors, decision-making aids, and other practical tips. This book is ideal as a complementary text for university students, and is a useful learning tool for those moving into more data-related roles. Key Features: Success factors and tools for all project phases Includes application examples for various subject areas Introduces many aspects of Data Science, from requirements analysis to data acquisition and visualization
Data Science
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Author : Vijay Kotu
language : en
Publisher: Morgan Kaufmann
Release Date : 2018-11-27
Data Science written by Vijay Kotu and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-27 with Computers categories.
Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You'll be able to: - Gain the necessary knowledge of different data science techniques to extract value from data. - Master the concepts and inner workings of 30 commonly used powerful data science algorithms. - Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... - Contains fully updated content on data science, including tactics on how to mine business data for information - Presents simple explanations for over twenty powerful data science techniques - Enables the practical use of data science algorithms without the need for programming - Demonstrates processes with practical use cases - Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language - Describes the commonly used setup options for the open source tool RapidMiner
Data Science In Practice
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Author : Alan Said
language : en
Publisher: Springer
Release Date : 2018-09-19
Data Science In Practice written by Alan Said and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-19 with Computers categories.
This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.
Fundamentals Of Data Science
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Author : Jugal K. Kalita
language : en
Publisher: Elsevier
Release Date : 2023-11-17
Fundamentals Of Data Science written by Jugal K. Kalita and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-17 with Mathematics categories.
Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining and analytics. Users will find the authors' research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data. The book's authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. In addition, a number of descriptive learning tasks are included. - Presents the foundational concepts of data science along with advanced concepts and real-life applications for applied learning - Includes coverage of a number of key topics such as data quality and pre-processing, proximity and validation, predictive data science, descriptive data science, ensemble learning, association rule mining, Big Data analytics, as well as incremental and distributed learning - Provides updates on key applications of data science techniques in areas such as Computational Biology, Network Intrusion Detection, Natural Language Processing, Software Clone Detection, Financial Data Analysis, and Scientific Time Series Data Analysis - Covers computer program code for implementing descriptive and predictive algorithms
Building An Effective Data Science Practice
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Author : Vineet Raina
language : en
Publisher: Apress
Release Date : 2021-12-09
Building An Effective Data Science Practice written by Vineet Raina and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-09 with Computers categories.
Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You’ll start by delving into the fundamentals of data science – classes of data science problems, data science techniques and their applications – and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practice provides a common base of reference knowledge and solutions, and addresses the kinds of challenges that arise to ensure your data science team is both productive and aligned with the business goals from the very start. Reinforced with real examples, this book allows you to confidently determine the strategic answers to effectively align your business goals with the operations of the data science practice. What You’ll Learn Transform business objectives into concrete problems that can be solved using data science Evaluate how problems and the specifics of a business drive the techniques and model evaluation guidelines used in a project Build and operate an effective interdisciplinary data science team within an organization Evaluating the progress of the team towards the business RoI Understand the important regulatory aspects that are applicable to a data science practice Who This Book Is For Technology leaders, data scientists, and project managers
Data Science
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Author : Crazy Science
language : en
Publisher:
Release Date : 2023-09-18
Data Science written by Crazy Science and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-18 with categories.
Are you ready to embark on a thrilling journey into the world of data science, where raw data is transformed into invaluable insights? Dive headfirst into "Data Science Concepts and Practice," a groundbreaking book that demystifies the complex world of data science and equips you with the skills you need to succeed in this data-driven era. ���� Discover the Secrets of Data Analysis: From data wrangling to visualization, you'll master the art of extracting actionable knowledge from vast datasets. ���� Embrace Machine Learning: Unleash the potential of artificial intelligence as you delve into algorithms, predictive modeling, and the endless possibilities of AI. ���� Harness Big Data: Learn to navigate the sea of information with ease, turning big data into a powerful asset for your business or projects. ���� Practical Applications: Real-world examples and hands-on exercises ensure you not only understand the concepts but can apply them effectively. ���� Uncover Insights: Dive deep into data mining techniques to unearth hidden patterns and trends that drive smart decision-making. ���� Join the Data Revolution: Position yourself as a sought-after data scientist with the knowledge and expertise needed in today's competitive job market. Whether you're a beginner looking to enter the world of data science or a seasoned professional seeking to expand your skillset, this comprehensive guide is your roadmap to success. Packed with easy-to-follow explanations, captivating examples, and expert tips, "Data Science Concepts and Practice" is your essential companion on the path to becoming a data science wizard. Don't miss out on this opportunity to transform your career and elevate your understanding of data science. Join the ranks of data pioneers who are shaping the future today. Your journey to data enlightenment starts here!
Trends Of Data Science And Applications
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Author : Siddharth Swarup Rautaray
language : en
Publisher: Springer Nature
Release Date : 2021-03-21
Trends Of Data Science And Applications written by Siddharth Swarup Rautaray and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-21 with Computers categories.
This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021. The book covers contributions ranging from theoretical and foundation research, platforms, methods, applications, and tools in all areas. It provides theory and practices in the area of data science, which add a social, geographical, and temporal dimension to data science research. It also includes application-oriented papers that prepare and use data in discovery research. This book contains chapters from academia as well as practitioners on big data technologies, artificial intelligence, machine learning, deep learning, data representation and visualization, business analytics, healthcare analytics, bioinformatics, etc. This book is helpful for the students, practitioners, researchers as well as industry professional.
Veridical Data Science
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Author : Bin Yu (Statistician)
language : en
Publisher:
Release Date : 2024
Veridical Data Science written by Bin Yu (Statistician) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Data mining categories.
"Veridical Data Science gives a complete and comprehensive overview of the world of data science using a problem/solution-oriented prospective"--
Big Data And Social Science
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Author : Ian Foster
language : en
Publisher: CRC Press
Release Date : 2020-11-17
Big Data And Social Science written by Ian Foster and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-17 with Mathematics categories.
Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations. Features: Takes an accessible, hands-on approach to handling new types of data in the social sciences Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes Illustrates social science and data science principles through real-world problems Links computer science concepts to practical social science research Promotes good scientific practice Provides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHub New to the Second Edition: Increased use of examples from different areas of social sciences New chapter on dealing with Bias and Fairness in Machine Learning models Expanded chapters focusing on Machine Learning and Text Analysis Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.
Research Methods In Learning Design And Technology
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Author : Enilda Romero-Hall
language : en
Publisher: Routledge
Release Date : 2020-10-19
Research Methods In Learning Design And Technology written by Enilda Romero-Hall and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-19 with Education categories.
Research Methods in Learning Design and Technology explores the many forms, both new and established, that research takes within the field of instructional design and technology (IDT). Chapters by experienced IDT researchers address methodologies such as meta-analysis, social media research, user experience design research, eye-tracking research, and phenomenology, situating each approach within the broader context of how IDT research has evolved and continues to evolve over time. This comprehensive, up-to-date volume familiarizes graduate students, faculty, and instructional design practitioners with the full spectrum of approaches available for investigating the new and changing educational landscapes. The book also discusses the history and prospective future of research methodologies in the IDT field.