Principles Of Data Science
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Principles Of Data Science
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Author : Sinan Ozdemir
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-12-16
Principles Of Data Science written by Sinan Ozdemir 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 2016-12-16 with Computers categories.
Learn the techniques and math you need to start making sense of your data About This Book Enhance your knowledge of coding with data science theory for practical insight into data science and analysis More than just a math class, learn how to perform real-world data science tasks with R and Python Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn Get to know the five most important steps of data science Use your data intelligently and learn how to handle it with care Bridge the gap between mathematics and programming Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results Build and evaluate baseline machine learning models Explore the most effective metrics to determine the success of your machine learning models Create data visualizations that communicate actionable insights Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples, and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.
Principles Of Data Science
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Author : Ambrish Kumar Sharma
language : en
Publisher: AG PUBLISHING HOUSE (AGPH Books)
Release Date : 2022-10-19
Principles Of Data Science written by Ambrish Kumar Sharma and has been published by AG PUBLISHING HOUSE (AGPH Books) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-19 with Study Aids categories.
With the advent of the "big data" era, the necessity for secure data storage has risen. To solve the problem of data storage, the main emphasis was on building a framework. The key ingredient is data science. Data Science is an interdisciplinary field that applies statistical methods, computer science, and other disciplines to raw data to conclude the world. Data is a crucial part of every business since it provides the information upon which wise business choices may be made. To deal with the growing volume of data, the interdisciplinary subject of data science emerged. It employs rigorous methods, protocols, algorithms, and frameworks from the scientific community to mine vast stores of data for useful information. Both structured and unstructured information may be extracted. To understand and analyse real-world events using data, a field known as "data science" has emerged to bring together concepts, data analysis, Machine Learning, and related methodologies. Data science is a term for a wide range of subfields within the study of data analysis. Data Science is a broad discipline that draws upon many disciplines' theories, practices, and tools, including but not limited to statistics, information science, mathematics, and computer science. Data scientists use many different methods, such as machine learning, data visualization, pattern recognition, probability modelling, signal processing, data engineering, etc
Principles Of Data Science Third Edition
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Author : SINAN. OZDEMIR
language : en
Publisher:
Release Date : 2024-01-31
Principles Of Data Science Third Edition written by SINAN. OZDEMIR and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-31 with Computers categories.
Principles of Data Science provides an end-to-end framework for cultivating critical thinking about data, performing practical data science, building performant machine learning models, and mitigating bias in AI pipelines.
Data Science For Undergraduates
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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2018-10-11
Data Science For Undergraduates written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-11 with Education categories.
Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.
Principles Of Data Science With Python
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Author : Arash Karimpour
language : en
Publisher:
Release Date : 2020-07-31
Principles Of Data Science With Python written by Arash Karimpour and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-31 with categories.
97 Things About Ethics Everyone In Data Science Should Know
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Author : Bill Franks
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-08-06
97 Things About Ethics Everyone In Data Science Should Know written by Bill Franks and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-06 with Computers categories.
Most of the high-profile cases of real or perceived unethical activity in data science arenâ??t matters of bad intent. Rather, they occur because the ethics simply arenâ??t thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult. In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices. Articles include: Ethics Is Not a Binary Conceptâ??Tim Wilson How to Approach Ethical Transparencyâ??Rado Kotorov Unbiased ≠ Fairâ??Doug Hague Rules and Rationalityâ??Christof Wolf Brenner The Truth About AI Biasâ??Cassie Kozyrkov Cautionary Ethics Talesâ??Sherrill Hayes Fairness in the Age of Algorithmsâ??Anna Jacobson The Ethical Data Storytellerâ??Brent Dykes Introducing Ethicizeâ?¢, the Fully AI-Driven Cloud-Based Ethics Solution!â??Brian Oâ??Neill Be Careful with "Decisions of the Heart"â??Hugh Watson Understanding Passive Versus Proactive Ethicsâ??Bill Schmarzo
Principles Of Data Science
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Author : Hamid R. Arabnia
language : en
Publisher: Springer Nature
Release Date : 2020-07-08
Principles Of Data Science written by Hamid R. Arabnia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-08 with Technology & Engineering categories.
This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice
Data Science From Scratch
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Author : Joel Grus
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-04-12
Data Science From Scratch written by Joel Grus and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-12 with Computers categories.
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.
The Data Science Path
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Author : Pasquale De Marco
language : en
Publisher: Pasquale De Marco
Release Date : 2025-07-14
The Data Science Path 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 2025-07-14 with Computers categories.
In the rapidly evolving world of data, "The Data Science Path" emerges as an invaluable guide for aspiring data scientists, business professionals, and anyone seeking to harness the power of data. This comprehensive book provides a structured and accessible roadmap to understanding the fundamentals of data science, empowering you to transform raw data into actionable insights. Embark on a journey through the chapters of this book, and you will: * Gain a solid foundation in the principles and methodologies that underpin data science, enabling you to approach data-driven problems with confidence. * Master the art of data wrangling, learning how to effectively organize, clean, and prepare data for analysis, ensuring its integrity and reliability. * Explore a wide range of data visualization techniques, gaining the ability to communicate complex data insights clearly and persuasively, making them accessible to both technical and non-technical audiences. * Delve into the realm of machine learning algorithms, both supervised and unsupervised, discovering how to build predictive models and extract meaningful patterns from data, unlocking its hidden potential. * Learn about the latest advancements and best practices in data science, staying at the forefront of this dynamic field and gaining the skills necessary to thrive in the data-driven economy. With its comprehensive coverage of essential topics, coupled with real-world examples, hands-on exercises, and thought-provoking case studies, "The Data Science Path" is your indispensable companion on the journey to becoming a proficient data scientist. Its clear and engaging writing style makes complex concepts approachable, empowering you to unlock the full potential of data and drive informed decision-making in a world increasingly shaped by information. If you like this book, write a review!
Applied Data Science
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Author : Martin Braschler
language : en
Publisher: Springer
Release Date : 2019-06-13
Applied Data Science written by Martin Braschler and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-13 with Computers categories.
This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science:first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.