Communication Principles For Data Science

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Communication Principles For Data Science

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Author : Changho Suh
language : en
Publisher:
Release Date : 2023

Communication Principles For Data Science written by Changho Suh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

This book introduces the basic principles underlying the design and analysis of the digital communication systems that have heralded the information revolution. One major goal of the book is to demonstrate the role of the digital communication principles in a wide variety of data science applications, including community detection, computational biology, speech recognition and machine learning. One defining feature of this book is to make an explicit connection between the communication principles and data science problems, as well as to succinctly deliver the "story" of how the communication principles play a role for trending data science applications. All the key "plots" involved in the story are coherently developed with the help of tightly coupled exercise problem sets, and the associated fundamentals are explored mostly from first principles. Another key feature is that it includes programming implementation of a variety of algorithms inspired by fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python and TensorFlow. This book does not follow a traditional book-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent storylines and themes. It serves as a textbook mainly for a junior- or senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in probability and random processes, and basic familiarity with Python. But the background can be supplemented by almost self-contained materials, as well as by numerous exercise problems intended for elaborating on non-trivial concepts. In addition, Part III for data science applications should provide motivation and insights to students and even professional scientists who are interested in the field.

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 Communications Principles

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Author : Richard D. Gitlin
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Data Communications Principles written by Richard D. Gitlin 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 2012-12-06 with Technology & Engineering categories.

This unique text, for both the first year graduate student and the newcomer to the field, provides in-depth coverage of the basic principles of data communications and covers material which is not treated in other texts, including phase and timing recovery and echo cancellation. Throughout the book, exercises and applications illustrate the material while up-to-date references round out the work.

Communication Principles For Data Science

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Author : Changho Suh
language : en
Publisher: Springer Nature
Release Date : 2023-06-12

Communication Principles For Data Science written by Changho Suh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-12 with Computers categories.

This book introduces the basic principles underlying the design and analysis of the digital communication systems that have heralded the information revolution. One major goal of the book is to demonstrate the role of the digital communication principles in a wide variety of data science applications, including community detection, computational biology, speech recognition and machine learning. One defining feature of this book is to make an explicit connection between the communication principles and data science problems, as well as to succinctly deliver the “story” of how the communication principles play a role for trending data science applications. All the key “plots” involved in the story are coherently developed with the help of tightly coupled exercise problem sets, and the associated fundamentals are explored mostly from first principles. Another key feature is that it includes programming implementation of a variety of algorithms inspired by fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python and TensorFlow. This book does not follow a traditional book-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent storylines and themes. It serves as a textbook mainly for a junior- or senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in probability and random processes, and basic familiarity with Python. But the background can be supplemented by almost self-contained materials, as well as by numerous exercise problems intended for elaborating on non-trivial concepts. In addition, Part III for data science applications should provide motivation and insights to students and even professional scientists who are interested in the field.

Communicating With Data

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Author : Deborah Nolan
language : en
Publisher: Oxford University Press
Release Date : 2021-03-25

Communicating With Data written by Deborah Nolan and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-25 with Science categories.

Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component. Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication.

Data Communications Principles

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Author : Richard D Gitlin
language : en
Publisher:
Release Date : 1992-09-30

Data Communications Principles written by Richard D Gitlin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992-09-30 with categories.

Data Communication Principles For Fixed And Wireless Networks

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Author : Ahmad
language : en
Publisher:
Release Date : 2008-10-01

Data Communication Principles For Fixed And Wireless Networks written by Ahmad and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-01 with categories.

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.

Data Communication Principles

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Author : Aftab Ahmad
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-08

Data Communication Principles written by Aftab Ahmad 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 2007-05-08 with Computers categories.

Data Communication Principles for Fixed and Wireless Networks focuses on the physical and data link layers. Included are examples that apply to a diversified range of higher level protocols such as TCP/IP, OSI and packet based wireless networks. Performance modeling is introduced for beginners requiring basic mathematics. Separate discussion has been included on wireless cellular networks performance and on the simulation of networks. Throughout the book, wireless LANS has been given the same level of treatment as fixed network protocols. It is assumed that readers would be familiar with basic mathematics and have some knowledge of binary number systems. Data Communication Principles for Fixed and Wireless Networks is for students at the senior undergraduate and first year graduate levels. It can also be used as a reference work for professionals working in the areas of data networks, computer networks and internet protocols.

Data Science

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Author : John D. Kelleher
language : en
Publisher: MIT Press
Release Date : 2018-04-13

Data Science written by John D. Kelleher and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-13 with Computers categories.

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.