30 Second Data Science
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30 Second Data Science
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Author : Liberty Vittert
language : en
Publisher:
Release Date : 2020-09-29
30 Second Data Science written by Liberty Vittert and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-29 with Computers categories.
30-Second Data Science covers 50 key elements in the field of data-gathering from basic to ethics, explained clearly and concisely.
30 Second Data Science
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Author : Liberty Vittert
language : en
Publisher:
Release Date : 2020-09-29
30 Second Data Science written by Liberty Vittert and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-29 with Computers categories.
30-Second Data Scienceis the quickest way to discover how data is a driving force not just in the big issues, such as climate change and healthcare, but in our daily lives. Data science is an entirely new discipline that encompasses a new era of information, from finding criminals to predicting epidemics. But there's more to it than the vast quantities of information gathered by our computers, smartphones, and credit cards. Carefully compiled by experts in the field,30-Second Data Science covers the basic statistical principles that drive the algorithms, how data affects us in every way--science, society, business, pleasure--along with the ethical quandaries and its future promise of a better world. Each 30-Second entry details a different facet of data science in just 300 words and one picture, showing how the concept of bringing together different types of data, and using powerful computer programs to find patterns no human eye could spot, is already transforming our world. Exploring key ideas and featuring biographies of the people behind them, 30-Second Data Science explains clearly and concisely all you need to know about data science, from basics to ethics. The 30 Second series presents concise, informative guides to the most important topics which shape the world around us, presenting terms which are key to understanding the subject in 30 seconds, 300 words, and one image.
R For Data Science
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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12
R For Data Science written by Hadley Wickham 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 2016-12-12 with Computers categories.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
30 Second Theories
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Author : Martin Rees
language : en
Publisher: Icon Books Ltd
Release Date : 2010-05-06
30 Second Theories written by Martin Rees and has been published by Icon Books Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-06 with Science categories.
When it comes to big science, very few things are conclusively known. From Quantum Mechanics to Natural Selection, what we have instead are theories - ideas explain why things happen the way they do. We don't know for certain these are correct - no one ever saw the Big Bang - but with them we can paint beautiful, breathtaking pictures of everything from human behaviour to what the future may hold. Profiling the key scientists behind each theory, "30-Second Theories" presents each entry in a unique, eye-catching full-colour design, with thought-provoking extras and stylish illustrations. It is essential for anyone keen on expanding their mind with science's most thrilling ideas.
Big Data Science In Finance
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Author : Irene Aldridge
language : en
Publisher: John Wiley & Sons
Release Date : 2021-01-08
Big Data Science In Finance written by Irene Aldridge 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 2021-01-08 with Computers categories.
Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.
Introduction To Data Science
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Author : Rafael A. Irizarry
language : en
Publisher: CRC Press
Release Date : 2019-11-12
Introduction To Data Science written by Rafael A. Irizarry and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with Mathematics categories.
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course.
Foundations Of Data Science
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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23
Foundations Of Data Science written by Avrim Blum 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 2020-01-23 with Computers categories.
Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.
Practical Data Science Cookbook
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Author : Prabhanjan Tattar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-06-29
Practical Data Science Cookbook written by Prabhanjan Tattar 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 2017-06-29 with Computers categories.
Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization
30 Second Ai Robotics
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Author : Luis de Miranda
language : en
Publisher: Ivy Press
Release Date : 2019-03-14
30 Second Ai Robotics written by Luis de Miranda and has been published by Ivy Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-14 with Technology & Engineering categories.
This comprehensive presentation of the core concepts and historical landmarks in robotics and artificial intelligence is a must-read for those who want to understand the important changes happening now in our everyday lives, in the workplace, and in our minds and bodies. What is deep in “deep learning”? Can artificial intelligence really think? What will robots really look like in the near future? Is there a new class divide between those who understand technology and those who fear it? A clear and exhaustive introduction for non-specialists, 30-Second AI & Robotics will help the reader to navigate the world of ubiquitous computers, smart cities, and collaborative robots. At last, an optimistic and friendly book about our human possibilities in the time of automata.
Data Science
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Author : Yang Wang
language : en
Publisher: Springer Nature
Release Date : 2022-08-10
Data Science written by Yang Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-10 with Computers categories.
This two volume set (CCIS 1628 and 1629) constitutes the refereed proceedings of the 8th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2022 held in Chengdu, China, in August, 2022. The 65 full papers and 26 short papers presented in these two volumes were carefully reviewed and selected from 261 submissions. The papers are organized in topical sections on: Big Data Mining and Knowledge Management; Machine Learning for Data Science; Multimedia Data Management and Analysis.