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Browsing Học liệu điện tử (Electronic learning materials) by Topic "Khoa Khoa học ứng dụng"

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Now showing 1 - 9 of 9
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    Algorithms For Big Data
    (World Scientific, 2020) Feldman, Moran
    This unique volume is an introduction for computer scientists, including a formal study of theoretical algorithms for Big Data applications, which allows them to work on such algorithms in the future. It also serves as a useful reference guide for the general computer science population, providing a comprehensive overview of the fascinating world of such algorithms.To achieve these goals, the algorithmic results presented have been carefully chosen so that they demonstrate the important techniques and tools used in Big Data algorithms, and yet do not require tedious calculations or a very deep mathematical background.
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    Data And Society
    (World Scientific, 2022) Beynon-Davies, Paul
    Most literature thinks of the relationship between data and society as additive, meaning that data and society are seen as two separate sets of things but which overlap to form an intersection. The literature then goes off to unpack the intersection of the two circles and partners the term data in this manner with terms descriptive of the domain of society — ownership, control, surveillance, and privacy, to name but a few.Within this book, we want to promote an alternative viewpoint of the relationship between data and society. Rather than explaining how data fits with or contributes to some burning societal issues, we want to explain how data is constitutive of many such issues. The term constitutive is used here in the sense of data having power to institute, establish, or enact society.
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    Data Mining : Practical Machine Learning Tools and Techniques
    (Morgan Kaufmann, 2017) Witten, Ian H.; Frank, Eibe; Hall, Mark A.; Pal, Christopher J.
    Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning.
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    R Visualizations : Derive Meaning From Data
    (Chapman and Hall/CRC, 2020) Gerbing, David W.
    R Visualizations: Derive Meaning from Data focuses on one of the two major topics of data analytics: data visualization, a.k.a., computer graphics. In the book, major R systems for visualization are discussed, organized by topic and not by system. Anyone doing data analysis will be shown how to use R to generate any of the basic visualizations with the R visualization systems. Further, this book introduces the author's lessR system, which always can accomplish a visualization with less coding than the use of other systems, sometimes dramatically so, and also provides accompanying statistical analyses.Key Features Presents thorough coverage of the leading R visualization system, ggplot2. Gives specific guidance on using base R graphics to attain visualizations of the same quality as those provided by ggplot2. Shows how to create a wide range of data visualizations: distributions of categorical and continuous variables, many types of scatterplots including with a third variable, time series, and maps. Inclusion of the various approaches to R graphics organized by topic instead of by system.
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    Recommender Systems : The Textbook
    (Springer, 2016) Aggarwal, Charu C.
    This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation.
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    Recommender Systems Handbook
    (Springer, 2011) Ricci, Francesco; Rokach, Lior; Shapira, Bracha; Kantor, Paul B.
    The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments.
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    Secure Data Science : Integrating Cyber Security and Data Science
    (CRC Press, 2022) Thuraisingham, Bhavani; Kantarcioglu, Murat; Khan, Latifur
    Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy.
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    SQL for Data Scientists : A Beginner's Guide for Building Datasets for Analysis
    (Wiley, 2021) Teate, Renee M. P.
    Jump-start your career as a data scientist—learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that's dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data.
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    The Book of R : A First Course in Programming and Statistics
    (2016) Davies, Tilman M.
    The Book of R is a comprehensive, beginner-friendly guide to R, the world's most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you'll find everything you need to begin using R effectively for statistical analysis.You'll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You'll even learn how to create impressive data visualizations with R's basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package.