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Titlebook: Introduction to Data Science; A Python Approach to Laura Igual,Santi Seguí Textbook 2024Latest edition The Editor(s) (if applicable) and Th

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發(fā)表于 2025-3-21 18:09:45 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Introduction to Data Science
副標(biāo)題A Python Approach to
編輯Laura Igual,Santi Seguí
視頻videohttp://file.papertrans.cn/474/473596/473596.mp4
概述Describes tools and techniques that demystify data science.Discusses Python extensions, techniques and modules to perform statistical analysis and machine learning.Includes case studies, and supplies
叢書名稱Undergraduate Topics in Computer Science
圖書封面Titlebook: Introduction to Data Science; A Python Approach to Laura Igual,Santi Seguí Textbook 2024Latest edition The Editor(s) (if applicable) and Th
描述.This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts?from statistics, machine/deep?learning?and responsible data science,?useful techniques for?network?analysis and?natural language processing,?and practical applications?of data science such as recommender systems or sentiment analysis.?.Topics and features:.?.Provides numerous practical case studies using real-world data throughout the book?.Supports understanding through hands-on experience of solving data science problems using Python?.Describes?concepts,?techniques and tools for statistical analysis, machine learning, graph analysis,?natural language processing, deep learning and?responsible data science.Reviews a range of applications of data science, including recommender systems and sentiment analysis of text data?.Provides supplementary code resources and data at an associated website?.This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical discipli
出版日期Textbook 2024Latest edition
關(guān)鍵詞Data Science; Parallel Computing; Python Programming; Statistical Inference; Graph Analysis
版次2
doihttps://doi.org/10.1007/978-3-031-48956-3
isbn_softcover978-3-031-48955-6
isbn_ebook978-3-031-48956-3Series ISSN 1863-7310 Series E-ISSN 2197-1781
issn_series 1863-7310
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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1863-7310 is and machine learning.Includes case studies, and supplies .This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts?from statistics, machine/deep?learning?and responsible dat
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,Introduction to?Data Science,, enabling their widespread use and the fast dissemination of results. This book has been designed as a small contribution to this democratization process by showing that anyone interested in this topic can become a junior data scientist in a few weeks.
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Recommender Systems,amily to travel to? These are typical questions that companies like Netflix or Amazon include in their products. We also see and discuss how recommender systems should be evaluated. Finally, a practical case with MovieLens dataset is presented.
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Supervised Learning,tion will then allow us to develop a strategy for model selection. Finally, two of the best-known techniques in machine learning are introduced: support vector machines and random forests. These are then applied to the proposed problem of predicting those loans that will not be successfully covered once they have been accepted.
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Network Analysis,t and answering a set of questions. For instance: Which is the most representative member of the network in terms of the most “connected”, the most “circulated”, the “closest” or the most “accessible” to the rest of the members?
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