eBook
Introduction to Data Science : Data Analysis and Prediction Algorithms with R
| Τίτλος: | Introduction to Data Science : Data Analysis and Prediction Algorithms with R |
|---|---|
| Περιγραφή: | 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. |
| Συγγραφείς: | Rafael A. Irizarry |
| Resource Type: | eBook. |
| Θέματα: | Data mining, Information visualization, R (Computer program language), Statistics--Data processing, Quantitative research, Computer algorithms, Probabilities--Data processing |
| Categories: | MATHEMATICS / Probability & Statistics / General, COMPUTERS / Machine Theory, COMPUTERS / Programming / General, COMPUTERS / System Administration / Linux & UNIX Administration, COMPUTERS / Data Science / Data Visualization |
| Βάση Δεδομένων: | eBook Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edsebk DbLabel: eBook Index An: 2160994 RelevancyScore: 950 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 949.708740234375 |
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| Items | – Name: Title Label: Title Group: Ti Data: Introduction to Data Science : Data Analysis and Prediction Algorithms with R – Name: Abstract Label: Description Group: Ab Data: 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rafael+A%2E+Irizarry%22">Rafael A. Irizarry</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Information+visualization%22">Information visualization</searchLink><br /><searchLink fieldCode="DE" term="%22R+%28Computer+program+language%29%22">R (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics--Data+processing%22">Statistics--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+algorithms%22">Computer algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Probabilities--Data+processing%22">Probabilities--Data processing</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+General%22">MATHEMATICS / Probability & Statistics / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Machine+Theory%22">COMPUTERS / Machine Theory</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Programming+%2F+General%22">COMPUTERS / Programming / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+System+Administration+%2F+Linux+%26+UNIX+Administration%22">COMPUTERS / System Administration / Linux & UNIX Administration</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Data+Visualization%22">COMPUTERS / Data Science / Data Visualization</searchLink> |
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| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 005.362 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Data mining Type: general – SubjectFull: Information visualization Type: general – SubjectFull: R (Computer program language) Type: general – SubjectFull: Statistics--Data processing Type: general – SubjectFull: Quantitative research Type: general – SubjectFull: Computer algorithms Type: general – SubjectFull: Probabilities--Data processing Type: general Titles: – TitleFull: Introduction to Data Science : Data Analysis and Prediction Algorithms with R Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rafael A. Irizarry – PersonEntity: Name: NameFull: Rafael A. Irizarry IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2020 – D: 03 M: 04 Type: profile Y: 2020 Identifiers: – Type: isbn-print Value: 9780367357986 – Type: isbn-print Value: 9780367357993 – Type: isbn-print Value: 9781032286600 – Type: isbn-electronic Value: 9781000707731 – Type: isbn-electronic Value: 9781000708035 – Type: isbn-electronic Value: 9780429341830 Titles: – TitleFull: Introduction to Data Science : Data Analysis and Prediction Algorithms with R Type: main |
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