Kooperativer Bibliotheksverbund

UID:

Format: XIX, 575 p. 162 illus., 156 illus. in color. , online resource.

Edition: 1st ed. 2023.

ISBN: 9783031133398

Content: In recent years, large amounts of data became available in all areas of science, industry and society. This provides unprecedented opportunities for enhancing our knowledge, and to solve scientific and societal problems. In order to emphasize the importance of this, data have been called the "oil of the 21st Century". Unfortunately, data do usually not reveal information easily, but analysis methods are required to extract it. This is the main task of data science. The textbook provides students with tools they need to analyze complex data using methods from machine learning, artificial intelligence and statistics. These are the main fields comprised by data science. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. This allows the immediate practical application of the learning concepts side-by-side. The book advocates an integration of statistical thinking, computational thinking and mathematical thinking because data science is an interdisciplinary field requiring an understanding of statistics, computer science and mathematics. Furthermore, the book highlights the understanding of the domain knowledge about experiments or processes that generate or produce the data. The goal of the authors is to provide students with a systematic approach to data science that allows a continuation of the learning process beyond the presented topics. Hence, the book enables learning to learn. Main features of the book: - emphasizing the understanding of methods and underlying concepts - integrating statistical thinking, computational thinking and mathematical thinking - highlighting the understanding of the data - exploring the power of visualizations - balancing theoretical and practical presentations - demonstrating the application of methods using R - providing detailed examples and discussions - presenting data science as a complex network Elements of Data Science, Machine Learning and Artificial Intelligence using R presents basic, intermediate and advanced methods for learning from data, culminating into a practical toolbox for a modern data scientist. The comprehensive coverage allows a wide range of usages of the textbook from (advanced) undergraduate to graduate courses. .

Note: Introduction -- Introduction to learning from data -- Part 1: General topics -- Prediction models -- Error measures -- Resampling -- Data types -- Part 2: Core methods -- Maximum Likelihood & Bayesian analysis -- Clustering -- Dimension Reduction -- Classification -- Hypothesis testing -- Linear Regression -- Model Selection -- Part 3: Advanced topics -- Regularization -- Deep neural networks -- Multiple hypothesis testing -- Survival analysis -- Generalization error -- Theoretical foundations -- Conclusion.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031133381

Additional Edition: Printed edition: ISBN 9783031133404

Additional Edition: Printed edition: ISBN 9783031133411

Language: English

DOI: 10.1007/978-3-031-13339-8

URL: https://doi.org/10.1007/978-3-031-13339-8

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XV, 414 Seiten : , Illustrationen, Diagramme ; , 24 cm x 17 cm.

ISBN: 978-3-11-056467-9 , 3-11-056467-X

Series Statement: De Gruyter Oldenbourg STEM

Additional Edition: Erscheint auch als Online-Ausgabe, PDF ISBN 978-3-11-056499-0

Additional Edition: Erscheint auch als Online-Ausgabe, EPUB ISBN 978-3-11-056502-7

Language: English

Subjects: Computer Science

Keywords: Data Science ; Mathematik ; R ; Data Mining ; Mathematik ; R ; Lehrbuch ; Lehrbuch

URL: http://www.degruyter.com/search?f_0=isbnissn&q_0=9783110564679&searchTitles=true

URL: http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031582132&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA

URL: http://www.degruyter.com/search?f_0=isbnissn&q_0=9783110564679&searchTitles=true

URL: http://www.degruyter.com/search?f_0=isbnissn&q_0=9783110564679&searchTitles=true

URL: Inhaltsverzeichnis

Author information: Moutari, Salissou.

Author information: Dehmer, Matthias, 1968-

Author information: Emmert-Streib, Frank.

UID:

Format: 1 Online-Ressource (XV, 414 Seiten) , Illustrationen, Diagramme

Edition: 1. Auflage

ISBN: 9783110564990 , 9783110565027

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-11-056467-9

Language: English

Subjects: Computer Science

Keywords: Data Mining ; Mathematik ; R ; Data Science ; Mathematik ; R ; Lehrbuch

DOI: 10.1515/9783110564990

URL: Volltext  (URL des Erstveröffentlichers)

Author information: Dehmer, Matthias 1968-

Author information: Moutari, Salissou

Author information: Emmert-Streib, Frank

UID:

Format: XV, 408 Seiten , Illustrationen, Diagramme , 24 cm x 17 cm

Edition: 2nd edition

ISBN: 9783110795882 , 3110795884

Series Statement: De Gruyter STEM

Note: Literaturverzeichnis: Seiten 397-404 , Erscheint auch als Online-Ausgabe 9783110796179 (ISBN) , Erscheint auch als Online-Ausgabe 9783110796063 (ISBN)

Language: English

Keywords: Data Science ; R 〈Programm〉 ; Lehrbuch

URL: https://www.degruyter.com/isbn/9783110795882

Author information: Emmert-Streib, Frank

Author information: Dehmer, Matthias

UID:

Format: 1 Online-Ressource (XV, 408 Seiten) : , Illustrationen, Diagramme (überwiegend farbig).

Edition: 2nd edition

ISBN: 978-3-11-079606-3 , 978-3-11-079617-9

Series Statement: De Gruyter STEM

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-11-079588-2

Language: English

Subjects: Computer Science

Keywords: Data Mining ; Mathematik ; R ; Data Science ; Mathematik ; R ; Lehrbuch

DOI: 10.15375/9783110796063

URL: Volltext  (URL des Erstveröffentlichers)

Author information: Dehmer, Matthias, 1968-

Author information: Moutari, Salissou

Author information: Emmert-Streib, Frank.

UID:

Format: 1 online resource (582 pages)

Edition: First edition.

ISBN: 3-031-13339-0

Note: Intro -- Preface -- Contents -- 1 Introduction to Learning from Data -- 1.1 What Is Data Science? -- 1.2 Converting Data into Knowledge -- 1.2.1 Big Aims: Big Questions -- 1.2.2 Generating Insights by Visualization -- 1.3 Structure of the Book -- 1.3.1 Part I -- 1.3.2 Part II -- 1.3.3 Part III -- 1.4 Our Motivation for Writing This Book -- 1.5 How to Use This Book -- 1.6 Summary -- Part I General Topics -- 2 General Prediction Models -- 2.1 Introduction -- 2.2 Categorization of Methods -- 2.2.1 Properties of the Data -- 2.2.2 Properties of the Optimization Algorithm -- 2.2.3 Properties of the Model -- 2.2.4 Summary -- 2.3 Overview of Prediction Models -- 2.4 Causal Model versus Predictive Model -- 2.5 Explainable AI -- 2.6 Fundamental Statistical Characteristics of Prediction Models -- 2.6.1 Example -- 2.7 Summary -- 2.8 Exercises -- 3 General Error Measures -- 3.1 Introduction -- 3.2 Motivation -- 3.3 Fundamental Error Measures -- 3.4 Error Measures -- 3.4.1 True-Positive Rate and True-Negative Rate -- 3.4.2 Positive Predictive Value and Negative Predictive Value -- 3.4.3 Accuracy -- 3.4.4 F-Score -- 3.4.5 False Discovery Rate and False Omission Rate -- 3.4.6 False-Negative Rate and False-Positive Rate -- 3.4.7 Matthews Correlation Coefficient -- 3.4.8 Cohen's Kappa -- 3.4.9 Normalized Mutual Information -- 3.4.10 Area Under the Receiver Operator Characteristic Curve -- 3.5 Evaluation of Outcome -- 3.5.1 Evaluation of an Individual Method -- 3.5.2 Comparing Multiple Binary Decision-Making Methods -- 3.6 Summary -- 3.7 Exercises -- 4 Resampling Methods -- 4.1 Introduction -- 4.2 Resampling Methods for Error Estimation -- 4.2.1 Holdout Set -- 4.2.2 Leave-One-Out CV -- 4.2.3 K-Fold Cross-Validation -- 4.3 Extended Resampling Methods for Error Estimation -- 4.3.1 Repeated Holdout Set -- 4.3.2 Repeated K-Fold CV -- 4.3.3 Stratified K-Fold CV. , 4.4 Bootstrap -- 4.4.1 Resampling With versus Resampling Without Replacement -- 4.5 Subsampling -- 4.6 Different Types of Prediction Data Sets -- 4.7 Sampling from a Distribution -- 4.8 Standard Error -- 4.9 Summary -- 4.10 Exercises -- 5 Data -- 5.1 Introduction -- 5.2 Data Types -- 5.2.1 Genomic Data -- 5.2.2 Network Data -- 5.2.3 Text Data -- 5.2.4 Time-to-Event Data -- 5.2.5 Business Data -- 5.3 Summary -- Part II Core Methods -- 6 Statistical Inference -- 6.1 Exploratory Data Analysis and Descriptive Statistics -- 6.1.1 Data Structure -- 6.1.2 Data Preprocessing -- 6.1.3 Summary Statistics and Presentation of Information -- 6.1.4 Measures of Location -- 6.1.4.1 Sample Mean -- 6.1.4.2 Trimmed Sample Mean -- 6.1.4.3 Sample Median -- 6.1.4.4 Quartile -- 6.1.4.5 Percentile -- 6.1.4.6 Mode -- 6.1.4.7 Proportion -- 6.1.5 Measures of Scale -- 6.1.5.1 Sample Variance -- 6.1.5.2 Range -- 6.1.5.3 Interquartile Range -- 6.1.6 Measures of Shape -- 6.1.6.1 Skewness -- 6.1.6.2 Kurtosis -- 6.1.7 Data Transformation -- 6.1.8 Example: Summary of Data and EDA -- 6.2 Sample Estimators -- 6.2.1 Point Estimation -- 6.2.2 Unbiased Estimators -- 6.2.3 Biased Estimators -- 6.2.4 Sufficiency -- 6.3 Bayesian Inference -- 6.3.1 Conjugate Priors -- 6.3.2 Continuous Parameter Estimation -- 6.3.2.1 Example: Continuous Bayesian Inference Using R -- 6.3.3 Discrete Parameter Estimation -- 6.3.4 Bayesian Credible Intervals -- 6.3.5 Prediction -- 6.3.6 Model Selection -- 6.4 Maximum Likelihood Estimation -- 6.4.1 Asymptotic Confidence Intervals for MLE -- 6.4.2 Bootstrap Confidence Intervals for MLE -- 6.4.3 Meaning of Confidence Intervals -- 6.5 Expectation-Maximization Algorithm -- 6.5.1 Example: EM Algorithm -- 6.6 Summary -- 6.7 Exercises -- 7 Clustering -- 7.1 Introduction -- 7.2 What Is Clustering? -- 7.3 Comparison of Data Points -- 7.3.1 Distance Measures. , 7.3.2 Similarity Measures -- 7.4 Basic Principle of Clustering Algorithms -- 7.5 Non-hierarchical Clustering Methods -- 7.5.1 K-Means Clustering -- 7.5.2 K-Medoids Clustering -- 7.5.3 Partitioning Around Medoids (PAM) -- 7.6 Hierarchical Clustering -- 7.6.1 Dendrograms -- 7.6.2 Two Types of Dissimilarity Measures -- 7.6.3 Linkage Functions for Agglomerative Clustering -- 7.6.4 Example -- 7.7 Defining Feature Vectors for General Objects -- 7.8 Cluster Validation -- 7.8.1 External Criteria -- 7.8.2 Assessing the Numerical Values of Indices -- 7.8.3 Internal Criteria -- 7.9 Summary -- 7.10 Exercises -- 8 Dimension Reduction -- 8.1 Introduction -- 8.2 Feature Extraction -- 8.2.1 An Overview of PCA -- 8.2.2 Geometrical Interpretation of PCA -- 8.2.3 PCA Procedure -- 8.2.4 Underlying Mathematical Problems in PCA -- 8.2.5 PCA Using Singular Value Decomposition -- 8.2.6 Assessing PCA Results -- 8.2.7 Illustration of PCA Using R -- 8.2.8 Kernel PCA -- 8.2.9 Discussion -- 8.2.10 Non-negative Matrix Factorization -- 8.2.10.1 NNMF Using the Frobenius Norm as Objective Function -- 8.2.10.2 NNMF Using the Generalized Kullback-Leibler Divergence as Objective Function -- 8.2.10.3 Example of NNMF Using R -- 8.3 Feature Selection -- 8.3.1 Filter Methods Using Mutual Information -- 8.4 Summary -- 8.5 Exercises -- 9 Classification -- 9.1 Introduction -- 9.2 What Is Classification? -- 9.3 Common Aspects of Classification Methods -- 9.3.1 Basic Idea of a Classifier -- 9.3.2 Training and Test Data -- 9.3.3 Error Measures -- 9.3.3.1 Error Measures for Multi-class Classification -- 9.4 Naive Bayes Classifier -- 9.4.1 Educational Example -- 9.4.2 Example -- 9.5 Linear Discriminant Analysis -- 9.5.1 Extensions -- 9.6 Logistic Regression -- 9.7 k-Nearest Neighbor Classifier -- 9.8 Support Vector Machine -- 9.8.1 Linearly Separable Data -- 9.8.2 Nonlinearly Separable Data. , 9.8.3 Nonlinear Support Vector Machines -- 9.8.4 Examples -- 9.9 Decision Tree -- 9.9.1 What Is a Decision Tree? -- 9.9.1.1 Three Principal Steps to Get a Decision Tree -- 9.9.2 Step 1: Growing a Decision Tree -- 9.9.3 Step 2: Assessing the Size of a Decision Tree -- 9.9.3.1 Intuitive Approach -- 9.9.3.2 Formal Approach -- 9.9.4 Step 3: Pruning a Decision Tree -- 9.9.4.1 Alternative Way to Construct Optimal Decision Trees: Stopping Rules -- 9.9.5 Predictions -- 9.10 Summary -- 9.11 Exercises -- 10 Hypothesis Testing -- 10.1 Introduction -- 10.2 What Is Hypothesis Testing? -- 10.3 Key Components of Hypothesis Testing -- 10.3.1 Step 1: Select Test Statistic -- 10.3.2 Step 2: Null Hypothesis H0 and AlternativeHypothesis H1 -- 10.3.3 Step 3: Sampling Distribution -- 10.3.3.1 Examples -- 10.3.4 Step 4: Significance Level α -- 10.3.5 Step 5: Evaluate the Test Statistic from Data -- 10.3.6 Step 6: Determine the p-Value -- 10.3.7 Step 7: Make a Decision about the Null Hypothesis -- 10.4 Type 2 Error and Power -- 10.4.1 Connections between Power and Errors -- 10.5 Confidence Intervals -- 10.5.1 Confidence Intervals for a Population Mean with Known Variance -- 10.5.2 Confidence Intervals for a Population Mean with Unknown Variance -- 10.5.3 Bootstrap Confidence Intervals -- 10.6 Important Hypothesis Tests -- 10.6.1 Student's t-Test -- 10.6.1.1 One-Sample t-Test -- 10.6.1.2 Two-Sample t-Test -- 10.6.1.3 Extensions -- 10.6.2 Correlation Tests -- 10.6.3 Hypergeometric Test -- 10.6.3.1 Null Hypothesis and Sampling Distribution -- 10.6.3.2 Examples -- 10.6.4 Finding the Correct Hypothesis Test -- 10.7 Permutation Tests -- 10.8 Understanding versus Applying Hypothesis Tests -- 10.9 Historical Notes and Misinterpretations -- 10.10 Summary -- 10.11 Exercises -- 11 Linear Regression Models -- 11.1 Introduction -- 11.1.1 What Is Linear Regression?. , 11.1.2 Motivating Example -- 11.2 Simple Linear Regression -- 11.2.1 Ordinary Least Squares Estimation of Coefficients -- 11.2.2 Variability of the Coefficients -- 11.2.3 Testing the Necessity of Coefficients -- 11.2.4 Assessing the Quality of a Fit -- 11.3 Preprocessing -- 11.4 Multiple Linear Regression -- 11.4.1 Testing the Necessity of Coefficients -- 11.4.2 Assessing the Quality of a Fit -- 11.5 Diagnosing Linear Models -- 11.5.1 Error Assumptions -- 11.5.2 Linearity Assumption of the Model -- 11.5.3 Leverage Points -- 11.5.4 Outliers -- 11.5.5 Collinearity -- 11.5.6 Discussion -- 11.6 Advanced Topics -- 11.6.1 Interactions -- 11.6.2 Nonlinearities -- 11.6.3 Categorical Predictors -- 11.6.4 Generalized Linear Models -- 11.6.4.1 How to Determine Which Family to Use When Fitting a GLM -- 11.6.4.2 Advantages of GLMs over Traditional OLS Regression -- 11.6.4.3 Example: Poisson Regression -- 11.6.4.4 Example: Logistic Regression -- 11.7 Summary -- 11.8 Exercises -- 12 Model Selection -- 12.1 Introduction -- 12.2 Difference Between Model Selection and Model Assessment -- 12.3 General Approach to Model Selection -- 12.4 Model Selection for Multiple Linear Regression Models -- 12.4.1 R2 and Adjusted R2 -- 12.4.2 Mallow's Cp Statistic -- 12.4.3 Akaike's Information Criterion (AIC) and Schwarz's BIC -- 12.4.4 Best Subset Selection -- 12.4.5 Stepwise Selection -- 12.4.5.1 Forward Stepwise Selection -- 12.4.5.2 Backward Stepwise Selection -- 12.5 Model Selection for Generalized Linear Models -- 12.5.1 Negative Binomial Regression Model -- 12.5.2 Zero-Inflated Poisson Model -- 12.5.3 Quasi-Poisson Model -- 12.5.4 Comparison of GLMs -- 12.6 Model Selection for Bayesian Models -- 12.7 Nonparametric Model Selection for General Models with Resampling -- 12.8 Summary -- 12.9 Exercises -- Part III Advanced Topics -- 13 Regularization -- 13.1 Introduction. , 13.2 Preliminaries.

Additional Edition: ISBN 9783031133381

Language: English

UID:

Format: 1 online resource (XVI, 408 p.)

ISBN: 9783110796063

Series Statement: De Gruyter STEM

Content: The aim of the book is to help students become data scientists. Since this requires a series of courses over a considerable period of time, the book intends to accompany students from the beginning to an advanced understanding of the knowledge and skills that define a modern data scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data science.

Note: Frontmatter -- , Preface to the second edition -- , Contents -- , 1 Introduction -- , Part I: Introduction to R -- , 2 Overview of programming paradigms -- , 3 Setting up and installing the R program -- , 4 Installation of R packages -- , 5 Introduction to programming in R -- , 6 Creating R packages -- , Part II: Graphics in R -- , 7 Basic plotting functions -- , 8 Advanced plotting functions: ggplot2 -- , 9 Visualization of networks -- , Part III: Mathematical basics of data science -- , 10 Mathematics as a language for science -- , 11 Computability and complexity -- , 12 Linear algebra -- , 13 Analysis -- , 14 Differential equations -- , 15 Dynamical systems -- , 16 Graph theory and network analysis -- , 17 Probability theory -- , 18 Optimization -- , Bibliography -- , Index , Issued also in print. , In English.

Additional Edition: ISBN 9783110796179

Additional Edition: ISBN 9783110795882

Language: English

DOI: 10.1515/9783110796063

URL: https://doi.org/10.1515/9783110796063

URL: https://www.degruyter.com/isbn/9783110796063

URL: https://doi.org/10.1515/9783110796063

URL: https://www.degruyter.com/isbn/9783110796063