Fundamentals of Data Science: Theory and Practice 9780323972635, 9780323917780

Table of contents :
Cover image
Title page
Table of Contents
Copyright
Dedication
Preface
Acknowledgment
Foreword
Foreword
1: Introduction
Abstract
1.1. Data, information, and knowledge
1.2. Data Science: the art of data exploration
1.3. What is not Data Science?
1.4. Data Science tasks
1.5. Data Science objectives
1.6. Applications of Data Science
1.7. How to read the book?
References
2: Data, sources, and generation
Abstract
2.1. Introduction
2.2. Data attributes
2.3. Data-storage formats
2.4. Data sources
2.5. Data generation
2.6. Summary
References
3: Data preparation
Abstract
3.1. Introduction
3.2. Data cleaning
3.3. Data reduction
3.4. Data transformation
3.5. Data normalization
3.6. Data integration
3.7. Summary
References
4: Machine learning
Abstract
4.1. Introduction
4.2. Machine Learning paradigms
4.3. Inductive bias
4.4. Evaluating a classifier
4.5. Summary
References
5: Regression
Abstract
5.1. Introduction
5.2. Regression
5.3. Evaluating linear regression
5.4. Multidimensional linear regression
5.5. Polynomial regression
5.6. Overfitting in regression
5.7. Reducing overfitting in regression: regularization
5.8. Other approaches to regression
5.9. Summary
References
6: Classification
Abstract
6.1. Introduction
6.2. Nearest-neighbor classifiers
6.3. Decision trees
6.4. Support-Vector Machines (SVM)
6.5. Incremental classification
6.6. Summary
References
7: Artificial neural networks
Abstract
7.1. Introduction
7.2. From biological to artificial neuron
7.3. Multilayer perceptron
7.4. Learning by backpropagation
7.5. Loss functions
7.6. Activation functions
7.7. Deep neural networks
7.8. Summary
References
8: Feature selection
Abstract
8.1. Introduction
8.2. Steps in feature selection
8.3. Principal-component analysis for feature reduction
References
9: Cluster analysis
Abstract
9.1. Introduction
9.2. What is cluster analysis?
9.3. Proximity measures
9.4. Exclusive clustering techniques
9.5. High-dimensional data clustering
9.6. Biclustering
9.7. Cluster-validity measures
9.8. Summary
References
10: Ensemble learning
Abstract
10.1. Introduction
10.2. Ensemble-learning framework
10.3. Supervised ensemble learning
10.4. Unsupervised ensemble learning
10.5. Semisupervised ensemble learning
10.6. Issues and challenges
10.7. Summary
References
11: Association-rule mining
Abstract
Acknowledgement
11.1. Introduction
11.2. Association analysis: basic concepts
11.3. Frequent itemset-mining algorithms
11.4. Association mining in quantitative data
11.5. Correlation mining
11.6. Distributed and parallel association mining
11.7. Summary
References
12: Big Data analysis
Abstract
12.1. Introduction
12.2. Characteristics of Big Data
12.3. Types of Big Data
12.4. Big Data analysis problems
12.5. Big Data analytics techniques
12.6. Big Data analytics platforms
12.7. Big Data analytics architecture
12.8. Tools and systems for Big Data analytics
12.9. Active challenges
12.10. Summary
References
13: Data Science in practice
Abstract
13.1. Need of Data Science in the real world
13.2. Hands-on Data Science with Python
13.3. Dataset preprocessing
13.4. Feature selection and normalization
13.5. Classification
13.6. Clustering
13.7. Summary
References
14: Conclusion
Abstract
Index