Fundamentals of Data Science Theory and Practice 1st Edition by Jugal K Kalita, Dhruba K Bhattacharyya, Swarup Roy ISBN 9780323917780 032391778X

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