Introduction To Data Science – Amit Kumar Das

It leads to increase in computational complexity (high time and space complexity) and also increase in complexity in the model trained with that data leading to overfitting. This phrase was coined by Richard E. Belman, an American mathematician who introduced concept of dynamic programming in 1953. 6.3.2 Key Drivers of Feature Selection – Feature Relevance and Redundancy Feature Relevance: In the case of supervised models, the input dataset is labelled.

A model is inducted based on the training data (which is a part of the input labeled data) – so that the inducted model can assign class labels to new, unlabeled data. Each of the predictors is expected to contribute information to decide the value of the class label. In case a predictor is contributing very less or no information, it is said to be irrelevant.

In unsupervised models, input dataset is not labeled. Grouping of similar data instances are done and similarity of data instances are evaluated based on value of different features. Certain features contribute little or no useful information for deciding the similarity of data instances. Hence, those features make no significant information contribution in the grouping process. These features are marked as irrelevant in context of the unsupervised learning task. To put a perspective, we can think of a simple example of the student dataset that we discussed in the beginning of this section.

Roll number of a student doesn’t contribute any significant information in predicting what the weight of a student would be. Similarly, if we are trying to group together students with similar academic capabilities, roll number can really not contribute any information whatsoever. So, in context of the supervised task of predicting student weight or the unsupervised task of grouping students with similar academic merit, the variable roll number is quite irrelevant. Any feature which is irrelevant is a candidate for rejection when we are selecting a subset of features.

Feature Redundancy: A feature may contribute information which is similar to the information contributed by one or more other features.

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OceanofPDF.com INTRODUCTION TO DATA SCIENCE Amit Kumar Das Mousoomi Bora Vishal Dhure OceanofPDF.com TABLE OF CONTENTS Foreword Preface Acknowledgements About the Authors Model Syllabus for Introduction to Data Science 1. Introduction Introduction Objective 1.1 What is Data Science 1.2 Why Data Science – Benefits and Uses 1.2.1 Use Cases of Data Science 1.3 Evolution of Data Science 1.3.1 History of Data Science 1.4 Data Science Process 1.5 Data Mining 1.5.1 Types of Data Mining 1.6 Data Warehousing 1.6.1 Characteristics of Data Warehouse 1.6.2 Data Warehouse Architecture 1.6.3 Why Data Warehousing?

1.7 Data Science Roles 1.7.1 Data Strategist 1.7.2 Data Architect 1.7.3 Data Scientist 1.7.4 Data Engineer 1.7.5 Data Analyst 1.7.6 Business Intelligence (BI) Analyst 1.7.7 ML Ops Engineer 1.7.8 Data Product Manager 1.8 Applications of Data Science 1.9 Data Privacy and Security 1.9.1 The Laws Governing Data Privacy 1.9.2 How Data Privacy and Security is Maintained in the Field of Data Science? 1.10 Ethical Considerations in Data Science 1.10.1 Ethical Principles in Data Science 1.10.2 Best Practices for Ethical Data Science Summary Multiple-choice Questions Short-answer Type Questions (2 marks each) Subjective Questions (5 marks questions) Long-Answer Type Questions (10 Marks Each) 2.

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