Data science : concepts and practice / Vijay Kotu, Bala Deshpande.

Format:

Book

Author/Creator:

Kotu, Vijay, author.

Deshpande, Balachandre, author.

Language:

English

Subjects (All):

Data mining.

Electronic data processing.

Physical Description:

1 online resource (570 pages)

Edition:

Second edition.

Place of Publication:

Cambridge, MA : Morgan Kaufmann Publishers, an imprint of Elsevier, [2019]

System Details:

text file

Summary:

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language Describes the commonly used setup options for the open source tool RapidMiner

Contents:

Front Cover

Data Science

Copyright Page

Dedication

Contents

Foreword

Preface

Why Data Science?

Why This Book?

Who Can Use This Book?

Acknowledgments

1 Introduction

1.1 AI, Machine learning, and Data Science

1.2 What is Data Science?

1.2.1 Extracting Meaningful Patterns

1.2.2 Building Representative Models

1.2.3 Combination of Statistics, Machine Learning, and Computing

1.2.4 Learning Algorithms

1.2.5 Associated Fields

1.3 Case for Data Science

1.3.1 Volume

1.3.2 Dimensions

1.3.3 Complex Questions

1.4 Data Science Classification

1.5 Data Science Algorithms

1.6 Roadmap for This Book

1.6.1 Getting Started With Data Science

1.6.2 Practice using RapidMiner

1.6.3 Core Algorithms

References

2 Data Science Process

2.1 Prior Knowledge

2.1.1 Objective

2.1.2 Subject Area

2.1.3 Data

2.1.4 Causation Versus Correlation

2.2 Data Preparation

2.2.1 Data Exploration

2.2.2 Data Quality

2.2.3 Missing Values

2.2.4 Data Types and Conversion

2.2.5 Transformation

2.2.6 Outliers

2.2.7 Feature Selection

2.2.8 Data Sampling

2.3 Modeling

2.3.1 Training and Testing Datasets

2.3.2 Learning Algorithms

2.3.3 Evaluation of the Model

2.3.4 Ensemble Modeling

2.4 Application

2.4.1 Production Readiness

2.4.2 Technical Integration

2.4.3 Response Time

2.4.4 Model Refresh

2.4.5 Assimilation

2.5 Knowledge

3 Data Exploration

3.1 Objectives of Data Exploration

3.2 Datasets

3.2.1 Types of Data

Numeric or Continuous

Categorical or Nominal

3.3 Descriptive Statistics

3.3.1 Univariate Exploration

Measure of Central Tendency

Measure of Spread

3.3.2 Multivariate Exploration

Central Data Point

Correlation

3.4 Data Visualization

3.4.1 Univariate Visualization

Histogram.

Quartile

Distribution Chart

3.4.2 Multivariate Visualization

Scatterplot

Scatter Multiple

Scatter Matrix

Bubble Chart

Density Chart

3.4.3 Visualizing High-Dimensional Data

Parallel Chart

Deviation Chart

Andrews Curves

3.5 Roadmap for Data Exploration

4 Classification

4.1 Decision Trees

4.1.1 How It Works

Step 1: Where to Split Data?

Step 2: When to Stop Splitting Data?

4.1.2 How to Implement

Implementation 1: To Play Golf or Not?

Implementation 2: Prospect Filtering

Step 1: Data Preparation

Step 2: Divide dataset Into Training and Testing Samples

Step 3: Modeling Operator and Parameters

Step 4: Configuring the Decision Tree Model

Step 5: Process Execution and Interpretation

4.1.3 Conclusion

4.2 Rule Induction

Approaches to Developing a Rule Set

4.2.1 How It Works

Step 1: Class Selection

Step 2: Rule Development

Step 3: Learn-One-Rule

Step 4: Next Rule

Step 5: Development of Rule Set

4.2.2 How to Implement

Step 2: Modeling Operator and Parameters

Step 3: Results Interpretation

Alternative Approach: Tree-to-Rules

4.2.3 Conclusion

4.3 k-Nearest Neighbors

4.3.1 How It Works

Measure of Proximity

Distance

Weights

Correlation similarity

Simple matching coefficient

Jaccard similarity

Cosine similarity

4.3.2 How to Implement

Step 3: Execution and Interpretation

4.3.3 Conclusion

4.4 Naïve Bayesian

4.4.1 How It Works

Step 1: Calculating Prior Probability P(Y)

Step 2: Calculating Class Conditional Probability P(Xi|Y)

Step 3: Predicting the Outcome Using Bayes' Theorem

Issue 1: Incomplete Training Set

Issue 2: Continuous Attributes

Issue 3: Attribute Independence.

4.4.2 How to Implement

Step 3: Evaluation

Step 4: Execution and Interpretation

4.4.3 Conclusion

4.5 Artificial Neural Networks

4.5.1 How It Works

Step 1: Determine the Topology and Activation Function

Step 2: Initiation

Step 3: Calculating Error

Step 4: Weight Adjustment

4.5.2 How to Implement

4.5.3 Conclusion

4.6 Support Vector Machines

Concept and Terminology

4.6.1 How It Works

4.6.2 How to Implement

Implementation 1: Linearly Separable Dataset

Step 3: Process Execution and Interpretation

Example 2: Linearly Non-Separable Dataset

Parameter Settings

4.6.3 Conclusion

4.7 Ensemble Learners

Wisdom of the Crowd

4.7.1 How It Works

Achieving the Conditions for Ensemble Modeling

4.7.2 How to Implement

Ensemble by Voting

Bootstrap Aggregating or Bagging

Implementation

Boosting

AdaBoost

Random Forest

4.7.3 Conclusion

5 Regression Methods

5.1 Linear Regression

5.1.1 How it Works

5.1.2 How to Implement

Step 2: Model Building

Step 4: Application to Unseen Test Data

5.1.3 Checkpoints

5.2 Logistic Regression

5.2.1 How It Works

How Does Logistic Regression Find the Sigmoid Curve?

A Simple but Tragic Example

5.2.2 How to Implement

Step 2: Modeling Operator and Parameters.

Step 3: Execution and Interpretation

Step 4: Using MetaCost

Step 5: Applying the Model to an Unseen Dataset

5.2.3 Summary Points

5.3 Conclusion

6 Association Analysis

6.1 Mining Association Rules

6.1.1 Itemsets

Support

Confidence

Lift

Conviction

6.1.2 Rule Generation

6.2 Apriori Algorithm

6.2.1 How it Works

Frequent Itemset Generation

Rule Generation

6.3 Frequent Pattern-Growth Algorithm

6.3.1 How it Works

6.3.2 How to Implement

Step 3: Create Association Rules

Step 4: Interpreting the Results

6.4 Conclusion

7 Clustering

Clustering to Describe the Data

Clustering for Preprocessing

Types of Clustering Techniques

7.1 k-Means Clustering

7.1.1 How It Works

Step 1: Initiate Centroids

Step 2: Assign Data Points

Step 3: Calculate New Centroids

Step 4: Repeat Assignment and Calculate New Centroids

Step 5: Termination

Special Cases

Evaluation of Clusters

7.1.2 How to Implement

Step 2: Clustering Operator and Parameters

7.2 DBSCAN Clustering

7.2.1 How It Works

Step 1: Defining Epsilon and MinPoints

Step 2: Classification of Data Points

Step 3: Clustering

Optimizing Parameters

Special Cases: Varying Densities

7.2.2 How to Implement

7.3 Self-Organizing Maps

7.3.1 How It Works

Step 1: Topology Specification

Step 2: Initialize Centroids

Step 3: Assignment of Data Objects

Step 4: Centroid Update

Step 6: Mapping a New Data Object.

7.3.2 How to Implement

Step 2: SOM Modeling Operator and Parameters

Visual Model

Location Coordinates

Conclusion

8 Model Evaluation

8.1 Confusion Matrix

8.2 ROC and AUC

8.3 Lift Curves

8.4 How to Implement

8.5 Conclusion

9 Text Mining

9.1 How It Works

9.1.1 Term Frequency-Inverse Document Frequency

9.1.2 Terminology

9.2 How to Implement

9.2.1 Implementation 1: Keyword Clustering

Step 1: Gather Unstructured Data

Step 2: Data Preparation

Step 3: Apply Clustering

9.2.2 Implementation 2: Predicting the Gender of Blog Authors

Step 3.1: Identify Key Features

Step 3.2: Build Models

Step 4.1: Prepare Test Data for Model Application

Step 4.2: Applying the Trained Models to Testing Data

Bias in Machine Learning

9.3 Conclusion

10 Deep Learning

10.1 The AI Winter

AI Winter: 1970's

Mid-Winter Thaw of the 1980s

The Spring and Summer of Artificial Intelligence: 2006-Today

10.2 How it works

10.2.1 Regression Models As Neural Networks

10.2.2 Gradient Descent

10.2.3 Need for Backpropagation

10.2.4 Classifying More Than 2 Classes: Softmax

10.2.5 Convolutional Neural Networks

10.2.6 Dense Layer

10.2.7 Dropout Layer

10.2.8 Recurrent Neural Networks

10.2.9 Autoencoders

10.2.10 Related AI Models

10.3 How to Implement

Handwritten Image Recognition

Step 1: Dataset Preparation

Step 2: Modeling using the Keras Model

Step 3: Applying the Keras Model

Step 4: Results

10.4 Conclusion

11 Recommendation Engines.

Why Do We Need Recommendation Engines?.

Notes:

Description based on print version record.

ISBN:

9780128147627

0128147628