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Submit a ProposalData Science Handbook
 | A Practical Approach By Kolla Bhanu Prakash Series: Next-Generation Computing and Communication Engineering Copyright: 2022 | Status: Published ISBN: 9781119857334 | Hardcover | 463 pages Price: $195 USD  |
One Line Description
This desk reference handbook gives a hands-on experience on different
algorithms and popular techniques used in real-time in data
science to all researchers working in various domains.
Audience
The handbook will be used by graduate students up to research scholars in
computer science and electrical engineering as well as industry professionals in
a range of industries such as healthcare.
The handbook will be used by graduate students up to research scholars in
computer science and electrical engineering as well as industry professionals in
a range of industries such as healthcare.
Description
Data Science is one of the leading research-driven areas in the modern era. It
is having a critical role in healthcare, engineering, education, mechatronics,
and medical robotics. Building models and working with data is not valueneutral.
We choose the problems with which we work, make assumptions in
these models, and decide on metrics and algorithms for the problems. The data
scientist identifies the problem which can be solved with data and expert tools
of modeling and coding.
The book starts with introductory concepts in data science like data munging,
data preparation, and transforming data. Chapter 2 discusses data visualization,
drawing various plots and histograms. Chapter 3 covers mathematics and statistics
for data science. Chapter 4 mainly focuses on machine learning algorithms in
data science. Chapter 5 comprises of outlier analysis and DBSCAN algorithm.
Chapter 6 focuses on clustering. Chapter 7 discusses network analysis. Chapter
8 mainly focuses on regression and naive-bayes classifier. Chapter 9 covers
web-based data visualizations with Plotly. Chapter 10 discusses web scraping.
The book concludes with a section discussing 19 projects on various subjects in
data science.
Back to Top
Data Science is one of the leading research-driven areas in the modern era. It
is having a critical role in healthcare, engineering, education, mechatronics,
and medical robotics. Building models and working with data is not valueneutral.
We choose the problems with which we work, make assumptions in
these models, and decide on metrics and algorithms for the problems. The data
scientist identifies the problem which can be solved with data and expert tools
of modeling and coding.
The book starts with introductory concepts in data science like data munging,
data preparation, and transforming data. Chapter 2 discusses data visualization,
drawing various plots and histograms. Chapter 3 covers mathematics and statistics
for data science. Chapter 4 mainly focuses on machine learning algorithms in
data science. Chapter 5 comprises of outlier analysis and DBSCAN algorithm.
Chapter 6 focuses on clustering. Chapter 7 discusses network analysis. Chapter
8 mainly focuses on regression and naive-bayes classifier. Chapter 9 covers
web-based data visualizations with Plotly. Chapter 10 discusses web scraping.
The book concludes with a section discussing 19 projects on various subjects in
data science.
Back to Top
Author / Editor Details
Kolla Bhanu Prakash, PhD, is a Professor and Research Group Head for A.I. & Data Science Research group at K L University, India. He has published more
than 80 research papers in international and national journals and conferences,
as well as authored/edited 12 books and seven patents. His research interests
include deep learning, data science, and quantum computing.
www.
Kolla Bhanu Prakash, PhD, is a Professor and Research Group Head for A.I. & Data Science Research group at K L University, India. He has published more
than 80 research papers in international and national journals and conferences,
as well as authored/edited 12 books and seven patents. His research interests
include deep learning, data science, and quantum computing.
www.
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Table of Contents
Acknowledgment
Preface
1. Data Munging Basics
1 Introduction
1.1 Filtering and Selecting Data
1.2 Treating Missing Values
1.3 Removing Duplicates
1.4 Concatenating and Transforming Data
1.5 Grouping and Data Aggregation
References
2. Data Visualization
2.1 Creating Standard Plots (Line, Bar, Pie)
2.2 Defining Elements of a Plot
2.3 Plot Formatting
2.4 Creating Labels and Annotations
2.5 Creating Visualizations from Time Series Data
2.6 Constructing Histograms, Box Plots, and Scatter Plots
References
3. Basic Math and Statistics
3.1 Linear Algebra
3.2 Calculus
3.2.1 Differential Calculus
3.2.2 Integral Calculus
3.3 Inferential Statistics
3.3.1 Central Limit Theorem
3.3.2 Hypothesis Testing
3.3.3 ANOVA
3.3.4 Qualitative Data Analysis
3.4 Using NumPy to Perform Arithmetic Operations on Data
3.5 Generating Summary Statistics Using Pandas and Scipy
3.6 Summarizing Categorical Data Using Pandas
3.7 Starting with Parametric Methods in Pandas and Scipy
3.8 Delving Into Non-Parametric Methods Using Pandas and Scipy
3.9 Transforming Dataset Distributions
References
4. Introduction to Machine Learning
4.1 Introduction to Machine Learning
4.2 Types of Machine Learning Algorithms
4.3 Explanatory Factor Analysis
4.4 Principal Component Analysis (PCA)
References
5. Outlier Analysis
5.1 Extreme Value Analysis Using Univariate Methods
5.2 Multivariate Analysis for Outlier Detection
5.3 DBSCan Clustering to Identify Outliers
References
6. Cluster Analysis
6.1 K-Means Algorithm
6.2 Hierarchial Methods
6.3 Instance-Based Learning w/ k-Nearest Neighbor
References
7. Network Analysis with NetworkX
7.1 Working with Graph Objects
7.2 Simulating a Social Network (ie; Directed Network Analysis)
7.3 Analyzing a Social Network
References
8. Basic Algorithmic Learning
8.1 Linear Regression
8.2 Logistic Regression
8.3 Naive Bayes Classifiers
References
9. Web-Based Data Visualizations with Plotly
9.1 Collaborative Aanalytics
9.2 Basic Charts
9.3 Statistical Charts
9.4 Plotly Maps
References
10. Web Scraping with Beautiful Soup
10.1 The BeautifulSoup Object
10.2 Exploring NavigableString Objects
10.3 Data Parsing
10.4 Web Scraping
10.5 Ensemble Models with Random Forests
References
Data Science Projects
11. Covid19 Detection and Prediction
Bibliography
12. Leaf Disease Detection
Bibliography
13. Brain Tumor Detection with Data Science
Bibliography
14. Color Detection with Python
Bibliography
15. Detecting Parkinson’s Disease
Bibliography
16. Sentiment Analysis
Bibliography
17. Road Lane Line Detection
Bibliography
18. Fake News Detection
Bibliography
19. Speech Emotion Recognition
Bibliography
20. Gender and Age Detection with Data Science
Bibliography
21. Diabetic Retinopathy
Bibliography
22. Driver Drowsiness Detection in Python
Bibliography
23. Chatbot Using Python
Bibliography
24. Handwritten Digit Recognition Project
Bibliography
25. Image Caption Generator Project in Python
Bibliography
26. Credit Card Fraud Detection Project
Bibliography
27. Movie Recommendation System
Bibliography
28. Customer Segmentation
Bibliography
29. Breast Cancer Classification
Bibliography
30. Traffic Signs Recognition
Bibliography
Back to Top
Acknowledgment
Preface
1. Data Munging Basics
1 Introduction
1.1 Filtering and Selecting Data
1.2 Treating Missing Values
1.3 Removing Duplicates
1.4 Concatenating and Transforming Data
1.5 Grouping and Data Aggregation
References
2. Data Visualization
2.1 Creating Standard Plots (Line, Bar, Pie)
2.2 Defining Elements of a Plot
2.3 Plot Formatting
2.4 Creating Labels and Annotations
2.5 Creating Visualizations from Time Series Data
2.6 Constructing Histograms, Box Plots, and Scatter Plots
References
3. Basic Math and Statistics
3.1 Linear Algebra
3.2 Calculus
3.2.1 Differential Calculus
3.2.2 Integral Calculus
3.3 Inferential Statistics
3.3.1 Central Limit Theorem
3.3.2 Hypothesis Testing
3.3.3 ANOVA
3.3.4 Qualitative Data Analysis
3.4 Using NumPy to Perform Arithmetic Operations on Data
3.5 Generating Summary Statistics Using Pandas and Scipy
3.6 Summarizing Categorical Data Using Pandas
3.7 Starting with Parametric Methods in Pandas and Scipy
3.8 Delving Into Non-Parametric Methods Using Pandas and Scipy
3.9 Transforming Dataset Distributions
References
4. Introduction to Machine Learning
4.1 Introduction to Machine Learning
4.2 Types of Machine Learning Algorithms
4.3 Explanatory Factor Analysis
4.4 Principal Component Analysis (PCA)
References
5. Outlier Analysis
5.1 Extreme Value Analysis Using Univariate Methods
5.2 Multivariate Analysis for Outlier Detection
5.3 DBSCan Clustering to Identify Outliers
References
6. Cluster Analysis
6.1 K-Means Algorithm
6.2 Hierarchial Methods
6.3 Instance-Based Learning w/ k-Nearest Neighbor
References
7. Network Analysis with NetworkX
7.1 Working with Graph Objects
7.2 Simulating a Social Network (ie; Directed Network Analysis)
7.3 Analyzing a Social Network
References
8. Basic Algorithmic Learning
8.1 Linear Regression
8.2 Logistic Regression
8.3 Naive Bayes Classifiers
References
9. Web-Based Data Visualizations with Plotly
9.1 Collaborative Aanalytics
9.2 Basic Charts
9.3 Statistical Charts
9.4 Plotly Maps
References
10. Web Scraping with Beautiful Soup
10.1 The BeautifulSoup Object
10.2 Exploring NavigableString Objects
10.3 Data Parsing
10.4 Web Scraping
10.5 Ensemble Models with Random Forests
References
Data Science Projects
11. Covid19 Detection and Prediction
Bibliography
12. Leaf Disease Detection
Bibliography
13. Brain Tumor Detection with Data Science
Bibliography
14. Color Detection with Python
Bibliography
15. Detecting Parkinson’s Disease
Bibliography
16. Sentiment Analysis
Bibliography
17. Road Lane Line Detection
Bibliography
18. Fake News Detection
Bibliography
19. Speech Emotion Recognition
Bibliography
20. Gender and Age Detection with Data Science
Bibliography
21. Diabetic Retinopathy
Bibliography
22. Driver Drowsiness Detection in Python
Bibliography
23. Chatbot Using Python
Bibliography
24. Handwritten Digit Recognition Project
Bibliography
25. Image Caption Generator Project in Python
Bibliography
26. Credit Card Fraud Detection Project
Bibliography
27. Movie Recommendation System
Bibliography
28. Customer Segmentation
Bibliography
29. Breast Cancer Classification
Bibliography
30. Traffic Signs Recognition
Bibliography
Back to Top