Data Engineering and Data Science

Concepts and Applications

Kukatlapalli Pradeep Kumar, Aynur Unal, Vinay Jha Pillai, Hari Murthy, M. Niranjanamurthy

• Publisher: Wiley-Scrivener
• Year: 2023
• Print ISBN: 9781119841876
• eBook ISBN: 9781119841975
• Edition: 1
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

DATA ENGINEERING and DATA SCIENCE

Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the "one-stop shop" for the concepts and applications of data science and engineering for data scientists across many industries.

The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn't need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information.

In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.

Table of contents

• Cover
• Table of Contents
• Series Page
• Title Page
• Copyright Page
• Preface
• 1 Quality Assurance in Data Science: Need, Challenges and Focus
• 2 Design and Implementation of Social Media Mining - Knowledge Discovery Methods for Effective Digital Marketing Strategies
• 3 A Study on Big Data Engineering Using Cloud Data Warehouse
• 4 Data Mining with Cluster Analysis Through Partitioning Approach of Huge Transaction Data
• 5 Application of Data Science in Macromodeling of Nonlinear Dynamical Systems
• 6 Comparative Analysis of Various Ensemble Approaches for Web Page Classification
• 7 Feature Engineering and Selection Approach Over Malicious Image
• 8 Cubic-Regression and Likelihood Based Boosting GAM to Model Drug Sensitivity for Glioblastoma
• 9 Unobtrusive Engagement Detection through Semantic Pose Estimation and Lightweight ResNet for an Online Class Environment
• 10 Building Rule Base for Decision Making - A Fuzzy-Rough Approach
• 11 An Effective Machine Learning Approach to Model Healthcare Data
• 12 Recommendation Engine for Retail Domain Using Machine Learning Techniques
• 13 Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix
• 14 ML Algorithms and Their Approach on COVID-19 Data Analysis
• 15 Analysis and Design for the Early Stage Detection of Lung Diseases Using Machine Learning Algorithms
• 16 Estimation of Cancer Risk through Artificial Neural Network
• 17 Applications and Advancements in Data Science and Analytics
• About the Editors
• Index
• Also of Interest
• End User License Agreement