Business Analytics: A Managerial and Applied Approach presents analytics as a decision discipline for managers, MBA learners, and working professionals. The book develops a practical framework for converting data into measurable business value through problem framing, data foundations, visualization, statistical thinking, predictive modeling, forecasting, optimization, experimentation, and responsible implementation. It is structured to support both end-to-end learning and practical managerial use in areas such as customer analytics, operations, finance, and risk.

Abstract

Business Analytics equips managers, MBA learners, and working professionals with a decision-first framework for converting data into measurable business value. Rather than presenting analytics as a purely technical discipline, this book integrates strategy, execution, and quantitative reasoning to help readers frame problems, select appropriate methods, interpret results responsibly, and communicate insights that drive action. It begins with a managerial overview of analytics, including its scope, organizational value, decision contexts, and common failure modes, and then establishes the foundations required for trustworthy analysis, such as data sources, structures, quality, preparation, sampling, governance, privacy, and responsible use. The text develops competency across the analytics spectrum through descriptive analytics, KPI design, exploratory analysis, dashboard principles, segmentation, and executive storytelling. It introduces probability, uncertainty, confidence intervals, and hypothesis testing in a managerial and interpretive way, helping readers understand evidence, quantify risk, and avoid common errors in interpretation. Predictive modeling chapters explain workflow, feature reasoning, validation, and evaluation, covering regression, logistic regression, and tree-based methods with business-linked metrics such as lift, calibration, AUC, and cost-sensitive measures. Forecasting and time series are connected to planning decisions in demand, inventory, and workforce. Prescriptive analytics extends insights into action through optimization, simulation, and scenario planning. The book also addresses experimentation, causal inference, customer and revenue analytics, operations and supply chain analytics, financial and risk analytics, and concludes with an implementation playbook covering responsible AI, governance, MLOps, change management, ROI, and OKRs for sustainable organizational impact.

Keywords

business analytics, decision-making, data foundations, descriptive analytics, data visualization, statistical thinking, predictive modeling, time series forecasting, optimization, experimentation, causal inference, responsible AI, analytics governance, MLOps, ROI, OKRs

What This Book Helps Readers Do

• Frame business challenges as analytics problems with clear objectives, constraints, stakeholders, and success measures
• Understand descriptive, diagnostic, predictive, and prescriptive analytics and when each is appropriate
• Build strong foundations in data definitions, data quality, preparation, governance, privacy, and ethical use
• Communicate insights through dashboards, visualization, and decision narratives
• Interpret uncertainty using probability and statistical thinking
• Evaluate predictive models using business-relevant measures such as lift, calibration, AUC, cost, and risk
• Move from prediction to action using optimization, simulation, and scenario planning
• Design and interpret experiments and causal approaches credibly and safely
• Apply analytics across marketing and revenue, operations and supply chain, finance and risk
• Understand implementation realities including operating models, tool choices, governance, MLOps, adoption, ROI, and OKRs

Intended Audience

This book is designed for managers, MBA students, and working professionals who want a practical, decision-first understanding of analytics. It is also useful to faculty, trainers, and organizational leaders seeking a structured framework for evidence-based decision-making and analytics capability building.

Table of Contents

Front Matter

• Foreword
• Acknowledgements
• Abstract
• Preface
• How to Use This Book
• About the Author
• Note to Readers

Chapter 1. Business Analytics: Managerial Overview

• What is Business Analytics? Scope and Value
• Analytics in the MBA Context: Decisions, Strategy, and Execution
• Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
• Analytics Lifecycle: Problem Framing to Deployment
• Analytic Thinking: Hypotheses, Causality, and Trade-offs
• Common Pitfalls: Biases, Misinterpretation, and Overfitting
• Managerial Toolkit: Questions to ask before approving an analytics initiative
• Chapter Summary
• Key Terms
• Review Questions (MBA level)
• Mini Case: Fixing On-Time Delivery at MetroFoods

Chapter 2. Data Foundations for Analytics

• Business Data Sources: ERP, CRM, Web, Social, IoT
• Data Types and Structures: Tables, Time Series, Text, Clickstream
• Data Quality: Completeness, Accuracy, Consistency, Timeliness
• Data Preparation: Cleaning, Transformation, Feature Creation
• Sampling and Data Collection in Business Settings
• Data Governance: Ownership, Privacy, and Compliance

Chapter 3. Descriptive Analytics and Visualization

• KPIs, Dashboards, and Performance Management
• Exploratory Data Analysis (EDA) for Business
• Data Visualization Principles for Managers
• Segmentation Basics: Cohorts, RFM, and Clustering Intuition
• Storytelling with Data: Narratives and Executive Communication
• Common Visualization Mistakes and How to Avoid Them
• Chapter Summary
• Key Terms
• Review Questions (MBA level)
• Mini Case: The Mystery of Rising Stock-outs
• Practical Exercises

Chapter 4. Probability, Uncertainty, and Statistical Thinking

• Uncertainty in Business Decisions
• Probability Concepts for Managers
• Distributions Common in Business Data
• Sampling Distributions and the Central Limit Theorem
• Confidence Intervals and Practical Interpretation
• Hypothesis Testing: p-values, Errors, and Power
• Chapter Summary
• Key Terms
• Review Questions (MBA level)
• Mini Case: Credit Growth vs. Risk at Zenith Bank

Chapter 5. Predictive Modeling for Business

• Predictive Problem Types: Classification vs. Regression
• Model Building Workflow: Train/Test, Validation, Cross-Validation
• Linear Regression for Forecasting and Drivers
• Logistic Regression for Propensity and Risk
• Decision Trees and Ensemble Models: Random Forests, Boosting (Conceptual)
• Model Evaluation: Accuracy, AUC, RMSE, Lift, Calibration
• Managerial Toolkit: A Predictive Model “Model Card” Template
• Chapter Summary
• Key Terms
• Review Questions (MBA level)
• Mini Case: Retention Targeting at MetroFoods

Chapter 6. Business Forecasting and Time Series Analytics

• Forecasting Use Cases: Demand, Sales, Inventory, Workforce
• Time Series Components: Trend, Seasonality, Cycles
• Baseline Methods: Moving Average, Exponential Smoothing
• ARIMA Concepts and When to Use It
• Forecast Accuracy Metrics and Bias
• S&OP and Forecasting in Operations
• Managerial Toolkit: Selecting a Forecasting Approach
• Chapter Summary
• Key Terms
• Review Questions (MBA level)
• Mini Case: Planning Fresh Produce for a Festival Week

Chapter 7. Prescriptive Analytics and Optimization

• From Prediction to Action: Decision Models
• Optimization Basics: Objective, Constraints, Feasibility
• Linear Programming for Allocation and Planning
• Integer Programming for Scheduling and Network Design
• Simulation and What-If Analysis
• Decision Under Uncertainty: Robust and Scenario Planning
• Managerial Toolkit: How to Build a Prescriptive Model
• Chapter Summary
• Key Terms
• Review Questions (MBA level)
• Mini Case: Designing a Peak-Hour Service Plan

Chapter 8. Experimentation, A/B Testing, and Causal Inference

• Why Causality Matters in Business
• Designing Experiments: Randomization and Control
• A/B Testing Metrics: Conversion, Retention, Revenue
• Sample Size, Power, and MDE (Managerial Intuition)
• Quasi-Experiments: Difference-in-Differences, Matching (Conceptual)
• Common Experiment Traps: Novelty, Interference, P-hacking
• Managerial Toolkit: Experiment Design Checklist
• Chapter Summary
• Key Terms
• Review Questions (MBA level)
• Mini Case: Coupon ROI at MetroFoods

Chapter 9. Customer, Marketing, and Revenue Analytics

• Customer Analytics Frameworks: Funnel, Journey, LTV
• Churn Analytics and Retention Strategy
• Pricing Analytics: Elasticity, Promotions, and Price Tests
• Marketing Mix and Attribution (Conceptual)
• Recommendation and Personalization (Managerial View)
• Revenue Management and Capacity Constraints
• Managerial Toolkit: Customer Analytics Operating Dashboard
• Chapter Summary
• Key Terms
• Review Questions (MBA level)
• Mini Case: Profitable Growth Plan for MetroFoods

Chapter 10. Operations and Supply Chain Analytics

• Inventory Analytics: EOQ, Safety Stock, Service Levels
• Process Analytics: Bottlenecks, Cycle Time, Variability
• Quality Analytics: Control Charts and Six Sigma Link
• Logistics and Routing Analytics (Managerial View)
• Workforce and Capacity Planning
• Risk and Resilience in Supply Chains
• Managerial Toolkit: Operations Analytics Playbook
• Chapter Summary
• Key Terms
• Review Questions (MBA level)
• Mini Case: Reducing Stock-outs Without Increasing Waste

Chapter 11. Financial and Risk Analytics

• Analytics for Financial Performance and Value Drivers
• Credit Risk, Fraud, and Anomaly Detection (Conceptual)
• Portfolio Concepts for Managers
• Scenario Analysis and Stress Testing
• KPIs for Finance: Cash Conversion, Margins, ROIC
• Model Risk Management and Controls
• Managerial Toolkit: Finance Analytics Governance Pack
• Chapter Summary
• Key Terms
• Review Questions (MBA level)
• Mini Case: Balancing Growth and Risk at Zenith Bank

Chapter 12. Analytics Strategy, Ethics, and Implementation

• Building an Analytics Operating Model
• Make vs. Buy: Tools, Platforms, and Vendor Evaluation
• MLOps and Model Lifecycle Management (MBA-Level Overview)
• Ethics, Fairness, and Responsible AI
• Change Management: Adoption, Incentives, and Culture
• Measuring Impact: ROI, OKRs, and Governance
• Managerial Toolkit: Analytics Strategy Blueprint (One Page)
• Chapter Summary
• Key Terms
• Review Questions (MBA level)
• Capstone Case: Scaling Analytics at MetroFoods
• Decision Canvas (Template)
• Model Card (Managerial Template)
• Abbreviations and Notation
• Recommended Readings and Practical Resources
• Toolkit and Case Index
• Colophon
• Index
• Glossary
• References

About the Author

Dr. R. Sakthivel (M.Tech–IT, MBA, M.Sc (Mathematics), PhD) is a senior management academic and Academic Administrator with over 30 years of progressive experience in higher education, spanning teaching, institutional leadership, accreditation support, and research. He is currently serving as Professor, Department of Management Studies, Chikkanna Government Arts College, Tiruppur, where he has been in service since 01 December 2021, steering academic planning, delivery, mentoring, and departmental governance. He previously served as Regional Officer, South Western Regional Office (SWRO), AICTE, from 01 December 2018 to 30 November 2021. Prior to that, he was Head of the Department (Management Studies), Government Arts College, Coimbatore, from 01 March 2011 to 30 November. Earlier in his career, he served as Director – Management Studies, Karpagam Institute of Technology, Coimbatore (01 October 2007 – 28 February 2011), leading end-to-end academic and administrative functions including national and international conference organization, accreditation and compliance reporting, curriculum development, admissions, examinations, industrial engagement, student counselling, project supervision, hostel and discipline administration, and placement facilitation. He began his academic career as Professor (MBA), St. Peter’s Engineering College, Chennai (01 September 1994 – 30 April 2007), teaching core domains such as Marketing Management, Marketing Research, and Entrepreneurship Development, while contributing to institutional accreditation documentation and university/AICTE compliance requirements.

Research and Academic Contributions

His doctoral research in Service Marketing (University of Madras, 2002–2006) anchors a sustained research trajectory across healthcare reforms and private health insurance, customer relationship management in insurance services, telecom consumer behaviour, leadership training, and organisational behaviour themes. His work has been disseminated through journal publications and peer academic forums. Dr. Sakthivel has also contributed extensively to academic quality assurance and governance. He has served as an examiner for the University of Madras, Anna University, and Bharathiar University; as an Anna University representative to affiliated institutions; and as a question-paper setter for multiple universities and autonomous colleges. These roles have strengthened evaluation standards, assessment integrity, and governance frameworks in management education. Committed to advancing management education through academic leadership, research, and institutional excellence.

Recommended Citation

Sakthivel, R. Business Analytics: A Managerial and Applied Approach. Chennai, India: Jupiter Publications Consortium, 2026. DOI: 10.47715/978-93-86388-99-5.

Citation Formats with DOI

APA 7

Sakthivel, R. (2026). Business analytics: A managerial and applied approach. Jupiter Publications Consortium. https://doi.org/10.47715/978-93-86388-99-5

MLA 9

Sakthivel, R. Business Analytics: A Managerial and Applied Approach. Jupiter Publications Consortium, 2026. https://doi.org/10.47715/978-93-86388-99-5

Chicago 17

Sakthivel, R. Business Analytics: A Managerial and Applied Approach. Chennai, India: Jupiter Publications Consortium, 2026. https://doi.org/10.47715/978-93-86388-99-5

Harvard

Sakthivel, R. 2026, Business Analytics: A Managerial and Applied Approach, Jupiter Publications Consortium, Chennai, viewed via DOI: https://doi.org/10.47715/978-93-86388-99-5

IEEE

R.Sakthivel, Business Analytics: A Managerial and Applied Approach. Chennai, India: Jupiter Publications Consortium, 2026, doi: 10.47715/978-93-86388-99-5.

The digital transformation of education through Artificial Intelligence (AI) parallels a paradigm shift akin to that observed in precision healthcare. This monograph explores the integration of AI-powered personalization in learning environments, demonstrating how intelligent systems can tailor pedagogy, content, and assessment based on learner behavior, preferences, and performance. Through a multidisciplinary lens, the work evaluates adaptive technologies, ethical challenges, algorithmic bias, and the evolving role of educators. It presents a policy-informed roadmap for scalable and equitable AI implementation in education. Drawing inspiration from personalized diagnostics and treatment models in healthcare, the monograph advocates for a learner-centered, data-informed, ethically grounded, and future-ready framework. Rich with case studies, conceptual frameworks, and evidence-based recommendations, it serves as a comprehensive guide for educators, policymakers, researchers, and AI technologists committed to shaping the future of learning.

Keywords:

Artificial Intelligence, Personalized Learning, Educational Technology, Adaptive Learning Systems, Learning Analytics, Algorithmic Fairness, Ethical AI, Intelligent Tutoring Systems, AI Literacy, Educational Equity, Precision Healthcare, Scalable Implementation, Policy Frameworks, Emotional AI, Augmented Reality, Virtual Reality, Human-AI Collaboration, Digital Transformation

CITE THIS BOOK:

APA (7th ed.)

Kumar, S. M. (2025). AI-powered personalization in learning. Jupiter Publications Consortium. https://doi.org/10.47715/978-93-86388-73-5

MLA (9th ed.)

Kumar, S. Mohan. AI-Powered Personalization in Learning. Jupiter Publications Consortium, 2025. DOI: 10.47715/978-93-86388-73-5.

Chicago (17th ed., Author–Date)

Kumar, S. Mohan. 2025. AI-Powered Personalization in Learning. Chennai: Jupiter Publications Consortium. https://doi.org/10.47715/978-93-86388-73-5.

Chicago (Notes & Bibliography)

Kumar, S. Mohan. AI-Powered Personalization in Learning. Chennai: Jupiter Publications Consortium, 2025. https://doi.org/10.47715/978-93-86388-73-5.

Harvard Style

Kumar, S.M. (2025) AI-powered personalization in learning. Chennai: Jupiter Publications Consortium. DOI: 10.47715/978-93-86388-73-5.

IEEE

S. M. Kumar, AI-Powered Personalization in Learning. Chennai, India: Jupiter Publications Consortium, 2025. doi: 10.47715/978-93-86388-73-5.

Vancouver

1. Kumar SM. AI-Powered Personalization in Learning. Chennai: Jupiter Publications Consortium; 2025. 152 p. DOI: 10.47715/978-93-86388-73-5.

Turabian

Kumar, S. Mohan. AI-Powered Personalization in Learning. Chennai: Jupiter Publications Consortium, 2025. https://doi.org/10.47715/978-93-86388-73-5.

BibTeX

RIS Format

AMA Style

Kumar SM. AI-Powered Personalization in Learning. Chennai, India: Jupiter Publications Consortium; 2025. doi:10.47715/978-93-86388-73-5.

The monograph “AI in Precision Healthcare: A New Frontier” explores the transformative role of Artificial Intelligence in reshaping healthcare through personalization, prediction, and data-driven decision-making. This work offers a comprehensive overview of the integration of AI technologies into various domains of precision medicine, ranging from diagnostics and therapeutics to patient monitoring and chronic disease management. It also examines the convergence of machine learning, deep learning, and big data
analytics with clinical practices to enable individualized treatment strategies. In addition, the book addresses key ethical, legal, and operational challenges such as data privacy, algorithmic bias, and accountability in AI systems. Through real-world applications, conceptual clarity, and multidisciplinary insights, this monograph serves as a vital resource for academicians, practitioners, and policymakers aiming to understand and enhance AI in modern healthcare systems.

Keywords: Artificial Intelligence, Precision Medicine, Machine Learning, Deep Learning, Diagnostics, Predictive Analytics, Personalized Treatment, Clinical Decision Support, Medical Imaging, Health Informatics, Wearable Devices, Data Privacy, Ethical AI, Healthcare Technology, Risk Prediction, Patient Monitoring

How to Cite this Monograph:

APA Style Citation:

Mohan Kumar, D. S., & Balakrishnan, D. G. (2025). AI in Precision Healthcare: A New Frontier (1st ed.). Jupiter Publications Consortium. https://doi.org/10.47715/978-93-86388-50-6

IEEE Style Citation:

D. S. Mohan Kumar and D. G. Balakrishnan, AI in Precision Healthcare: A New Frontier, 1st ed., Chennai, Tamil Nadu, India: Jupiter Publications Consortium, 2025. doi: 10.47715/978-93-86388-50-6.

Chicago Style Citation:

Mohan Kumar, Dr. S., and Dr. G. Balakrishnan. AI in Precision Healthcare: A New Frontier. Chennai, Tamil Nadu, India: Jupiter Publications Consortium, 2025. https://doi.org/10.47715/978-93-86388-50-6.

MLA Style Citation:

Mohan Kumar, S., and G. Balakrishnan. AI in Precision Healthcare: A New Frontier. Jupiter Publications Consortium, 2025. DOI: https://doi.org/10.47715/978-93-86388-50-6.

Author 1:

Prof. (Dr.) S. Mohan Kumar
M.Tech.[Software Engineering]
Ph.D [CSE-Medical Diagnosis CAD System]
Ph.D [Medical Imaging -Machine Learning]
Post Doctorate Degree D.Sc. [Engineering-DL]
EPLM (IIM-Calcutta)
D.Litt (Honorary)
Dean, Indra Ganesan College of Engineering
Tiruchirappalli, Tamil Nadu, India.

Author 2:

Prof. (Dr.) G. Balakrishnan
M.E .[Computer Science And Engineering]
PSG College of Technology, Coimbatore, India
Ph.D [Computer Science And Engineering]
Universiti Malaysia Sabah, Malaysia
Director (IGI) Syndicate Member (Anna University) Principal, Indra Ganesan College of Engineering
Tiruchirappalli, Tamil Nadu, India.

Advancements in Artificial Intelligence (AI), such as machine learning, natural language processing, computer vision, and cloud computing, are presenting new possibilities in information systems technology by improving the management of data, decision-making processes, interaction interfaces, and cybersecurity. With a focus on an optimised AI information system, this monograph studies the evolution of AI information systems. It addresses data processing, decision-making, and cybersecurity AI optimisations. AI-powered personalisation interfaces, AI-powered trends, AI-infused IoT, quantum computing, and the ethics surrounding AI are addressed in the book as well. Profound case studies make the content relatable for numerous industries to aid researchers, academics, and specialists in
understanding information systems intertwined with the intelligence of AI.

Keywords:

Artificial Intelligence, Information Systems, Machine Learning, Data Management, Decision Support Systems, Cybersecurity, Natural Language Processing, Cloud Computing, AI-driven Innovations, Emerging Technologies

How to Cite this Book:

Kanya, N., Rajavarman, V. N., & Pavan, S. (2025). AI-driven innovations in information systems. Jupiter Publications Consortium. https://doi.org/10.47715/978-93-86388-55-1

This book, “Mastering Lecturette, GD & WAT: Essential Techniques for Persuasive Articulation,” is a comprehensive guide aimed at empowering aspirants preparing for the Services Selection Board (SSB) and similar high-stakes assessments. It offers structured techniques and strategies to master critical communication aspects, including Lecturettes, Group Discussions (GD), and the Word Association Test (WAT). The chapters are meticulously organized to address key areas such as impactful openings, motivational transitions, thought fillers, vocabulary enhancement, and effective conclusions. Additionally, the book provides insights into reframing negative words constructively and maintaining composure under pressure. With practical examples, contextual relevance, and actionable tips, this guide equips readers to demonstrate leadership, emotional resilience, and clarity of thought. Designed to inspire confidence and refine articulation skills, it is an indispensable resource for anyone aiming to excel in high-pressure evaluation scenarios.

Keywords: Lecturette, Group Discussion, Word Association Test, SSB preparation, public speaking, articulation skills, leadership qualities, thought fillers, transitions, impactful communication.

Book Citation: Magesh, S., & Martina, E. F. (2025). Mastering Lecturette GD and WAT : Essential Techniques for Persuasive Articulation (1st ed.). Jupiter Publications Consortium, Chennai, Tamil Nadu, India. ISBN: 978-93-86388-89-6, DOI: www.doi.org/10.47715/978-93-86388-89-6

The evolution of search engines from simple keyword-based systems to more sophisticated semantic-based models marks a significant advancement in the field of information retrieval. This monograph presents a comprehensive study on the development of a Semantic-Based Pattern Search Engine, designed to enhance the accuracy and relevance of search results by understanding the contextual and semantic relationships within data. The research encompasses the creation of various engines, including a concept search engine, topic search engine, affective term detection engine, and emotion prediction engine. Additionally, the application of these technologies in online opinion mining is explored, demonstrating their practical value. The findings of this research contribute to the broader understanding of semantic search and offer valuable insights for future advancements in the domain.

Keywords: Semantic-Based Pattern Search Engine, Concept Search Engine, Topic Search Engine, Affective Term Detection, Emotion Prediction, Online Opinion Mining, Information Retrieval, Semantic Search, Contextual Search

How to Cite:

Ajitha, P. (2024). SEMANTIC BASED PATTERN SEARCH ENGINE (1st ed.). Jupiter Publications Consortium. ISBN: 978-93-86388-47-6, DOI: www.doi.org/10.47715/978-93-86388-47-6

The integration of the Internet of Things (IoT) into healthcare has led to significant advancements in patient monitoring and care through Wireless Body Area Networks (WBANs). However, these innovations also present substantial challenges in ensuring the security and privacy of sensitive medical data. This research explores the various security threats and vulnerabilities associated with WBANs and IoT-based healthcare systems, proposing novel solutions to enhance data protection. The study introduces the ECG Hummingbird Algorithm, designed for secure data transmission in WBANs, and the Modified Feather Lightweight Block (MFLB) Cipher, which offers a lightweight encryption solution suitable for resource-constrained IoT devices. Experimental results demonstrate the effectiveness of these algorithms in mitigating security risks while maintaining system efficiency. This work contributes to the growing field of cybersecurity in healthcare,
offering practical approaches to safeguarding patient information in an increasingly interconnected world.

Keywords: Internet of Things, IoT, healthcare, Wireless Body Area Networks, WBAN, security, privacy, ECG Hummingbird Algorithm,
Modified Feather Lightweight Block Cipher, encryption, data protection.

How to Cite this Monograph:

Sivasangari, A. (2024). Security and Privacy in IoT based Healthcare (1st ed.). Jupiter Publications Consortium. ISBN: 978-93-86388-62-9, DOI: www.doi.org/10.47715/978-93-86388-62-9

The monograph “Optimizing Routing Protocols for Energy Conservation in UWSN” explores advanced strategies to address the critical issue of energy efficiency in Underwater Wireless Sensor Networks (UWSNs). These networks have significant potential in various applications, such as aquatic life management, pollution monitoring, and coastal surveillance, but are often limited by high energy consumption. This work proposes innovative routing protocols that aim to conserve energy, extend the operational lifetime of the network, and enhance its reliability. The research encompasses a detailed study of UWSN architecture, acoustic propagation, and specialized routing protocols. The proposed energy-efficient strategies are thoroughly analyzed and validated through extensive experimentation, providing practical solutions and valuable insights for researchers and practitioners. The findings of this monograph contribute to the advancement of sustainable and efficient UWSNs, offering a robust framework for future developments in this field.

Keywords: UWSN, energy efficiency, routing protocols, underwater communication, acoustic propagation, network lifetime, environmental monitoring, sustainable networks

How to cite this Monograph:

Gomathi, R. M. (2024). OPTIMIZING ROUTING PROTOCOLS FOR ENERGY CONSERVATION IN UWSN (1st ed.). Jupiter Publications Consortium. ISBN: 978-93-86388-77-3, DOI: www.doi.org/10.47715/978-93-86388-77-3

This book provides an in-depth exploration of computational data analytics within the broader context of data science. It covers the fundamental concepts, methodologies, and tools that define the field, while also delving into advanced statistical and machine learning techniques tailored for large datasets. The text is structured to facilitate understanding of both theoretical principles and practical applications, bridging the gap between data analysis and real-world challenges. Ethical considerations, privacy, and data governance are emphasized to ensure readers are aware of the responsibilities that come with handling data. Each chapter is enriched with case studies that illustrate the application of computational data analytics in various domains such as healthcare, finance, and environmental studies. The book concludes with a forward-looking discussion on the future of data analytics, highlighting emerging trends and technologies.

Keywords: computational data analytics, data science, machine learning, statistical methods, big data, ethical considerations, real-world applications

Keywords: cybersecurity, artificial intelligence, machine learning, threat intelligence, malware detection, network security, incident response, security analytics, compliance, application security, cloud security.