Business Analytics : Data Management, Predictive Methods, Machine Learning, Big Data, Power BI and Tableau
Language: English
Published by Amazon Digital Services LLC - Kdp Aug 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 44.18
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- Title
- Business Analytics : Data Management, Predictive Methods, Machine Learning, Big Data, Power BI and Tableau
- Author
- Alessio Faccia
- Publisher
- Amazon Digital Services LLC - Kdp Aug 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798192926802
- Item weight
- 599 grams
- Dimensions
- 229x152x23 mm
Turn business data into sound decisions with a practical guide covering analytics, statistics, machine learning, visualisation and responsible AI.
Business Analytics gives managers, analysts, finance professionals, auditors, consultants, students and lecturers a structured route from raw data to defensible action. The book connects analytical methods with commercial decisions, operating constraints, financial value and accountability.
The opening chapters explain business intelligence, descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics. Readers learn how each approach answers a different management question, and why problem formulation determines the value of every later calculation.
Coverage of data acquisition and preparation addresses databases, APIs, websites and open-data portals. The text examines data quality, missing values, duplicate records, outliers, measurement scales, data integration, point-in-time controls and feature engineering. Examples show how weak joins, inconsistent definitions and information leakage distort apparently credible results.
Statistical sections cover distributions, sampling, confidence intervals, hypothesis testing, correlation, regression and causal reasoning. Technical measures receive commercial interpretation. Readers learn why statistical association does not prove cause, why a small p-value does not establish material business value and why uncertainty belongs in every responsible recommendation.
Applied chapters examine classification, clustering, forecasting and machine learning. Coverage includes decision trees, ensembles, support vector methods, neural networks, training and test design, overfitting, calibration, threshold selection, validation, drift and production monitoring. Discussion links model performance with false-positive costs, investigation capacity, customer outcomes and expected financial return.
Practical workflows cover Excel, SQL, Python and R. Readers see where each tool fits within extraction, cleaning, analysis, automation and reporting. Power BI and Tableau sections address data models, calculations, dashboards, filters, permissions, refresh controls and visual design. Guidance on charts, scales, colour and uncertainty helps readers present evidence without distortion.
Deployment receives equal attention. A model creates value only when its output changes an action. Readers learn how to connect analytics with operating processes, assign ownership, monitor performance and retire obsolete systems. Financial appraisal covers net benefit, return on investment, payback, net present value, sensitivity analysis and Monte Carlo simulation.
Responsible practice runs throughout the book. Dedicated coverage addresses privacy, bias, fairness, cybersecurity, explainability, human review, audit trails, supplier risk and AI governance. Generative AI sections examine prompt injection, confidential-data exposure, unsupported outputs and excessive tool permissions. Applications in recruitment, credit and customer service connect technical controls with managerial accountability.
Business Analytics suits readers seeking a rigorous reference for study, professional practice, corporate training or applied research. It supports newcomers who need clear foundations and experienced practitioners who need a disciplined method for evaluating models, dashboards and analytical projects.
Readers will learn how to:
Frame measurable business questions.
Assess source reliability and data quality.
Prepare reproducible analytical datasets.
Select statistical and machine-learning methods.
Validate models under realistic operating conditions.
"Synopsis" may belong to another edition of this title.
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