Fundamental concepts of business information and digital systems; business-driven AI; digital transformation; electronic business value; ethics and information security; infrastructures and platforms; business intelligence and KPIs; digital ecosystems; enterprise applications; systems development; and corporate responsibility. Laboratory work using Microsoft Excel includes KPI analysis, scenario modeling, dashboards, financial and ROI evaluation, data-driven decision support.
Business analytics workflow and decision framing; data preparation and feature engineering; exploratory data analysis for business intelligence and performance measurement; regression for prediction and explanation; uncertainty quantification and statistical inference; model selection, validation, and regularization; classification for business decisions including cost-sensitive evaluation and calibration; experiments and A/B testing design and analysis; introductory causal inference using matching and panel data; tree-based models and ensemble methods; factor models and text as data for business applications; reproducible analytics and professional reporting for managerial decision-making.
AI value creation in organizations; AI opportunities, limitations, and organizational readiness; generative AI in business and business intelligence contexts: capabilities and use case selection; work practices for task framing, prompting, evaluation, and verification; human oversight in algorithmic decision making; responsible AI: bias, fairness, transparency, explainability, and privacy; AI risk management and control mechanisms; AI governance and operating models; AI tools and platforms; alignment of AI, data, and business intelligence strategy; AI ecosystems and business models; business case development and value realization through KPIs, pilots, performance measurement, and decision support; road mapping and change management for AI adoption and scaling; operational and strategic recommendations.
Foundations of data visualization and visual cognition; principles of performance measurement , KPI design; balanced scorecards, target setting for organizational units; dashboard design for executive and managerial reporting; development of business intelligence dashboards; analytical storytelling; interpretation and communication of performance trends, target deviations, management insights; narrative design for decision support; governance and ethical communication of business intelligence outputs.
Enterprise business intelligence architecture; SQL for enterprise data modeling and reporting; data warehousing concepts , star schema design; cloud data platforms for business intelligence; data transformation and modeling; dashboard and report development; business intelligence systems for performance measurement and performance management; target setting and target cascading in enterprise settings; integration of budgeting, forecasting, incentives, management control into business intelligence systems; governance of organizational reporting and decision-support platforms.
Advanced analytics workflow and reproducibility for performance measurement in business contexts; feature engineering for organizational data; model validation, resampling, and business-oriented evaluation; predictive and diagnostic models for performance analysis; classification and rare-event analytics for management decisions; anomaly detection for operational and financial monitoring; segmentation and clustering for managerial insight; selected machine learning methods for business intelligence and decision support; interpretation, transparency, and responsible AI practices in measuring performance, evaluating initiatives, supporting evidence-based business decision-making.
Concepts and systems of target setting and performance management in organizations; alignment of strategy, budgets, KPIs, and operational targets; performance measurement frameworks including balanced scorecard, target cascading, and management-by-objectives; planning and budgeting links to performance management systems; analytical review of target achievement, variances, and corrective actions; implementation of analytics initiatives for performance improvement; project planning, stakeholder management, governance, and risk in performance-oriented analytics programs; ROI evaluation and value realization for business analytics and AI initiatives.
Introduction to workforce performance analytics in organizations; workforce data architecture and HR information systems; employee performance measurement and KPI design; target setting for workforce productivity, retention, and development; compensation, incentives, and performance management analytics; workforce planning and budgeting analytics; predictive models for workforce risk and talent outcomes; statistical and analytical methods for workforce decision making; interpretation of workforce dashboards and management reports; ethical governance of workforce data and analytics.
Comprehensive examination of cybersecurity management, strategic risk within organizational and global frameworks; foundational principles of information security and risk management; cybersecurity concepts and terminologies; human behavior, organizational processes, technological factors shaping cyber risk; security governance, policy development, organizational security architecture; operational readiness, business continuity planning, incident detection, response, and recovery; legal and ethical considerations in privacy, data protection, intellectual property; data governance, breach management; cybersecurity intelligence and risk communication; security awareness, training, cyber culture development; financial, legal, operational implications of cyber threats; cybersecurity challenges in fintech ventures and digital platforms; impact of emerging technologies including artificial intelligence, IoT, cloud computing, and quantum cryptography on supply chains and markets.
The first phase of the senior project sequence for Business Analytics & Artificial Intelligence majors. Students form teams and develop comprehensive project proposals addressing real organizational challenges related to business analytics, business intelligence, performance management, artificial intelligence, governance, and data-driven decision support. Working with faculty advisors and industry stakeholders, students define project scope, identify analytical and organizational requirements, conduct preliminary research and feasibility analysis, and establish implementation and governance plans. The course emphasizes project planning, stakeholder engagement, ethical considerations, teamwork, and professional communication.
The second phase of the senior project sequence for Business Analytics & Artificial Intelligence majors. Students implement and complete applied analytics or AI projects addressing real organizational challenges in business intelligence, performance analytics, governance, decision support, and digital transformation. Through iterative analysis, modeling, dashboard development, AI-enabled evaluation, stakeholder engagement, and performance assessment, teams produce evidence-based recommendations and organizationally feasible solutions. The course emphasizes analytical integration, professional communication, governance awareness, teamwork, ethical reasoning, and project delivery.
Governance of data used in business intelligence, performance measurement, and decision-support systems; governance frameworks and operating models; data policies, standards, definitions, business glossaries, metadata, and lineage; data quality management for organizational reporting and target setting; privacy, compliance, accountability requirements; data classification, stewardship, audit readiness, evidence management; technical controls for secure responsible handling of performance data; governance of data used in budgeting, forecasting, dashboards, analytical tools; governance requirements for analytics and AI-enabled business decisions.
Principles and practices of responsible AI in organizational decision-support systems; governance, accountability, human oversight in AI-enabled managerial decision-making; explainability, transparency, fairness, robustness, privacy, compliance in business contexts; evaluation and monitoring of AI-supported reporting, forecasting, and performance analysis; risks of algorithmic bias in budgeting, target setting, performance management; responsible use of AI in business intelligence and decision-support applications.
Platform business models and multi-sided markets; network effects and platform growth dynamics; platform data and business intelligence metrics; performance measurement for digital platforms and ecosystems; target setting for growth, engagement, retention, monetization; analytical methods for platform performance analysis; recommendation, matching, trust, fraud analytics; pricing and revenue performance analytics; platform governance and regulation; AI-enabled decision support in digital platforms; platform performance dashboards and case-based applications.
Analytics strategy in organizations; digital and analytics transformation foundations; strategic alignment of analytics initiatives with business objectives; performance management in analytics-enabled transformation; target setting and KPI design for transformation programs; budgeting and investment evaluation for analytics initiatives; analytics maturity assessment; governance and operating models for transformation; organizational change and capability development; performance improvement and value realization in analytics-driven organizations.
Concepts of risk, compliance, audit, and forensic analytics; internal controls and compliance monitoring; performance measurement in control and assurance environments; analytical methods for anomaly detection, fraud detection, and suspicious activity monitoring; transaction analytics for financial and operational risk; analytics for regulatory compliance and audit evidence; dashboarding and business intelligence for risk reporting; predictive and network analytics for compliance and fraud investigation; data-driven support for risk-based organizational decision making.
Business process analytics and process mining fundamentals; event logs and process data sources; process discovery and visualization; process performance measurement and bottleneck analysis; conformance checking and target-based process evaluation; analytical methods for identifying delay, waste, process variation; performance dashboards for operational monitoring; process enhancement and optimization; ERP-based process analytics; predictive and prescriptive process analytics for continuous performance improvement.
Experimental and causal analytics for business decision making; performance measurement through experiments and causal analysis; randomized experiments and A/B testing; target setting and intervention evaluation; statistical power and experimental design; observational causal inference and confounding; matching methods and propensity score techniques; difference-in-differences and panel-data methods; instrumental variables and regression discontinuity; causal machine learning; ethics and governance of experimentation in organizational and digital settings.
Integrative case-based analysis of contemporary business analytics and artificial intelligence applications in organizations; business intelligence and dashboard-based decision support; performance measurement and KPI analysis; target setting and performance management; budgeting and resource allocation; analytics for workforce, process, risk, and platform performance; responsible AI and governance in managerial decision-making; case analysis, articles, and applied exercises to practice structured diagnosis, evidence-based recommendations, executive communication, and ethical judgment in complex real-world business settings.