This department focuses on extracting actionable insights from complex datasets to drive business decisions. Students learn descriptive, diagnostic, predictive, and prescriptive analytics, big data processing, AI/ML applications, and advanced statistical modelling. The curriculum emphasizes data governance, ethical AI, explainable AI (XAI), and tools like Tableau and Power BI for visualization, enabling students to transform raw data into strategic intelligence.

• Data Collection & Cleaning: Gathering and preparing raw data for analysis.
• Statistical Analysis: Applying statistical methods to interpret data patterns.
• Machine Learning & AI Modelling: Building predictive models using algorithms like regression, clustering, and neural networks.
• Data Visualization: Creating dashboards and reports using tools like Tableau or Power BI.
• Big Data Analytics: Working with large datasets using tools like Hadoop or Spark.
• Business Intelligence (BI): Translating data into actionable business strategies.
• Customer Analytics: Analysing customer behaviour and preferences.
• Market Research & Trend Analysis: Identifying market trends and opportunities.
• A/B Testing & Experimentation: Testing hypotheses to optimize campaigns or features.
• Predictive Analytics: Forecasting future outcomes based on historical data.
• Sentiment Analysis: Analysing emotions and opinions expressed in text data (e.g., reviews, social media).
• Anomaly Detection: Identifying unusual patterns or outliers in datasets.
• Graph Analytics: Using graph theory to analyse relationships between entities (e.g., social networks).
• Time-Series Forecasting: Predicting future trends based on historical time-based data.
• Data Engineering: Building pipelines to collect, process, and store large volumes of data.
• Explainable AI (XAI): Developing models that provide interpretable explanations for their outputs.
• Causal Inference: Determining cause-and-effect relationships in data.
• Spatial Analytics: Analysing location-based data for mapping and geospatial insights.
• Behavioural Economics Modelling: Combining psychology and economics to predict decision-making.
• Data Governance: Establishing policies and procedures for managing data quality and security.