B.S. in Data Science & Analytics (DSM361)
Course Outcomes CO COGNITIVE ABILITIES COURSE OUTCOMES CO1 REMEMBERING Recall the characteristics, ecosystem components, and evolution of Big Data technologies. CO2 UNDERSTANDING Explain the architecture and working of Hadoop, Spark, and other Big Data frameworks. CO3 APPLYING Implement data processing workflows using Hadoop, Spark, Hive, and Pig. CO4 ANALYSING Compare various Big Data storage, processing, and analytics technologies. CO5 EVALUATING Evaluate Big Data tools for batch and real-time analytics based on requirements. CO6 CREATING Design and implement Big Data solutions for real-world applications.
B.S. in Data Science & Analytics (DSSEC356) Health Care Analytics
Course Outcomes CO COGNITIVE ABILITIES COURSE OUTCOMES CO1 REMEMBERING Recall fundamental concepts, data sources, and tools used in healthcare analytics. CO2 UNDERSTANDING Explain techniques for analyzing patient, clinical, and operational healthcare data. CO3 APPLYING Apply statistical, machine learning, and predictive models for healthcare decision-making. CO4 ANALYSING Analyze electronic health records (EHR), treatment outcomes, and hospital data to extract insights. CO5 EVALUATING Evaluate healthcare performance indicators such as patient satisfaction, cost efficiency, and resource utilization. CO6 CREATING Design dashboards and predictive models for Improving patient care, hospital operations, and clinical outcomes.
B.S. in Data Science & Analytics (DSSEC356) Marketing and Retail Analytics
Course Outcomes CO COGNITIVE ABILITIES COURSE OUTCOMES CO1 REMEMBERING Recall fundamental concepts, tools, and metrics used in marketing and retail analytics. CO2 UNDERSTANDING Explain data-driven techniques for customer segmentation, campaign analysis, and retail decision-making. CO3 APPLYING Implement clustering, association rule mining, and forecasting methods for marketing and retail datasets. CO4 ANALYSING Analyze customer behavior, sales trends, and product performance to derive actionable insights. CO5 EVALUATING Assess campaign performance, pricing strategies, and inventory efficiency using analytics techniques. CO6 CREATING Design and develop dashboards and reports for comprehensive marketing and retail analytics projects.
B.S. in Data Science & Analytics (DSSEC356) Financial Analytics
Course Outcomes CO COGNITIVE ABILITIES COURSE OUTCOMES CO1 REMEMBERING Recall fundamental concepts, tools, and techniques used in financial analytics. CO2 UNDERSTANDING Explain financial data sources, key ratios, risk factors, and valuation metrics. CO3 APPLYING Apply statistical and machine learning methods for stock price prediction, risk analysis, and portfolio optimization. CO4 ANALYSING Analyze financial statements, time series data, and market behavior to derive insights. CO5 EVALUATING Evaluate investment opportunities, risk-return trade-offs, and predictive models for decision-making. CO6 CREATING Design financial dashboards and predictive models for investment, credit scoring, and risk management.
B.S. in Data Science & Analytics (DSE355)
Course Outcomes CO COGNITIVE ABILITIES COURSE OUTCOMES CO1 REMEMBERING Recall fundamental security goals, cryptographic principles, and common ciphers. CO2 UNDERSTANDING Explain symmetric/asymmetric encryption algorithms, hashing, and digital signatures. CO3 APPLYING Apply cryptographic algorithms, steganographic techniques, and secure communication protocols. CO4 ANALYSING Analyze vulnerabilities, attacks, and security risks in digital systems. CO5 EVALUATING Evaluate privacy policies, security mechanisms, and legal compliance requirements. CO6 CREATING Design secure systems incorporating cryptography, data hiding, and privacy-by-design principles.
B.S. in Data Science & Analytics (DSE354)
Course Outcomes CO COGNITIVE ABILITIES COURSE OUTCOMES CO1 REMEMBERING Recall and identify fundamental concepts, theories, and models of consumer behaviour. CO2 UNDERSTANDING Explain and interpret how consumer behaviour theories and models apply in different market contexts. CO3 APPLYING Use statistical and machine learning methods to solve consumer and market research problems. CO4 ANALYSING Examine cultural, social, psychological, and personal factors that shape consumer decision- making. CO5 EVALUATING Critically assess research findings and provide evidence-based judgments for marketing strategies. CO6 CREATING Design and execute original market research studies and present innovative, actionable insights for decision-making.
B.S. in Data Science & Analytics (DSM353)
Course Outcomes CO COGNITIVE ABILITIES COURSE OUTCOMES CO1 REMEMBERING Recall fundamental concepts and architecture of operating systems. CO2 UNDERSTANDING Explain the structure and functioning of processes, memory, and file systems. CO3 APPLYING Apply concepts of scheduling, memory management, and synchronization. CO4 ANALYSING Analyze and compare different OS strategies and algorithms. CO5 EVALUATING Evaluate the performance and suitability of OS components for given scenarios. CO6 CREATING Design and implement basic Unix shell scripts for OS-level tasks.
B.S. in Data Science & Analytics (DSM352)
Course Outcomes CO COGNITIVE ABILITIES COURSE OUTCOMES CO1 REMEMBERING Recall fundamental security goals, cryptographic principles, and common ciphers. CO2 UNDERSTANDING Explain symmetric/asymmetric encryption algorithms, hashing, and digital signatures. CO3 APPLYING Apply cryptographic algorithms, steganographic techniques, and secure communication protocols. CO4 ANALYSING Analyze vulnerabilities, attacks, and security risks in digital systems. CO5 EVALUATING Evaluate privacy policies, security mechanisms, and legal compliance requirements. CO6 CREATING Design secure systems incorporating cryptography, data hiding, and privacy-by-design principles.
B.S. in Data Science & Analytics (DSM351)
Course Outcomes CO COGNITIVE ABILITIES COURSE OUTCOMES CO1 REMEMBERING Recall key machine learning concepts, types, and terminology. CO2 UNDERSTANDING Explain the principles behind supervised, unsupervised, and ensemble learning techniques. CO3 APPLYING Implement ML algorithms for regression, classification, and clustering using Python libraries. CO4 ANALYSING Analyze model performance using appropriate metrics and validation techniques. CO5 EVALUATING Evaluate the suitability of various models and tune hyperparameters effectively. CO6 CREATING Develop complete machine learning solutions and deploy models using Python.
