B.S. in Biotechnology (BTM 353)
Course Outcomes CO1 – Demonstrate bacterial conjugation and study gene transfer mechanisms in E. coli. CO2 – Perform and understand plasmid isolation and GFP-based gene expression. CO3 – Generate and isolate bacterial mutants through physical and chemical mutagenesis. CO4 – Apply techniques like gradient plates and replica plating to study antibiotic resistance. CO5 – Isolate and characterize enzyme-producing microbes using screening and assay techniques. CO6 – Identify and characterize petite mutants in yeast to study mitochondrial genetics.
B.S. in Biotechnology (BTM 352)
Course Outcomes CO1: Understand the basic concepts of enzymes, enzyme classification, and nomenclature. CO2: Explain enzyme kinetics, mechanisms of enzyme action, and enzyme inhibition. CO3: Gain knowledge about enzyme regulation, allosteric enzymes, and coenzymes. CO4: Outline principles of enzyme purification, characterization, and industrial applications.
B.S. in Biotechnology (BTM 351)
Course Outcomes CO1: Explain the fundamental processes of gene expression and the regulatory mechanisms in prokaryotic and eukaryotic systems. CO2: Analyze epigenetic and post-transcriptional mechanisms involved in gene regulation, including methylation, histone modifications, and RNA interference pathways. CO3: Describe and compare natural and artificial gene transfer methods, including physical, chemical, and biological approaches, and their application in different organisms. CO4: Differentiate between types of mutations and mutagens, and evaluate various mutagenesis techniques, including site-directed and random mutagenesis, along with DNA repair mechanisms. CO5: Demonstrate knowledge of vector systems such as plasmids, phages, cosmids, BACs, and YACs, and apply basic genetic engineering tools like restriction enzymes, ligases, and cloning strategies. CO6: Construct recombinant DNA molecules using molecular cloning tools and techniques, and interpret screening and selection strategies in genetic engineering experiments.
B.S. in Data Science & Analytics (DSE364)
Course Outcomes CO COGNITIVE ABILITIES COURSE OUTCOMES CO1 REMEMBERING Recall fundamental concepts, linguistics basics, and NLP pipeline components. CO2 UNDERSTANDING Explain text preprocessing, feature extraction, and statistical & deep learning models for NLP. CO3 APPLYING Implement NLP tasks such as tokenization, POS tagging, sentiment analysis, and text classification. CO4 ANALYSING Compare traditional machine learning and deep learning approaches for NLP applications. CO5 EVALUATING Evaluate NLP models using precision, recall, F1-score, BLEU, and perplexity metrics. CO6 CREATING Design and develop end-to-end NLP solutions for real-world applications such as chatbots, translation, and information retrieval.
B.S. in Data Science & Analytics (DSE363)
Course Outcomes CO COGNITIVE ABILITIES COURSE OUTCOMES CO1 REMEMBERING Define the fundamental concepts of time series, its components, and basic statistical properties. CO2 UNDERSTANDING Explain the concepts of stationarity, autocorrelation, and various time series models (AR, MA, ARIMA, Exponential Smoothing, etc.). CO3 APPLYING Apply time series techniques for decomposition, model fitting, and forecasting using R/Python. CO4 ANALYSING Analyze temporal data to identify trends, seasonality, and residual patterns; evaluate model assumptions and diagnostics. CO5 EVALUATING Compare and assess different forecasting models (ARIMA, Exponential Smoothing, GARCH, VAR, etc.) using accuracy measures (AIC, BIC, RMSE, MAPE). CO6 CREATING Design and implement forecasting solutions for real-world datasets (finance, business, environment) and present actionable insights.
B.S. in Data Science & Analytics (DSM362)
Course Outcomes CO COGNITIVE ABILITIES COURSE OUTCOMES CO1 REMEMBERING Recall foundational concepts in predictive modeling and evaluation. CO2 UNDERSTANDING Explain predictive tasks, modeling workflows, and challenges like class imbalance. CO3 APPLYING Apply various predictive algorithms to real-world datasets using Python. CO4 ANALYSING Analyze model performance using validation methods and interpretability tools. CO5 EVALUATING Evaluate and compare predictive models using appropriate metrics. CO6 CREATING Build and tune end-to-end predictive modeling pipelines with Python.
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.
