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Department Wise COs (Course Out Comes)

COURSE OUTCOMES

B.Sc HONOURS DATA SCIENCE


PaperPaper NameOutcomes After completion of the course, the student should be able to
SEMESTER I
COURSE 1ESSENTIALS AND APPLICATIONS OF MATHEMATICAL, PHYSICAL AND CHEMICAL SCIENCESCO 1 : Apply critical thinking skills to solve complex problems involving complex numbers, trigonometric ratios, vectors, and statistical measures.
CO 2 : Explain the basic principles and concepts underlying fundamental areas of physics and connect knowledge to everyday situations.
CO 3 : Explain basic principles of chemistry and connect knowledge to daily life.
CO 4 : Understand interplay between mathematics, physics, and chemistry in various applications.
CO 5 : Explore history of Internet and understand network security concepts including threats and countermeasures.
COURSE 2ADVANCES IN MATHEMATICAL, PHYSICAL AND CHEMICAL SCIENCESCO 1 : Explore mathematical applications in physics and chemistry for real-world problem solving.
CO 2 : Understand renewable energy sources, nanomaterials, quantum communication, biophysics principles, and shape memory materials.
CO 3 : Understand computer-aided drug design, nanosensors, and effects of chemical pollutants.
CO 4 : Convert between number systems and understand transmission media types (wired/wireless).
SEMESTER II
COURSE 3INTRODUCTION TO DATA SCIENCE AND R PROGRAMMINGCO 1 : Recognize disciplines contributing to data science efforts.
CO 2 : Understand data science processes from problem identification to visualization.
CO 3 : Identify appropriate algorithms for different problem types.
CO 4 : Use R/Python for data analytics and visualization.
PRACTICALCO 1 : Perform basic operations in R with arithmetic and statistics.
CO 2 : Implement data manipulation and loading in R.
CO 3 : Apply loop functions (lapply, sapply, tapply, apply, mapply).
CO 4 : Create basic plots and visualizations.
COURSE 4DESCRIPTIVE STATISTICSCO 1 : Implement statistical concepts in data science domains.
CO 2 : Organize data and evaluate summary measures.
CO 3 : Analyze qualitative data characteristics and associations.
CO 4 : Conduct preliminary data exploration.
CO 5 : Perform correlation and regression analysis.
PRACTICALCO 1 : Create histograms and frequency polygons.
CO 2 : Design bar/pie charts and compute central tendency measures.
SEMESTER III
COURSE 5PYTHON PROGRAMMING FOR DATA ANALYSISCO 1 : Program in Python for data analysis.
CO 2 : Understand data analysis phases and libraries.
CO 3 : Implement data cleaning with Pandas.
COURSE 5PRACTICALCO 1 : Apply Python data analysis methods.
CO 2 : Navigate Python programming environments.
COURSE 6INFERENTIAL AND APPLIED STATISTICSCO 1 : Apply law of large numbers.
CO 2 : Conduct point estimation and hypothesis testing.
CO 3 : Perform inferences on probability distributions.
CO 4 : Implement non-parametric methods.
PRACTICALCO 1 : Analyze time series data components.
CO 2 : Implement demographic concepts through practical assignments.
COURSE 7DATA MINING TECHNIQUES USING RCO 1 : Understand data mining algorithms.
CO 2 : Compare different data mining approaches.
CO 3 : Apply techniques to real-world problems.
SEMESTER IV
COURSE 8WEB TECHNOLOGIESCO 1 : Create static/dynamic websites.
CO 2 : Implement JavaScript interactions.
CO 3 : Host websites and manage web content.
PRACTICALCO 1 : Develop college website pages.
CO 2 : Create framed webpages with dynamic layouts.
SEMESTER V
COURSE 12SUPERVISED ML WITH PYTHONCO 1 : Understand ML concepts and model persistence.
CO 2 : Implement feature extraction and classification.
CO 3 : Compare SVM with other classifiers.
PRACTICAL

CO 1 : Explore classification/regression algorithms.
CO 2 : Implement supervised learning tasks.