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.