First Semester Statistical Computing Course using Python
The course Optimization Techniques focuses on solving linear, integer, network-based, and nonlinear optimization problems using methods such as the Simplex method, Duality, Goal Programming, and Kuhn-Tucker conditions. It blends theoretical understanding with practical approaches in operations research to model and solve real-world decision-making problems.
- BCM Teacher: Albi Elizabeth Abraham
This course provides a comprehensive understanding of real analysis, vector spaces, linear algebra, and matrix theory, with applications in optimization and transformations. It equips students with essential tools for analyzing and solving mathematical problems in multivariable calculus and linear systems.
- BCM Teacher: Albi Elizabeth Abraham
This an odd semester course for first PG students which deals with random number generation,pgf ,mgf etc...
- BCM Teacher: Asha Kiran Francis
This is an odd semester course for first PG Students which deals with stimulation and probabilities.
- BCM Teacher: Asha Kiran Francis
- BCM Teacher: Asha Kiran Francis
This course provides a comprehensive understanding of time series data and forecasting techniques. It begins with classical decomposition methods, including trend and seasonality estimation using moving averages and exponential smoothing techniques such as Holt and Holt-Winters methods. The course then introduces stochastic models, covering autocorrelation functions, stationarity, and Box-Jenkins models including AR, MA, ARMA, and ARIMA. Estimation methods like Yule-Walker, maximum likelihood, and least squares are explored alongside model diagnostics and forecasting. Advanced topics include spectral analysis, seasonal ARIMA models, and an introduction to ARCH and GARCH models for modeling volatility.
This course introduces students to the fundamentals of Python programming with a focus on applications in statistical computing and data analysis. It covers core programming concepts including data types, control flow, functions, and object-oriented programming. Students will learn to process text and CSV files, handle exceptions, and visualize data using Python libraries. The course emphasizes statistical testing, data visualization techniques, and the simulation of random variables using Python. Practical applications include implementing analytical and sampling methods, performing hypothesis tests, and understanding probability distributions through simulation.
- BCM Teacher: Dona Joseph