This course provides an advanced foundation in modern statistical and machine learning methods used in data science, covering four major areas: the Expectation–Maximization (EM) algorithm for parameter estimation in mixture and latent-variable models; Support Vector Machines (SVMs) including margin-based classifiers, kernel methods, multi-class strategies, and the Proximal SVM; Multidimensional Scaling (MDS) for visualizing similarity data, creating perceptual maps, and interpreting latent dimensions; and Structural Equation Modelling (SEM), focusing on latent constructs, model specification, estimation, and interpretation. Together, these modules equip students with theoretical understanding and practical skills for analyzing complex data, building predictive models, and interpreting multivariate relationships.
- BCM Teacher: Dona Joseph
This course provides comprehensive training in statistical analysis using JAMOVI and
Introduces R programming. Students will learn to analyse real data sets, conduct
various statistical tests, and apply regression analysis using JAMOVI, enhancing their
proficiency in statistical analysis for research and data-driven decision-making. Upon
completion of this course student acquires NOS1,2,3,5 of Data Analysis
Associate available in NQR.
- BCM Teacher: Dona Joseph
To get basic knowledge and skills of data analysis using spreadsheets and be able
to create printed materials with professional quality using LaTex.
- BCM Teacher: Dona Joseph
The course delves into the crucial intersection of ethics and data analysis tools.
Students examine real-world ethical dilemmas and learn strategies to mitigate biases
and ensure responsible data handling within software-driven analyses. The course
also gives an introduction to statistical machine learning and enables the student to
up-skill his technical presentation skills.
- BCM Teacher: Dona Joseph
Data Visualization course offered to M Sc Statistics Fourth semester
Machine Learning Course offered to M Sc Statistics Fourth Semester course