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MSC Semester I - PRINCIPLES OF DATA SCIENCE
Updated on Sep, 2026
Programming, Data Science, Python
Duration - 21.5 hours
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Introduction to Data: Data types, measurement of scales, understanding data with
descriptive statistics and with data visualization and data pre-processing steps. Data
transformations (standardize, normalize, converting data from one scale to other scales).
Parametric & Non-Parametric tests (z-, f-, t-, and F-tests; sign test, median, Wilcoxon sign rank,
Mann-Whitney U, run tests, and feature selection methods.
Introduction to Data Modelling: Review of the Modelling Process and Concepts of Model
evolution, overfitting, underfitting, cross-validation concepts (train/test, K-fold, and leave-out
one approaches), Supervised and Unsupervised Modelling, Model Performance for
classification techniques for qualitative and quantitative data, model improvement, and saving
models for future use.
supervised learning algorithms: classification algorithms, multiple linear regression,
Multinomial logistic, support vector machine, random forest, and AdaBoost. Ensemble methods.
Decision Tree, Perceptron learning, Naive Bayes classifier.
Unsupervised learning Algorithms: Introduction to cluster analysis, clustering techniques,
Agglomerative hierarchical clustering techniques, K-means, K-medoids, and KNN.
Check out the detailed breakdown of what’s inside the course
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