MSC Semester I - PRINCIPLES OF DATA SCIENCE (DS)

UNIT-I

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.

UNIT-II

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.

UNIT-III

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.

UNIT-IV

Unsupervised learning Algorithms: Introduction to cluster analysis, clustering techniques,

Agglomerative hierarchical clustering techniques, K-means, K-medoids, and KNN.