PGDDS 2018

This course aims to provide an introduction to the data science approach to the quantitative analysis of data using the methods of statistical learning, an approach blending classical statistical methods with recent advances in computational and machine learning. We will cover the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the methods we cover. We also cover data preparation and processing, including working with structured databases, key-value formatted data, and unstructured textual data. At the end of this course students will have a sound understanding of the field of data science, the ability to analyze data using some of its main methods, and a solid foundation for more advanced or more specialized study.
The course is integrated in two trimesters (9 Months) with six core papers.    




Class Hours


DS101: Mathematics for Analytics

(30 Hours)

DS102: Statistics for Analytics

(30 Hours)

DS103: Applied Econometrics

(30 Hours)

DS104: Introduction to Machine Learning

(30 Hours)


DS201: Hadoop and Application

(30 Hours)

DS202: Behavioural Analytics for Business

(30 Hours)

DS203: Financial Analytics

(30 Hours)

DS204: Cases and Projects in Big Data Analytics

(30 Hours)


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