PG Level Advanced Certification Programme in Applied Data Science and
Machine Learning

Introduction to big data in biology

Levels of omics data, basic information flow in biology

Importance of Networks in Biology: Overview

Introduction to Network Science

Learning from Network structure: Predicting essential genes

Learning on Networks: Community detection to identify disease genes - Learning using Networks: Graph mining for predicting biosynthesis routes - Omic data analysis: Predicting mutations and genes that drive cancer

1. Problem Statement : Four case studies will be demonstrated. CS1: Choice of mode CS2: Travel time estimation CS3: Accident hot spot analysis CS4: Accident severity modelling

2. Model(s) intended to demonstrate : Logistics regression, Support vector regression, k-means clustering and random forest

3. Dataset to be used during the demo

4. Dataset for the mini project

1. Levels of omics data, basic information flow in biology

2. Genomics, Transcriptomics, Epigenomics, Proteomics and Multi omics - Identification human disease genes using genomics

3. Application of transcriptomics for identifying disease mechanisms

4. Clinical data - kinds of clinical data Garbhini dataset - a clinical data case study

a. Gradient Descent and its variants

b. Momentum, Adam, etc.

c. Batch Normalization

a. Introduction

b. CNN Operations

c. CNN Training

d. Illustrative Example (“Hello World”) - MNIST digit classification e. Image Recognition-SoTA model(s)

f. Object detection/localization - SoTA model(s)

g. Semantic segmentation -SoTA model(s)