Life Sciences and HealthCare Analytics Course in USA

Using the popular Python and R programming languages, this module will enable learners to analyze the healthcare data including EHR and MIMIC- II datasets. They will also learn how to use Python to work with Big data in the field of LifeSciences.

The purpose of this module is to introduce the different Data Models prevalent in Life Sciences and Healthcare Data. Students will be exposed to Entity- Relationship diagrams and enunciate how to utilize them in providing Healthcare and Bio-statistics models. It will help learners in understanding the Genomics Exploratory Data Analysis, Differential Expression Levels, Normalization, and Batch Effects of the data.

• a. Genomics - DNA, RNA (Next Generation Sequencing Data Analysis), Gene MicroArray Gene Expression Data Analysis
• b. Proteomics - Deal with Proteomics Data Analysis, Drug Resistance and Drug Repurposing Data Analysis
• c. Metabolomics - Molecular-level (Abundance and Intensity values) Data Analysis, Identifying Biomarkers, etc.

This module gets into the heart and soul of the Data Science program. We start from basic statistics and progress to regression models, unsupervised and supervised learning models.

• a. Basic Statistics, Hypothesis Testing and Regression Models
• b. Unsupervised Machine Learning Techniques
• c. Supervised Machine Learning Algorithms

This module will help in building a predictive model to determine if a patient is likely to get re-admitted based on historical data, current diagnosis, treatment, lifestyle and behavioral indicators which will ultimately help take preventive measures.

This module introduces Natural Language Processing techniques and how they can be applied to clinical trial data. Students will also be exposed to some toolsets used in real-life scenarios and will be able to extract information stored in Electronic Medical Records (EMRs).

This module introduces students to the Multi-Layer Perceptron (MLP) which is the most common version of Artificial Neural Networks (ANNs). Students will also learn how they can be used to predict on EHR and other types of healthcare data. They will also predict a model that can detect cancer from DICOM images using Convolutional Neural Network (CNN) models. Finally, this model will inform the students about the limitations of Deep Learning Models in the Life Sciences and Healthcare Domain.

In this module, students will learn how to take advantage of Deep Learning Architectures to analyze and extract insights from panoramic dental x-rays to facilitate the segmentation of mandibles automatically. Students will see that this method is accurate and pretty quick in diagnosing dental records.