M.Sc. in Bioinformatics(M.Sc. BI)

MSc (Bioinformatics) Course at REVA University has been designed to meet the human resources needs of existing and futuristic biotech industries, biotech research organizations and academic institutions. This bioinformatics master’s bridges the interfaces between genomics, computing and healthcare and aims to equip you with the skills to analyses, interpret and use biological data to inform and improve healthcare and health outcomes. At REVA University the Bioinformatics Programme focuses on the practical application of Bioinformatics. Bridge courses are offered to reduce the gap between biology, bioinformatics domain. The curriculum commences with training in programming, data science and elementary bioinformatics tools aimed at using existing software to collect, analyses and interpret DNA and protein sequence information and moves on to more open challenges. The Programme also provide sufficient skills and training on entrepreneurship development opportunities in Bioinformatics. The Programme deals with courses on Genomic data science, Programming in R and Python, Biostatistical Analysis, Omics Technologies, Systems Biology, Big Data Analytics, Cloud computing, data mining and artificial intelligence, deep learning, and many other related courses.

Program highlights

• Interdisciplinary Science amalgamating Biology, Medicine, Mathematics, Statistics, Computer Science.
• Guidance from expert academicians and lead researchers in the field of Clinical Research, agriculture, microbiology, and biostatistics.
• Projects based learning in collaborations with multiple industry partners such as Strand Genomics, Med Genome, Eurofins, ThermoFisher Scientific, Qiagen, etc.
• Platform to explore the frontiers of bioscience from precision medicine to precision agriculture, and other emerging fields, including molecular ecosystems biology.
• Opportunity to analyses predominant omics datatypes including next generation sequencing, mass spectrometry, emerging single molecule techniques, genome engineering.
• imaging, and integrative analysis toolsets.
• Strong foundation in statistical machine learning to shape career related to the field of information sciences.
• Student exchange programme in collaborations with foreign Universities.
• Guidance from highly qualified faculty members, industry.An ability to integrate algorithms and statistical methods.
• Internship opportunities at reputed industries and research organizations.

Core Modules

• Advanced Genomic Data Sciences
• R and Python Programming
• Biostatistical Analysis
• Big Data Analytics
• Cloud Based Analytics in Genomics
• Data Mining and Artificial Intelligence
• Webservers and Database development
• Artificial Intelligence and Deep Learning techniques
• Clinical Genomics
• Agri-genomics
• Nutrigenomics
• AI based Tool Development