India's first Industry focused PG program in Bioinformatics
An industry-driven intensive 1-year PG program in collaboration with leading universities, Metaverse campus-Led Learning with Offline Community Experiences
Next Cohort
1st July, 2024
Program Duration
12 Months, Hybrid (25-30 hours/week)
Learn from experts working at
Download Syllabus
Industry PGP in Bioinformatics, Genomics & data science
Join the league of the top 10%
Biotechnologists
"Join the elite league of the top 10% in the field of biotechnology and unlock endless career possibilities with our specialized training, industry connections, and personalized guidance."
Check out our Bversity alumni
Srirathi R
Toxicologist
Sneha Nair
TCS Life science
Chandana
Immunogenix
Sudhanva
USA
Sarvesh
Stanford Online
Harish Krrishna
NE University, USA
Vishwa M
Azooka Life Science
Take the First Step towards becoming a part of the Top 1% of Biotechnologists in the country!
*Admissions Ongoing for 2024
Apply now
Breaking Barriers with Democratised Education
Access high-quality education at 1/10th the price of typical Master's courses.
Tap into the
wisdom of industry experts from the Biotechnology industry
Success Managers
Daily 1:1 Counselling for student’s growth and potential maximisation.
Learning sessions & projects are built by experts in the Biotech industry
Keerthanaasri J P
Ex
BUILT
Healthtech apps
Diksha Pandey
BUILT
100+ workflow pipelines
Ex
Vinay Kumar
LEAD
Product manager
Ex
Berty Ashley
BUILT
Cure for DMD
Gnaneshwar Yadav
BUILT
mRNA translations
Manisha Bhardwaj
BUILT
Proteomics app
Supported & Mentored by India's Top Leaders
Suresh Sambandam
CEO, Kissflow
Convenor, Dream TN
Sathish Kumar
Founder, Milky Mist
Nagaraja Prakasam
Investor, Global mentor
Andi Giri
CEO, Softsquare
and 15+ top leaders
and 30+ Industry Leaders
Excited Already? Download our
brochure and begin your journey!
Download here
Build a portfolio for yourself. Let the industries know the projects you have worked and skillsets you have.
Apply here
1
Work on real-time industry projects
Qualify for jobs at top product startups
2
Stand out to recruiters
Globally-competitive salary based on
your skills.
3
Stand out to recruiters
Get interviews on your calendar directly.
Harish Krishna N
Bioinformatics analyst
Significant experience working with diverse NGS datasets and developing analytical pipelines that can transform genomic data into clinical information thus contributing to improving patient lives.
Tools I am experienced with
Application
Process & Timeline
Step 1
Online Application
Submit the online application along with the required documents.
Step 2
SOP Evaluation
Shortlisted candidates will be required to submit a SOP & attend an assesment.
Step 3
1: 1 Interview
Shortlisted candidates will be called for an online interview. The selected candidates will receive an offer of admission to the programme.
Eligibility
B.Tech, B.Sc (4 years) M.Sc & M.Tech Life science/Biotechnology graduates who passed with min 7 CGPA
Students with qualifying marks in any entrance ( (CSIR-UGC NET, GAT-B, DBT-JRF, CSIR, ICMR) doesn’t require Assessment Test
Empowering next-gen Biotechnologists
Over 8,000 students have upskilled with Bversity and have started their career with Bversity
Why this is the best for your career ?
1 year of accelerated PG programme
Work on 3+ industry capstone projects
Build your portfolio in the Biotech industry with experts
Learn 15+ tools used in the Biotechnology industry
Industry experts as your mentors
Work in a biotech industry while you learn in your final term
Book your seat with
₹
20,000
Only
Total Program Fee
₹
1,50,000
Next Cohort
1st July, 2024
Program Duration
12 Months, Hybrid (25-30 hours/week)
Get a discount of INR 50,000
Total Course Fee
INR 2,00,000
INR 1,50,000
The discount is valid only for the first few cohort of students joining the batches
Learn With Easy installments & EMI Plans.
The credit facility is provided by a third party credit facility
provider and any arrangement with such third party is
outside Novatr’s purview.
Talk to us today
A carefully crafted curriculum that unlocks advanced career roles for you
Term 1
4 months
1
A carefully crafted learning journey that unlocks advanced career roles for you
Understand the key concepts in modern biology and their application to bioinformatics
Explore the tools, databases, and resources used in bioinformatics research, and gain proficiency in their usage
Develop an understanding of genomics and next-generation sequencing techniques, including data analysis and interpretation
Module 1: Fundamentals of Bioinformatics and Modern Biology
Demonstrate proficiency in the basic syntax and structure of Python and R programming languages, including working with variables, data types, operators, and expressions
Develop the ability to utilise control flow statements and functions effectively in Python and R to create dynamic and efficient code for various programming tasks
Master file handling techniques in both Python and R, including reading and writing files, accepting user input, and handling command-line arguments to enhance program flexibility and interactivity
Module 2: Python & R programming languages
Understand different types of datasets and data generation methods used in bioinformatics, as well as common public databases in the field
Develop proficiency in using industry-standard tools and libraries for processing and analysing biological data
Omics Data Analysis - Tools, Outcomes, Use Cases and Applications
Module 3: Data analysis & visualization
Demonstrate proficiency in managing and analysing data by utilising different types of biological databases, applying appropriate database management systems, and implementing data security and privacy measures
SQL (structured query language) for relational databases. Master the fundamental concepts and principles of SQL, including syntax, data types, data manipulation, and database management.
Apply advanced SQL concepts such as joins, subqueries, views, stored procedures, and functions to query and aggregate data from multiple tables, optimise query performance, and solve real-world data-related problems.
Module 4: Data architecture & database management
Build a strong foundation in biostatistics by applying key concepts, such as variability, population, sample, central tendency, and variability, in order to analyse and interpret biological data using descriptive statistics techniques.
Apply inferential statistics techniques, such as hypothesis testing and confidence interval construction, to make statistical inferences and draw conclusions about populations based on sample data.
Design and implement biotechnology experiments using appropriate experimental design principles, including randomization, replication, and blocking, to ensure reliable and valid results.
Module 5: Introduction to Statistics in Biotechnology
Explain the fundamental concepts of Artificial Intelligence (AI) and Machine Learning (ML), including their different types and applications.
Implement common Machine Learning algorithms (e.g. Linear Regression, Decision Trees) using popular libraries like scikit-learn to analyse biological data and interpret the results.
Critically evaluate the strengths and limitations of Deep Learning architectures (e.g., CNNs, RNNs) for specific bioinformatics tasks, considering tools like TensorFlow and PyTorch for implementation.
Module 6: AI & ML in Biotechnology
Download detailed syllabus
Concepts covered
Tools you will use to learn
Brush up your basics & get started
What can you expect ?
720 hours of technical learning
48 hours of soft-skill building
1 capstone industry project
2 weeks of hackathons, workshops & bootcamps with the industry
Term 2
4 months
1
A carefully crafted learning journey that unlocks advanced career roles for you
Understand the key concepts in modern biology and their application to bioinformatics
Explore the tools, databases, and resources used in bioinformatics research, and gain proficiency in their usage
Develop an understanding of genomics and next-generation sequencing techniques, including data analysis and interpretation
Module 1: Fundamentals of Bioinformatics and Modern Biology
Demonstrate proficiency in the basic syntax and structure of Python and R programming languages, including working with variables, data types, operators, and expressions
Develop the ability to utilise control flow statements and functions effectively in Python and R to create dynamic and efficient code for various programming tasks
Master file handling techniques in both Python and R, including reading and writing files, accepting user input, and handling command-line arguments to enhance program flexibility and interactivity
Module 2: Python & R programming languages
Understand different types of datasets and data generation methods used in bioinformatics, as well as common public databases in the field
Develop proficiency in using industry-standard tools and libraries for processing and analysing biological data
Omics Data Analysis - Tools, Outcomes, Use Cases and Applications
Module 3: Data analysis & visualization
Demonstrate proficiency in managing and analysing data by utilising different types of biological databases, applying appropriate database management systems, and implementing data security and privacy measures
SQL (structured query language) for relational databases. Master the fundamental concepts and principles of SQL, including syntax, data types, data manipulation, and database management.
Apply advanced SQL concepts such as joins, subqueries, views, stored procedures, and functions to query and aggregate data from multiple tables, optimise query performance, and solve real-world data-related problems.
Module 4: Data architecture & database management
Build a strong foundation in biostatistics by applying key concepts, such as variability, population, sample, central tendency, and variability, in order to analyse and interpret biological data using descriptive statistics techniques.
Apply inferential statistics techniques, such as hypothesis testing and confidence interval construction, to make statistical inferences and draw conclusions about populations based on sample data.
Design and implement biotechnology experiments using appropriate experimental design principles, including randomization, replication, and blocking, to ensure reliable and valid results.
Module 5: Introduction to Statistics in Biotechnology
Explain the fundamental concepts of Artificial Intelligence (AI) and Machine Learning (ML), including their different types and applications.
Implement common Machine Learning algorithms (e.g. Linear Regression, Decision Trees) using popular libraries like scikit-learn to analyse biological data and interpret the results.
Critically evaluate the strengths and limitations of Deep Learning architectures (e.g., CNNs, RNNs) for specific bioinformatics tasks, considering tools like TensorFlow and PyTorch for implementation.
Module 6: AI & ML in Biotechnology
Download detailed syllabus
Concepts covered
Tools you will use to learn
Brush up your basics & get started
What can you expect ?
720 hours of technical learning
48 hours of soft-skill building
1 capstone industry project
2 weeks of hackathons, workshops & bootcamps with the industry
100% Placement Assistance
Dedicated career support and guidance to help
students land at Top Tech Companies
40+ Career Specialists
To ensure a portfolio and industry CV is built for a biotechnologist
80 Career Partners
Giving students the choice of a stellar lineup to start their biotech career!
Placements at Bversity School of Biotechnology
AWArD WINNING EDTECH STARTUP OF THE YEAR 2023
Bversity School of Biotechnology
At Bversity, we are proud to be a pioneer in Biotech education space, dedicated to
delivering a world-class educational experience that blends quality, innovation, skills and flexibility. All our learning
programs are fully accredited, adhering to the rigorous standards of Atria University, Bangalore.
Bversity + Atria University
Bversity School of Biotechnology has partnered with the Atria University, Bangalore in running the PG programs successfully & accredits the courses.
Frequently asked questions
1. What is the duration of the Industry PGP Bioinformatics course?
The course duration is 12 months.
2. What is the course structure like?
3. How is the Industry PGP Bioinformatics course better than a regular 2-year masters program?
4. Are there any prerequisites for this course?
5. What are the key highlights of this course?
6. Who is this course suitable for?
7. How can I apply for admission?
8. How can I get more information about the course?
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