6-Week Career Accelerator Program
6-Week Career Accelerator Program
Next
Next
Gen
Gen
Genomics
Genomics
Program
Program
6Weeks
40hours
1Project
6Weeks
6Weeks
6Weeks
40hours
1Project
6Weeks
6Weeks
6Weeks
40hours
1Project
6Weeks
6Weeks
A 6-week, hands-on program for life science graduates and professionals who want to work with real NGS data, execute end-to-end genomics pipelines, and build reproducible, automation-ready workflows used in industry.
About Program
Why Genomics Pipeline Specialist Programme?
Many genomics learners know individual NGS tools but struggle to execute end-to-end, reproducible pipelines that clinical, diagnostics, and research labs actually run.
This 6-week hands-on program is designed for students and professionals who want to work with real sequencing data and understand how genomics pipelines operate in industry and lab environments.
Industry-Aligned Career Roles
Built for real genomics roles
Mapped to roles in genomics core facilities, diagnostics labs, research institutes, CROs & biotech companies.
Complete Genomics Workflow
Learn the full NGS pipeline
From raw FASTQ reads → quality control → alignment → variant calling → annotation → basic automation.
Mentorship + Practical Guidance
Learn to run pipelines on Linux, manage large genomics files & produce rerunnable, auditable workflows used in real labs.
Portfolio-Grade Capstone Project
One pipeline that proves your skill
Build an end-to-end DNA-Seq variant calling pipeline, complete with scripts, outputs
Eligibility
Who This Program Is For
This program is designed for:
Life science graduates interested in genomics roles
Bioinformatics learners who want pipeline and workflow skills
Professionals moving toward clinical genomics or sequencing teams
Anyone exploring genomics before committing to a full postgraduate track
Life science graduates interested in genomics roles
Bioinformatics learners who want pipeline and workflow skills
Professionals moving toward clinical genomics or sequencing teams
Anyone exploring genomics before committing to a full postgraduate track
Life science graduates interested in genomics roles
Bioinformatics learners who want pipeline and workflow skills
Professionals moving toward clinical genomics or sequencing teams
Anyone exploring genomics before committing to a full postgraduate track
Life science graduates interested in genomics roles
Bioinformatics learners who want pipeline and workflow skills
Professionals moving toward clinical genomics or sequencing teams
Anyone exploring genomics before committing to a full postgraduate track
Outcomes
Career Outcomes and Roles
This program aligns you toward roles such as:
Bioinformatics Pipeline Developer
Clinical Genomics Support Roles
Genomics Research Associate
NGS Operations Specialist
Bioinformatics Pipeline Developer
Clinical Genomics Support Roles
Genomics Research Associate
NGS Operations Specialist
Bioinformatics Pipeline Developer
Clinical Genomics Support Roles
Genomics Research Associate
NGS Operations Specialist
Bioinformatics Pipeline Developer
Clinical Genomics Support Roles
Genomics Research Associate
NGS Operations Specialist
Syllabus
What you'll learn
You will learn how real bioinformatics analysts work inside R&D and pharma teams also trained to :
01
Week 01
Foundations & Project Scoping
Live: Bootcamp mindset, expectations, and technical communication basics. Overview of variant-specific workflows (DGE vs DNA-Seq pipeline vs multiomics ML). Async: Core programming (Python/R foundations; variant-specific emphasis). Environment setup, Linux basics and remote/VM setup Introduction to Jupyter/RStudio and Git Capstone: Define project question and scope. Identify dataset(s) to be used. Set up Git repo and initial project structure.
01
Week 01
Foundations & Project Scoping
Live: Bootcamp mindset, expectations, and technical communication basics. Overview of variant-specific workflows (DGE vs DNA-Seq pipeline vs multiomics ML). Async: Core programming (Python/R foundations; variant-specific emphasis). Environment setup, Linux basics and remote/VM setup Introduction to Jupyter/RStudio and Git Capstone: Define project question and scope. Identify dataset(s) to be used. Set up Git repo and initial project structure.
02
WEEK 02
Data Acquisition & Quality Control
Live: Case study: messy real-world data (missing samples, poor QC) and decisions to include/exclude. Async: Data structures and key file formats (FASTQ, BAM, VCF for NGS; tables/matrices for DGE/multiomics). Data retrieval (public repositories, internal file structures) and cleaning. Capstone: Acquire and load project data. Perform basic QC and initial structuring (e.g., sample-level QC, summary stats).
02
WEEK 02
Data Acquisition & Quality Control
Live: Case study: messy real-world data (missing samples, poor QC) and decisions to include/exclude. Async: Data structures and key file formats (FASTQ, BAM, VCF for NGS; tables/matrices for DGE/multiomics). Data retrieval (public repositories, internal file structures) and cleaning. Capstone: Acquire and load project data. Perform basic QC and initial structuring (e.g., sample-level QC, summary stats).
03
WEEK 03
Core Analysis (Alignment / Statistics / Network Building)
Live: Code-along session (e.g., running alignment and interpreting mapping stats, or running DGE stats and checking assumptions). Async: Core algorithms (statistical testing or alignment or graph construction, depending on variant). Principles of reproducible analysis (scripts vs manual steps, logging). Capstone: Execute the main analysis step, B-DAP: Run DGE and generate a primary results table. A-GCPS: Complete alignment and first round of variant calling. M-AIMS: Build an initial network or first clustering/model draft.
03
WEEK 03
Core Analysis (Alignment / Statistics / Network Building)
Live: Code-along session (e.g., running alignment and interpreting mapping stats, or running DGE stats and checking assumptions). Async: Core algorithms (statistical testing or alignment or graph construction, depending on variant). Principles of reproducible analysis (scripts vs manual steps, logging). Capstone: Execute the main analysis step, B-DAP: Run DGE and generate a primary results table. A-GCPS: Complete alignment and first round of variant calling. M-AIMS: Build an initial network or first clustering/model draft.
04
WEEK 04
Intermediate Analysis & Visualization
Live: Mentoring session focused on presenting intermediate results and receiving feedback. Async: Advanced visualization (ggplot2/plotly or equivalent for clear, publication-style plots). Intro to ML principles in all variants (more depth for M-AIMS). Capstone: Refine results, create clear figures (volcano plots, coverage plots, ROC curves, networks). Draft initial narrative (results section style).
04
WEEK 04
Intermediate Analysis & Visualization
Live: Mentoring session focused on presenting intermediate results and receiving feedback. Async: Advanced visualization (ggplot2/plotly or equivalent for clear, publication-style plots). Intro to ML principles in all variants (more depth for M-AIMS). Capstone: Refine results, create clear figures (volcano plots, coverage plots, ROC curves, networks). Draft initial narrative (results section style).
05
WEEK 05
High-Value Integration: AI / Cloud / Annotation
Live: Case discussion: how cloud, AI, or annotation changes real R&D workflows. Async: Variant-specific deepening, B-DAP: Functional enrichment interpretation and storytelling. A-GCPS: Cloud concepts and an introduction to workflow managers. M-AIMS: Feature engineering and model improvement techniques. Capstone: Integrate advanced elements (annotation, ML, workflow automation). Move toward a near-final version of scripts/pipelines/models.
05
WEEK 05
High-Value Integration: AI / Cloud / Annotation
Live: Case discussion: how cloud, AI, or annotation changes real R&D workflows. Async: Variant-specific deepening, B-DAP: Functional enrichment interpretation and storytelling. A-GCPS: Cloud concepts and an introduction to workflow managers. M-AIMS: Feature engineering and model improvement techniques. Capstone: Integrate advanced elements (annotation, ML, workflow automation). Move toward a near-final version of scripts/pipelines/models.
06
WEEK 06
Capstone Finalization & Career Pathways
Live (1 hour): Career pathways overview, resume and LinkedIn positioning, interview questions aligned with each variant. Discussion of “tool agnosticism” and how to adapt when tools change. Presentation (2 hours): Each learner delivers a structured project presentation, Problem, data, workflow, results, biological/clinical implications, limitations. Q&A simulating technical and HR interview questions. Capstone: Final polishing of repo/notebook/pipeline. Optional: convert to a portfolio case study document.
06
WEEK 06
Capstone Finalization & Career Pathways
Live (1 hour): Career pathways overview, resume and LinkedIn positioning, interview questions aligned with each variant. Discussion of “tool agnosticism” and how to adapt when tools change. Presentation (2 hours): Each learner delivers a structured project presentation, Problem, data, workflow, results, biological/clinical implications, limitations. Q&A simulating technical and HR interview questions. Capstone: Final polishing of repo/notebook/pipeline. Optional: convert to a portfolio case study document.
WEEK 01
Foundations & Project Scoping
Live: Bootcamp mindset, expectations, and technical communication basics. Overview of variant-specific workflows (DGE vs DNA-Seq pipeline vs multiomics ML). Async: Core programming (Python/R foundations; variant-specific emphasis). Environment setup, Linux basics and remote/VM setup. Introduction to Jupyter/RStudio and Git Capstone: Define project question and scope. Identify dataset(s) to be used. Set up Git repo and initial project structure.
WEEK 01
Foundations & Project Scoping
Live: Bootcamp mindset, expectations, and technical communication basics. Overview of variant-specific workflows (DGE vs DNA-Seq pipeline vs multiomics ML). Async: Core programming (Python/R foundations; variant-specific emphasis). Environment setup, Linux basics and remote/VM setup. Introduction to Jupyter/RStudio and Git Capstone: Define project question and scope. Identify dataset(s) to be used. Set up Git repo and initial project structure.
WEEK 02
Data Acquisition & Quality Control
Live: Case study: messy real-world data (missing samples, poor QC) and decisions to include/exclude. Async: Data structures and key file formats (FASTQ, BAM, VCF for NGS; tables/matrices for DGE/multiomics). Data retrieval (public repositories, internal file structures) and cleaning. Capstone: Acquire and load project data. Perform basic QC and initial structuring (e.g., sample-level QC, summary stats).
WEEK 03
Core Analysis (Alignment / Statistics / Network Building)
Live: Code-along session (e.g., running alignment and interpreting mapping stats, or running DGE stats and checking assumptions). Async: Core algorithms (statistical testing or alignment or graph construction, depending on variant). Principles of reproducible analysis (scripts vs manual steps, logging). Capstone: Execute the main analysis step, B-DAP: Run DGE and generate a primary results table. A-GCPS: Complete alignment and first round of variant calling. M-AIMS: Build an initial network or first clustering/model draft.
WEEK 03
Core Analysis (Alignment / Statistics / Network Building)
Live: Code-along session (e.g., running alignment and interpreting mapping stats, or running DGE stats and checking assumptions). Async: Core algorithms (statistical testing or alignment or graph construction, depending on variant). Principles of reproducible analysis (scripts vs manual steps, logging). Capstone: Execute the main analysis step, B-DAP: Run DGE and generate a primary results table. A-GCPS: Complete alignment and first round of variant calling. M-AIMS: Build an initial network or first clustering/model draft.
WEEK 04
Intermediate Analysis & Visualization
Live: Mentoring session focused on presenting intermediate results and receiving feedback. Async: Advanced visualization (ggplot2/plotly or equivalent for clear, publication-style plots). Intro to ML principles in all variants (more depth for M-AIMS). Capstone: Refine results, create clear figures (volcano plots, coverage plots, ROC curves, networks). Draft initial narrative (results section style).
WEEK 04
Intermediate Analysis & Visualization
Live: Mentoring session focused on presenting intermediate results and receiving feedback. Async: Advanced visualization (ggplot2/plotly or equivalent for clear, publication-style plots). Intro to ML principles in all variants (more depth for M-AIMS). Capstone: Refine results, create clear figures (volcano plots, coverage plots, ROC curves, networks). Draft initial narrative (results section style).
WEEK 05
High-Value Integration: AI / Cloud / Annotation
Live: Case discussion: how cloud, AI, or annotation changes real R&D workflows. Async: Variant-specific deepening, B-DAP: Functional enrichment interpretation and storytelling. A-GCPS: Cloud concepts and an introduction to workflow managers. M-AIMS: Feature engineering and model improvement techniques. Capstone: Integrate advanced elements (annotation, ML, workflow automation). Move toward a near-final version of scripts/pipelines/models.
WEEK 05
High-Value Integration: AI / Cloud / Annotation
Live: Case discussion: how cloud, AI, or annotation changes real R&D workflows. Async: Variant-specific deepening, B-DAP: Functional enrichment interpretation and storytelling. A-GCPS: Cloud concepts and an introduction to workflow managers. M-AIMS: Feature engineering and model improvement techniques. Capstone: Integrate advanced elements (annotation, ML, workflow automation). Move toward a near-final version of scripts/pipelines/models.
WEEK 06
Capstone Finalization & Career Pathways
Live (1 hour): Career pathways overview, resume and LinkedIn positioning, interview questions aligned with each variant. Discussion of “tool agnosticism” and how to adapt when tools change. Presentation (2 hours): Each learner delivers a structured project presentation, Problem, data, workflow, results, biological/clinical implications, limitations. Q&A simulating technical and HR interview questions. Capstone: Final polishing of repo/notebook/pipeline. Optional: convert to a portfolio case study document.
WEEK 06
Capstone Finalization & Career Pathways
Live (1 hour): Career pathways overview, resume and LinkedIn positioning, interview questions aligned with each variant. Discussion of “tool agnosticism” and how to adapt when tools change. Presentation (2 hours): Each learner delivers a structured project presentation, Problem, data, workflow, results, biological/clinical implications, limitations. Q&A simulating technical and HR interview questions. Capstone: Final polishing of repo/notebook/pipeline. Optional: convert to a portfolio case study document.
Syllabus
What you'll learn
You will learn how real bioinformatics analysts work inside R&D and pharma teams also trained to :
Program Fee
Enroll Once. Become Job-Ready in 6 Weeks.
Enroll Once. Become Job-Ready in
6 Weeks.
Exclusive Cohort
Early Bird
Designed for learners building industry-ready genomics skills.
₹12,000
Inclusive of GST
Total duration: 6 weeks
Total duration: 6 weeks
Total learning hours: 39 hours
Total learning hours: 39 hours
Live sessions: 7
Live sessions: 7
Asynchronous learning: 20
Asynchronous learning: 20
Capstone work: 1
Capstone work: 1
Final presentation: 2
Final presentation: 2
What Makes This Program Different
A structured roadmap to launch a career in
bioinformatics
Hands-on computational biology toolkit,
including R, Python & reproducible workflows
Practical experience with industry-standard
omics data analysis pipelines
Learn what's used latest tools to build computational models & execute functions.
Clear clarity on job roles, required skils, and
hiring expectations in biotech and pharma
Why are Bioinformaticians in demand,
salary-ranges & career paths
A complete capstone project, resulting in a GitHub-ready portfolio
A complete capstone project, resulting in a GitHub-ready portfolio
Confidence to communicate results effectively in research & pharmaceutical environments
Bioinformatician's Most Powerful Weapon - An insider look into the Industry
Meet our learners trained on real-world AI & healthcare workflows, equipped with industry-grade skills and practical experience to deliver impact from day one.
Industry-Ready Talent
Our Alumni
Meet our learners trained on real-world AI & healthcare workflows, equipped with industry-grade skills and practical experience to deliver impact from day one.
Industry-Ready Talent
Our Alumni
Frequently asked questions
1. What is the duration of the CRA Program?
The program runs for 6 weeks in a structured, part-time format designed to fit alongside college or work commitments.
2. What is the structure of the program?
3. How is this program different from a traditional degree or certification?
4. Who is this program meant for?
5. What kind of career outcomes can participants expect?
6. What topics and tools are covered in the program?
Frequently asked questions
1. What is the duration of the CRA Program?
The program runs for 6 weeks in a structured, part-time format designed to fit alongside college or work commitments.
3. How is this program different from a traditional degree or certification?
2. What is the structure of the program?
4. Who is this program meant for?
5. What kind of career outcomes can participants expect?
6. What topics and tools are covered in the program?
Frequently asked questions
1. What is the duration of the CRA Program?
The program runs for 6 weeks in a structured, part-time format designed to fit alongside college or work commitments.
3. How is this program different from a traditional degree or certification?
2. What is the structure of the program?
4. Who is this program meant for?
5. What kind of career outcomes can participants expect?
6. What topics and tools are covered in the program?
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Copyright © 2024 Bversity school of Biotechnology. All rights reserved.
PARTNER OF
APPLICATIONS
FOLLOW US ON
Copyright © 2024 Bversity school of Biotechnology. All rights reserved.
Virtual college for Biotechnology