6-Week Career Accelerator Program
6-Week Career Accelerator Program
AI/ML
AI/ML
&
and
Computational
Computational
Biology
Biology
Program
Programme
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 integrate multiomics data, build AI/ML models, and generate biologically meaningful predictions used in industry.
About Program
Why AI/ML & Computational Biology Programme?
Many life science learners know basic coding or ML
concepts but struggle to integrate biological data and apply AI/ML models in a biologically meaningful way.
This 6-week, hands-on program is designed for students and professionals who want to work with real multiomics
data and build interpretable AI/ML models used in modern
life science teams.
Many life science learners know basic coding or ML
concepts but struggle to integrate biological data and apply AI/ML models in a biologically meaningful way.
This 6-week, hands-on program is designed for students and professionals who want to work with real multiomics data and build interpretable AI/ML models used in modern life science teams.
Industry-Aligned Career Roles
Built for real AI/ML biology roles
Mapped to roles across systems biology teams, computational biology groups, AI-driven biotech startups, and R&D organizations.
Complete AI/ML Workflow
Learn the full modeling pipeline
From multiomics data structuring → integration → network & pathway modeling → AI/ML prediction → biological interpretation.
Mentorship + Practical Guidance
Learn to prepare model-ready datasets, evaluate predictions, and translate ML outputs into biological hypotheses that R&D teams can act on.
Portfolio-Grade Capstone Project
Build a multiomics-based predictive model, complete with notebooks, visualizations, and a clear biological narrative.
Eligibility
Who This Program Is For
This program is designed for:
Life science graduates exploring AI and ML in biology
Bioinformatics learners moving toward advanced analytics
Professionals curious about multomics and predictive modeling
Learners who want clarity before committing to long AI programs
Life science graduates exploring AI and ML in biology
Bioinformatics learners moving toward advanced analytics
Professionals curious about multomics and predictive modeling
Learners who want clarity before committing to long AI programs
Life science graduates exploring AI and ML in biology
Bioinformatics learners moving toward advanced analytics
Professionals curious about multomics and predictive modeling
Learners who want clarity before committing to long AI programs
Life science graduates exploring AI and ML in biology
Bioinformatics learners moving toward advanced analytics
Professionals curious about multomics and predictive modeling
Learners who want clarity before committing to long AI programs
Outcomes
Career Outcomes and Roles
This program aligns you toward roles such as:
Systems Biology Analyst
Computational Biologist
Bio-Data Engineer
AI & ML Analyst in Life Sciences
Systems Biology Analyst
Computational Biologist
Bio-Data Engineer
AI & ML Analyst in Life Sciences
Systems Biology Analyst
Computational Biologist
Bio-Data Engineer
AI & ML Analyst in Life Sciences
Systems Biology Analyst
Computational Biologist
Bio-Data Engineer
AI & ML Analyst in Life Sciences
Syllabus
What you'll learn
You will learn how real AI/ML 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 AI/ML 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 AI/ML 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 transition into AI/ML-driven biology and systems science roles
Hands-on AI/ML and computational biology toolkit and reproducible modeling workflows
Practical experience integrating multiomics data & building predictive models
Learn what's used latest tools to build computational models & execute functions.
Clear clarity on advanced job roles, required skills & hiring expectations in AI-driven biotech
Why are Bioinformaticians in demand,
salary-ranges & career paths
A complete capstone project, resulting in a multiomics-based, GitHub-ready predictive model
A complete capstone project, resulting in a multiomics-based, GitHub-ready predictive model
Confidence to interpret and communicate model outputs as biological insights
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