School of Scientific Computing & Informatics · DevOps & cloud
Data Engineering & FAIR Data for Life Sciences
Life-science organisations generate more data than they can find again. This course teaches the data engineering and metadata practices that turn scattered files into a reusable, FAIR data asset.
Course overview
Data Engineering & FAIR Data covers how data move from instruments and sequencers into analysis-ready stores: ingestion, validation, columnar formats, lakehouse tables and workflow orchestration.
The FAIR half teaches metadata models, community standards and ontologies (such as MIxS, the OBO Foundry ontologies and Bioschemas), persistent identifiers and archive submission, so that data can be found, understood and reused.
Learning outcomes
By the end of the course you will be able to:
- Design ingestion and validation pipelines for laboratory and omics data
- Store data in Parquet and lakehouse tables
- Orchestrate pipelines with a workflow scheduler
- Describe datasets with standards, ontologies and identifiers
- Assess and improve the FAIRness of a data collection
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates in any science, engineering, pharmacy, medicine, agriculture, geography or computing programme (B.Sc., B.Tech., B.E., BCA, M.Sc., M.Tech., MCA, PhD)
- Researchers, faculty and working professionals in research, healthcare, biotech, pharma or IT who want to apply computing to science
- Final-year students may apply before their results are declared
Prerequisites
- Python and SQL basics
Best suited to
- Bioinformaticians and data engineers in biotech and research institutes
- Core-facility and research data managers
- Students targeting data roles in life sciences
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Instrument, sequencing and clinical data flows
- Data quality dimensions
- Data contracts
Topics
- Schemas and validation
- Incremental loads
- Handling bad records
Topics
- Parquet and partitioning
- Lakehouse table formats
- Orchestrating with a scheduler
Topics
- Minimum information standards
- Ontologies and controlled vocabularies
- Identifiers and provenance
Topics
- The FAIR principles
- Archive submission
- Measuring FAIRness
Topics
- Design
- Implementation
- Documentation
Practical sessions and projects
Capstone project
Make a dataset FAIR
Build a pipeline that ingests, validates, stores and describes a scientific dataset so that it meets the FAIR principles.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- Data in a life-science organisation
Map the data flow of a sequencing core and identify where quality and metadata are lost.
- Ingestion and validation
Build a pipeline that ingests sample sheets and rejects invalid records with clear reports.
- Storage and orchestration
Load processed results into partitioned Parquet tables and schedule the pipeline.
- Metadata and ontologies
Annotate a dataset with ontology terms and persistent identifiers.
- FAIR in practice
Assess a public dataset against FAIR criteria and fix what you can.
- Project: a FAIR data pipeline
Deliver a working pipeline that produces validated, well-described, reusable datasets.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. Recommended computer: 8 GB RAM, 30 GB free disk, Linux, macOS or Windows with WSL2.
Datasets
- Public sample metadata from BioSamples: Metadata curation.EMBL-EBI BioSamples
- Simulated instrument exports: Ingestion pipelines.Course data
Core reading
- Wilkinson MD et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3:160018.
- Kleppmann M. Designing Data-Intensive Applications. O’Reilly, 2017.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for Data Engineering & FAIR Data for Life Sciences, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for Data Engineering & FAIR Data for Life Sciences
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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