MasterFaculty of Science & Applied Sciences

Master of Genomic Data Science and AI Bioanalytics

Welcome to the age of data-driven biology. We will not just study life; we will read its code, understand its logic, and learn to write new possibilities for the future of health and medicine.

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Level: Master
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

NXAcademic
Edition

The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Genomics, Bioinformatics, Systems Biology, AI in Medicine

Practical focus

Computational Genomics, Data Visualization, Clinical Bioinformatics, AI in Diagnostics

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Takeda Pharmaceutical
Internships in drug discovery and bioinformatics
The RIKEN Institute
Internships in genomic research

Career opportunities

Bioinformatician
In a pharmaceutical or biotech company
Genomic Data Scientist
Analyzing genetic data for a research institution
Clinical Data Analyst
In a hospital or medical research setting
Research Scientist
In academia or industry

Jobs and projects

Programming
Competency in languages like Python or R for biological data analysis
Problem-Solving
Tackling complex biological questions with a data-driven approach
Critical Thinking
Evaluating scientific claims and data
Collaboration
Working effectively with biologists, data scientists, and clinicians

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- You will gain a deep understanding of how to use data to improve human health, with a skill set that is in high demand in the rapidly growing field of personalized medicine and diagnostics.
Skills you build
- Genomic Analysis: Proficiency in interpreting and analyzing genomic data.
- Bioinformatics: Mastery of the software and databases used in biological research.
- Data Modeling: Building predictive models for biological systems.
- Systems Biology: Understanding how different biological components interact.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Master of Genomic Data Science and AI Bioanalytics

01Advanced Genomics and Bioinformatics
1. FoundationsFoundations of Advanced Genomics and Bioinformatics
  The learner can you will gain a deep understanding of how to use data to improve human health, with a skill set that is in high demand in the rapidly growing field of personalized medicine and diagnostics, as applied to Advanced Genomics and Bioinformatics.
  The learner can distinguish related ideas inside Advanced Genomics and Bioinformatics.
2. MethodsMethods in Advanced Genomics and Bioinformatics
  The learner can apply a method from Advanced Genomics and Bioinformatics to a documented case.
  The learner can select an appropriate method from Advanced Genomics and Bioinformatics for a stated problem.
3. ApplicationApplication of Advanced Genomics and Bioinformatics
  The learner can evaluate a practice of Advanced Genomics and Bioinformatics against a stated criterion.
  The learner can transfer Advanced Genomics and Bioinformatics to a new documented context.
02Machine Learning in Biology and Medicine
1. FoundationsFoundations of Machine Learning in Biology and Medicine
  The learner can explain the core terms of Machine Learning in Biology and Medicine.
  The learner can distinguish related ideas inside Machine Learning in Biology and Medicine.
2. MethodsMethods in Machine Learning in Biology and Medicine
  The learner can apply a method from Machine Learning in Biology and Medicine to a documented case.
  The learner can select an appropriate method from Machine Learning in Biology and Medicine for a stated problem.
3. ApplicationApplication of Machine Learning in Biology and Medicine
  The learner can evaluate a practice of Machine Learning in Biology and Medicine against a stated criterion.
  The learner can transfer Machine Learning in Biology and Medicine to a new documented context.
03Proteomics and Systems Biology
1. FoundationsFoundations of Proteomics and Systems Biology
  The learner can explain the core terms of Proteomics and Systems Biology.
  The learner can distinguish related ideas inside Proteomics and Systems Biology.
2. MethodsMethods in Proteomics and Systems Biology
  The learner can apply a method from Proteomics and Systems Biology to a documented case.
  The learner can select an appropriate method from Proteomics and Systems Biology for a stated problem.
3. ApplicationApplication of Proteomics and Systems Biology
  The learner can evaluate a practice of Proteomics and Systems Biology against a stated criterion.
  The learner can transfer Proteomics and Systems Biology to a new documented context.
04Ethical and Social Implications of Genomics
1. FoundationsFoundations of Ethical and Social Implications of Genomics
  The learner can explain the core terms of Ethical and Social Implications of Genomics.
  The learner can distinguish related ideas inside Ethical and Social Implications of Genomics.
2. MethodsMethods in Ethical and Social Implications of Genomics
  The learner can apply a method from Ethical and Social Implications of Genomics to a documented case.
  The learner can select an appropriate method from Ethical and Social Implications of Genomics for a stated problem.
3. ApplicationApplication of Ethical and Social Implications of Genomics
  The learner can evaluate a practice of Ethical and Social Implications of Genomics against a stated criterion.
  The learner can transfer Ethical and Social Implications of Genomics to a new documented context.
05Computational Tools for Biologists
1. FoundationsFoundations of Computational Tools for Biologists
  The learner can explain the core terms of Computational Tools for Biologists.
  The learner can distinguish related ideas inside Computational Tools for Biologists.
2. MethodsMethods in Computational Tools for Biologists
  The learner can apply a method from Computational Tools for Biologists to a documented case.
  The learner can select an appropriate method from Computational Tools for Biologists for a stated problem.
3. ApplicationApplication of Computational Tools for Biologists
  The learner can evaluate a practice of Computational Tools for Biologists against a stated criterion.
  The learner can transfer Computational Tools for Biologists to a new documented context.
06The Practical Guide to Clinical Genomics
1. FoundationsFoundations of The Practical Guide to Clinical Genomics
  The learner can explain the core terms of The Practical Guide to Clinical Genomics.
  The learner can distinguish related ideas inside The Practical Guide to Clinical Genomics.
2. MethodsMethods in The Practical Guide to Clinical Genomics
  The learner can apply a method from The Practical Guide to Clinical Genomics to a documented case.
  The learner can select an appropriate method from The Practical Guide to Clinical Genomics for a stated problem.
3. ApplicationApplication of The Practical Guide to Clinical Genomics
  The learner can evaluate a practice of The Practical Guide to Clinical Genomics against a stated criterion.
  The learner can transfer The Practical Guide to Clinical Genomics to a new documented context.
07Bioinformatics for Public Health
1. FoundationsFoundations of Bioinformatics for Public Health
  The learner can explain the core terms of Bioinformatics for Public Health.
  The learner can distinguish related ideas inside Bioinformatics for Public Health.
2. MethodsMethods in Bioinformatics for Public Health
  The learner can apply a method from Bioinformatics for Public Health to a documented case.
  The learner can select an appropriate method from Bioinformatics for Public Health for a stated problem.
3. ApplicationApplication of Bioinformatics for Public Health
  The learner can evaluate a practice of Bioinformatics for Public Health against a stated criterion.
  The learner can transfer Bioinformatics for Public Health to a new documented context.
08Capstone Project in Disease Diagnostics
1. FoundationsFoundations of Capstone Project in Disease Diagnostics
  The learner can explain the core terms of Capstone Project in Disease Diagnostics.
  The learner can distinguish related ideas inside Capstone Project in Disease Diagnostics.
2. MethodsMethods in Capstone Project in Disease Diagnostics
  The learner can apply a method from Capstone Project in Disease Diagnostics to a documented case.
  The learner can select an appropriate method from Capstone Project in Disease Diagnostics for a stated problem.
3. ApplicationApplication of Capstone Project in Disease Diagnostics
  The learner can evaluate a practice of Capstone Project in Disease Diagnostics against a stated criterion.
  The learner can transfer Capstone Project in Disease Diagnostics to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

I am a biologist who believes the ultimate frontier is the human genome, and computation is our map. My work focuses on developing AI-driven tools to analyze vast biological datasets, unlocking insights into disease, evolution, and the fundamental processes of life itself.

Applied mentorship

I am a bioinformatician who is passionate about using data to make real-world impacts in medicine. My mentorship focuses on helping students apply their skills to clinical and research problems, turning raw genomic data into actionable health insights.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

• Blog Post: 'Why Your Genome is the Ultimate Big Data Problem' - An accessible article on the computational challenges and opportunities presented by human genomics. · • Blog Post: 'The Next Generation of Medicine: AI-Powered Diagnostics' - Discusses the role of AI in revolutionizing disease detection and personalized treatment plans. · • Conference Paper: 'A New Method for Identifying Genetic Markers for Chronic Disease' - Presented at the International Conference on Bioinformatics, detailing a breakthrough in disease marker detection. · • Journal Article: 'Predicting Gene Regulatory Networks using Deep Learning' - Published in Nature Genetics, this paper outlines a novel approach to understanding how genes are controlled. · • Standard Article: 'The Ethical Implications of Genomic Editing' - A multi-part series for a bioethics journal on the societal and ethical questions raised by new genomic technologies. · • Book: 'The Computational Biologist's Toolkit' - A comprehensive guide to the software and algorithms used in modern bioinformatics and genomic research. · • Total Score: 28/30 · • Kairos Badge: 🥇

R / 02

Mentor practice lens

• Article: 'The Role of Bioinformatics in Personalized Medicine' - A concise article on how genomic data is used to tailor treatments to individual patients. · • Guide: 'A Beginner's Guide to Using R for Genomic Data Analysis' - A practical tutorial for new students on the basics of a key bioinformatics programming language. · • Essay: 'Building Ethical AI for Clinical Diagnostics' - An essay on the challenges and responsibilities of creating AI tools for healthcare. · • Total Score: 28/30 · • Kairos Badge: 🥇

Adaptive capability

Professor superpower

GAF-powered Genomic Pattern Recognition

Adaptive capability

Mentor superpower

GAF-powered Clinical Insight Extraction

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Victor Ashland

Genomics, Bioinformatics, Systems Biology, AI in Medicine

Meet your professor Open the classroom

AI Super Mentor

Dr. Yuki Nakamura

Computational Genomics, Data Visualization, Clinical Bioinformatics, AI in Diagnostics

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Adapted. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	12000 EUR	9600 EUR	12000 EUR
12 months · Recommended	14400 EUR	12000 EUR	14400 EUR
15 months · Standard	16800 EUR	14400 EUR	16800 EUR
18 months · Flexible	19200 EUR	16800 EUR	19200 EUR
21 months · Extended	21600 EUR	19200 EUR	21600 EUR
24 months · Part-time	24000 EUR	21600 EUR	24000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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Master of Genomic Data Science and AI Bioanalytics

Academic focus

Practical focus

Takeda Pharmaceutical

The RIKEN Institute

Bioinformatician

Genomic Data Scientist

Clinical Data Analyst

Research Scientist

Programming

Problem-Solving

Critical Thinking

Collaboration

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Victor Ashland

Dr. Yuki Nakamura

Find the program that expands your universe.