MasterFaculty of Science & Applied Sciences

Master of Mathematical Structures and Predictive Modeling

Welcome, future architects of mathematical reality. Our journey will not just be to learn about patterns, but to understand the very structures that give rise to them, from the simplest data sets to the most complex AI.

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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

Abstract Algebra, Topological Data Analysis, Category Theory, Predictive Modeling

Practical focus

Applied Mathematics, Data Visualization, Scientific Research, Educational Technology

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

The World Health Organization (WHO)
Internships in public health data modeling
McKinsey & Company
Internships in business and data analytics

Career opportunities

Research Scientist
In a university, government lab, or private research firm
AI/ML Specialist
In a tech or finance company
Quantitative Analyst
In a hedge fund or investment firm
Senior Data Scientist
In a variety of industries

Jobs and projects

Critical Thinking
Evaluating the assumptions and limitations of models
Problem-Solving
Tackling complex, multi-faceted problems
Logical Communication
Explaining complex ideas clearly and concisely
Interdisciplinary Collaboration
Working with researchers from different backgrounds

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 the skills to not only analyze data but to use it to create powerful models that can guide strategic decisions. You will become a trusted advisor in the world of data-driven decision-making.
Skills you build
- Abstract Reasoning: The ability to work with abstract concepts and formal systems.
- Predictive Modeling: Building and validating models for predictive analytics.
- Data Science: Proficiency in data analysis and machine learning.
- Mathematical Software: Mastery of software for mathematical modeling and analysis.

Listed courses

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

Master of Mathematical Structures and Predictive Modeling

01Advanced Linear Algebra for Machine Learning
1. FoundationsFoundations of Advanced Linear Algebra for Machine Learning
  The learner can you will gain the skills to not only analyze data but to use it to create powerful models that can guide strategic decisions, as applied to Advanced Linear Algebra for Machine Learning.
  The learner can you will become a trusted advisor in the world of data-driven decision-making, as applied to Advanced Linear Algebra for Machine Learning.
2. MethodsMethods in Advanced Linear Algebra for Machine Learning
  The learner can apply a method from Advanced Linear Algebra for Machine Learning to a documented case.
  The learner can select an appropriate method from Advanced Linear Algebra for Machine Learning for a stated problem.
3. ApplicationApplication of Advanced Linear Algebra for Machine Learning
  The learner can evaluate a practice of Advanced Linear Algebra for Machine Learning against a stated criterion.
  The learner can transfer Advanced Linear Algebra for Machine Learning to a new documented context.
02Topological Data Analysis
1. FoundationsFoundations of Topological Data Analysis
  The learner can explain the core terms of Topological Data Analysis.
  The learner can distinguish related ideas inside Topological Data Analysis.
2. MethodsMethods in Topological Data Analysis
  The learner can apply a method from Topological Data Analysis to a documented case.
  The learner can select an appropriate method from Topological Data Analysis for a stated problem.
3. ApplicationApplication of Topological Data Analysis
  The learner can evaluate a practice of Topological Data Analysis against a stated criterion.
  The learner can transfer Topological Data Analysis to a new documented context.
03Stochastic Processes and Predictive Modeling
1. FoundationsFoundations of Stochastic Processes and Predictive Modeling
  The learner can explain the core terms of Stochastic Processes and Predictive Modeling.
  The learner can distinguish related ideas inside Stochastic Processes and Predictive Modeling.
2. MethodsMethods in Stochastic Processes and Predictive Modeling
  The learner can apply a method from Stochastic Processes and Predictive Modeling to a documented case.
  The learner can select an appropriate method from Stochastic Processes and Predictive Modeling for a stated problem.
3. ApplicationApplication of Stochastic Processes and Predictive Modeling
  The learner can evaluate a practice of Stochastic Processes and Predictive Modeling against a stated criterion.
  The learner can transfer Stochastic Processes and Predictive Modeling to a new documented context.
04Category Theory for Data Science
1. FoundationsFoundations of Category Theory for Data Science
  The learner can explain the core terms of Category Theory for Data Science.
  The learner can distinguish related ideas inside Category Theory for Data Science.
2. MethodsMethods in Category Theory for Data Science
  The learner can apply a method from Category Theory for Data Science to a documented case.
  The learner can select an appropriate method from Category Theory for Data Science for a stated problem.
3. ApplicationApplication of Category Theory for Data Science
  The learner can evaluate a practice of Category Theory for Data Science against a stated criterion.
  The learner can transfer Category Theory for Data Science to a new documented context.
05Research Seminar in Mathematical Structures
1. FoundationsFoundations of Research Seminar in Mathematical Structures
  The learner can explain the core terms of Research Seminar in Mathematical Structures.
  The learner can distinguish related ideas inside Research Seminar in Mathematical Structures.
2. MethodsMethods in Research Seminar in Mathematical Structures
  The learner can apply a method from Research Seminar in Mathematical Structures to a documented case.
  The learner can select an appropriate method from Research Seminar in Mathematical Structures for a stated problem.
3. ApplicationApplication of Research Seminar in Mathematical Structures
  The learner can evaluate a practice of Research Seminar in Mathematical Structures against a stated criterion.
  The learner can transfer Research Seminar in Mathematical Structures to a new documented context.
06Mathematical Modeling Workshops
1. FoundationsFoundations of Mathematical Modeling Workshops
  The learner can explain the core terms of Mathematical Modeling Workshops.
  The learner can distinguish related ideas inside Mathematical Modeling Workshops.
2. MethodsMethods in Mathematical Modeling Workshops
  The learner can apply a method from Mathematical Modeling Workshops to a documented case.
  The learner can select an appropriate method from Mathematical Modeling Workshops for a stated problem.
3. ApplicationApplication of Mathematical Modeling Workshops
  The learner can evaluate a practice of Mathematical Modeling Workshops against a stated criterion.
  The learner can transfer Mathematical Modeling Workshops to a new documented context.
07Data Analysis for Scientific Research
1. FoundationsFoundations of Data Analysis for Scientific Research
  The learner can explain the core terms of Data Analysis for Scientific Research.
  The learner can distinguish related ideas inside Data Analysis for Scientific Research.
2. MethodsMethods in Data Analysis for Scientific Research
  The learner can apply a method from Data Analysis for Scientific Research to a documented case.
  The learner can select an appropriate method from Data Analysis for Scientific Research for a stated problem.
3. ApplicationApplication of Data Analysis for Scientific Research
  The learner can evaluate a practice of Data Analysis for Scientific Research against a stated criterion.
  The learner can transfer Data Analysis for Scientific Research to a new documented context.
08Professional Skills for Applied Mathematicians
1. FoundationsFoundations of Professional Skills for Applied Mathematicians
  The learner can explain the core terms of Professional Skills for Applied Mathematicians.
  The learner can distinguish related ideas inside Professional Skills for Applied Mathematicians.
2. MethodsMethods in Professional Skills for Applied Mathematicians
  The learner can apply a method from Professional Skills for Applied Mathematicians to a documented case.
  The learner can select an appropriate method from Professional Skills for Applied Mathematicians for a stated problem.
3. ApplicationApplication of Professional Skills for Applied Mathematicians
  The learner can evaluate a practice of Professional Skills for Applied Mathematicians against a stated criterion.
  The learner can transfer Professional Skills for Applied Mathematicians to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

I am a mathematician who believes that the most powerful predictive models are rooted in elegant and abstract mathematical structures. My work focuses on advancing our knowledge of these structures and applying them to solve complex problems in AI and scientific research.

Applied mentorship

I am an applied mathematician with a passion for using mathematical models to solve real-world problems. My mentorship is focused on helping students bridge the gap between abstract theory and practical application, preparing them for a successful career in data science and research.

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 Abstract Algebra is the New AI' - An accessible essay on how advanced mathematical concepts like group theory and ring theory are becoming crucial for the next generation of AI. · • Blog Post: 'The Topology of Big Data' - Discusses how topological data analysis can be used to find hidden patterns and connections in high-dimensional data sets. · • Conference Paper: 'A Categorical Approach to Neural Network Architectures' - Presented at the International Conference on AI and Mathematics, detailing a novel way to design neural networks using category theory. · • Journal Article: 'Homological Invariants for Machine Learning' - Published in the Journal of Applied Mathematics, this paper introduces a new set of mathematical tools for analyzing and improving machine learning models. · • Standard Article: 'A Look at the Future of Predictive Modeling in the Digital Age' - A multi-part series for a tech magazine on how mathematical models are transforming everything from finance to medicine. · • Book: 'Mathematical Structures for Modern AI' - A comprehensive textbook on the theoretical foundations of AI and data science. · • Total Score: 28/30 · • Kairos Badge: 🥇

R / 02

Mentor practice lens

• Article: 'The Power of Mathematical Visualization in Scientific Research' - A short, impactful piece on how mathematical visualization can lead to new scientific breakthroughs. · • Guide: 'Using R and Python for Advanced Mathematical Modeling' - A practical tutorial for new students on the core software tools of the profession. · • Essay: 'The Human Side of AI: The Role of Ethics in Predictive Modeling' - An essay on why ethical reasoning is as important as technical skills in an AI professional's role. · • Total Score: 28/30 · • Kairos Badge: 🥇

Adaptive capability

Professor superpower

GAF-powered Abstract-to-Applied Translation

Adaptive capability

Mentor superpower

GAF-powered Algorithmic Insight

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. Edward Langridge

Abstract Algebra, Topological Data Analysis, Category Theory, Predictive Modeling

Meet your professor Open the classroom

AI Super Mentor

Dr. Beatriz Salazar

Applied Mathematics, Data Visualization, Scientific Research, Educational Technology

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 Mathematical Structures and Predictive Modeling

Academic focus

Practical focus

The World Health Organization (WHO)

McKinsey & Company

Research Scientist

AI/ML Specialist

Quantitative Analyst

Senior Data Scientist

Critical Thinking

Problem-Solving

Logical Communication

Interdisciplinary Collaboration

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Edward Langridge

Dr. Beatriz Salazar

Find the program that expands your universe.