MasterFaculty of Science & Applied Sciences

Master of Data-Driven Decision Systems and Applied Statistics

Welcome, future decision-makers of the digital age. In a world awash with data, my goal is to teach you how to design the systems that not only make sense of the chaos but also use it to create a better future.

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

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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Statistical Learning, Big Data Analytics, Decision Theory, Causal Inference

Practical focus

Statistical Consulting, Data Visualization, Data Storytelling, Public Policy Analysis

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 Bank
Internships in data analysis for development projects
McKinsey & Company
Opportunities in business and data analytics
The United Nations
Internships in data for sustainable development goals

Career opportunities

Senior Data Scientist
In a tech, finance, or government
Business Intelligence Architect
Designing data systems for businesses
Policy Analyst
Using data to inform government or NGO decisions
Research Scientist
In academia or industry

Jobs and projects

Problem-Solving
Applying a systematic approach to data-driven challenges
Programming
Competency in statistical languages like R or Python
Attention to Detail
Meticulousness in data cleaning and analysis
Ethical Reasoning
Understanding the ethical implications of data use

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
- Statistical Learning: Proficiency in advanced machine learning and statistical models.
- Big Data Analytics: Skills in extracting valuable patterns from large datasets.
- Decision Theory: Applying statistical principles to make optimal decisions.
- Causal Inference: Understanding the cause-and-effect relationships in data.

Listed courses

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

Master of Data-Driven Decision Systems and Applied Statistics

01Advanced Statistical Learning
1. FoundationsFoundations of Advanced Statistical 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 Statistical Learning.
  The learner can you will become a trusted advisor in the world of data-driven decision-making, as applied to Advanced Statistical Learning.
2. MethodsMethods in Advanced Statistical Learning
  The learner can apply a method from Advanced Statistical Learning to a documented case.
  The learner can select an appropriate method from Advanced Statistical Learning for a stated problem.
3. ApplicationApplication of Advanced Statistical Learning
  The learner can evaluate a practice of Advanced Statistical Learning against a stated criterion.
  The learner can transfer Advanced Statistical Learning to a new documented context.
02Big Data Analytics and Decision Systems
1. FoundationsFoundations of Big Data Analytics and Decision Systems
  The learner can explain the core terms of Big Data Analytics and Decision Systems.
  The learner can distinguish related ideas inside Big Data Analytics and Decision Systems.
2. MethodsMethods in Big Data Analytics and Decision Systems
  The learner can apply a method from Big Data Analytics and Decision Systems to a documented case.
  The learner can select an appropriate method from Big Data Analytics and Decision Systems for a stated problem.
3. ApplicationApplication of Big Data Analytics and Decision Systems
  The learner can evaluate a practice of Big Data Analytics and Decision Systems against a stated criterion.
  The learner can transfer Big Data Analytics and Decision Systems to a new documented context.
03Causal Inference and A/B Testing
1. FoundationsFoundations of Causal Inference and A/B Testing
  The learner can explain the core terms of Causal Inference and A/B Testing.
  The learner can distinguish related ideas inside Causal Inference and A/B Testing.
2. MethodsMethods in Causal Inference and A/B Testing
  The learner can apply a method from Causal Inference and A/B Testing to a documented case.
  The learner can select an appropriate method from Causal Inference and A/B Testing for a stated problem.
3. ApplicationApplication of Causal Inference and A/B Testing
  The learner can evaluate a practice of Causal Inference and A/B Testing against a stated criterion.
  The learner can transfer Causal Inference and A/B Testing to a new documented context.
04Ethics and AI in Decision-Making
1. FoundationsFoundations of Ethics and AI in Decision-Making
  The learner can explain the core terms of Ethics and AI in Decision-Making.
  The learner can distinguish related ideas inside Ethics and AI in Decision-Making.
2. MethodsMethods in Ethics and AI in Decision-Making
  The learner can apply a method from Ethics and AI in Decision-Making to a documented case.
  The learner can select an appropriate method from Ethics and AI in Decision-Making for a stated problem.
3. ApplicationApplication of Ethics and AI in Decision-Making
  The learner can evaluate a practice of Ethics and AI in Decision-Making against a stated criterion.
  The learner can transfer Ethics and AI in Decision-Making to a new documented context.
05Capstone Project in Data-Driven Decisions
1. FoundationsFoundations of Capstone Project in Data-Driven Decisions
  The learner can explain the core terms of Capstone Project in Data-Driven Decisions.
  The learner can distinguish related ideas inside Capstone Project in Data-Driven Decisions.
2. MethodsMethods in Capstone Project in Data-Driven Decisions
  The learner can apply a method from Capstone Project in Data-Driven Decisions to a documented case.
  The learner can select an appropriate method from Capstone Project in Data-Driven Decisions for a stated problem.
3. ApplicationApplication of Capstone Project in Data-Driven Decisions
  The learner can evaluate a practice of Capstone Project in Data-Driven Decisions against a stated criterion.
  The learner can transfer Capstone Project in Data-Driven Decisions to a new documented context.
06Data Visualization Workshops
1. FoundationsFoundations of Data Visualization Workshops
  The learner can explain the core terms of Data Visualization Workshops.
  The learner can distinguish related ideas inside Data Visualization Workshops.
2. MethodsMethods in Data Visualization Workshops
  The learner can apply a method from Data Visualization Workshops to a documented case.
  The learner can select an appropriate method from Data Visualization Workshops for a stated problem.
3. ApplicationApplication of Data Visualization Workshops
  The learner can evaluate a practice of Data Visualization Workshops against a stated criterion.
  The learner can transfer Data Visualization Workshops to a new documented context.
07Statistical Consulting Case Studies
1. FoundationsFoundations of Statistical Consulting Case Studies
  The learner can explain the core terms of Statistical Consulting Case Studies.
  The learner can distinguish related ideas inside Statistical Consulting Case Studies.
2. MethodsMethods in Statistical Consulting Case Studies
  The learner can apply a method from Statistical Consulting Case Studies to a documented case.
  The learner can select an appropriate method from Statistical Consulting Case Studies for a stated problem.
3. ApplicationApplication of Statistical Consulting Case Studies
  The learner can evaluate a practice of Statistical Consulting Case Studies against a stated criterion.
  The learner can transfer Statistical Consulting Case Studies to a new documented context.
08Professional Skills for Statisticians
1. FoundationsFoundations of Professional Skills for Statisticians
  The learner can explain the core terms of Professional Skills for Statisticians.
  The learner can distinguish related ideas inside Professional Skills for Statisticians.
2. MethodsMethods in Professional Skills for Statisticians
  The learner can apply a method from Professional Skills for Statisticians to a documented case.
  The learner can select an appropriate method from Professional Skills for Statisticians for a stated problem.
3. ApplicationApplication of Professional Skills for Statisticians
  The learner can evaluate a practice of Professional Skills for Statisticians against a stated criterion.
  The learner can transfer Professional Skills for Statisticians to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

I am a statistician who believes that data is the ultimate decision-making tool. My work focuses on designing intelligent systems that are powered by advanced statistics and big data, ensuring that our decisions are based on sound, evidence-based reasoning.

Applied mentorship

I am a statistician who is passionate about using data to make real-world impacts in business and public policy. My mentorship focuses on helping students apply their skills to clinical and research problems, turning raw data into actionable 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: 'Beyond the Hype: The True Power of Big Data Analytics' - A critical look at the promises and pitfalls of big data and how statistical methods can bring clarity. · • Blog Post: 'The Art of Statistical Decision-Making' - Discusses the use of statistical models to make optimal decisions in a world of uncertainty. · • Conference Paper: 'A New Method for Causal Inference in High-Dimensional Data' - Presented at the International Conference on Machine Learning, detailing a breakthrough in understanding causality in complex systems. · • Journal Article: 'Decision-Theoretic Frameworks for Autonomous Systems' - Published in the Journal of Applied Statistics, this paper provides a framework for designing intelligent systems that can make their own decisions. · • Standard Article: 'The Rise of Data-Driven Policy in Government' - A multi-part series for a policy magazine on how statistical models are transforming public policy. · • Book: 'Data-Driven Decision Systems: Theory and Application' - A comprehensive textbook on the principles and practices of designing intelligent decision-making systems. · • Total Score: 28/30 · • Kairos Badge: 🥇

R / 02

Mentor practice lens

• Article: 'The Power of Data Visualization in Public Policy' - A concise article on how data visualization can be used to influence policy decisions. · • Guide: 'A Beginner's Guide to Statistical Consulting' - A practical tutorial for new students on the basics of a key area of applied statistics. · • Essay: 'Building Ethical AI for Public Policy' - An essay on the challenges and responsibilities of creating AI tools for government. · • Total Score: 28/30 · • Kairos Badge: 🥇

Adaptive capability

Professor superpower

GAF-powered Decision Pathway Visualization

Adaptive capability

Mentor superpower

GAF-powered Data Narrative Construction

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. Robert Ellingham

Statistical Learning, Big Data Analytics, Decision Theory, Causal Inference

Meet your professor Open the classroom

AI Super Mentor

Dr. Johanna Lindholm

Statistical Consulting, Data Visualization, Data Storytelling, Public Policy Analysis

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.

Named tuition lists Add-on services

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Master of Data-Driven Decision Systems and Applied Statistics

Academic focus

Practical focus

The World Bank

McKinsey & Company

The United Nations

Senior Data Scientist

Business Intelligence Architect

Policy Analyst

Research Scientist

Problem-Solving

Programming

Attention to Detail

Ethical Reasoning

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Robert Ellingham

Dr. Johanna Lindholm

Find the program that expands your universe.