MasterFaculty of Social Sciences & Humanities

Computational Sociology and Social Data Science (M.Sc.)

Benvenuti! The world is a system waiting to be modeled. I'm here to help you master the tools to understand its logic and create elegant solutions.

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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

Social Theory, Social Networks, Urban Sociology, Social Analytics

Practical focus

Mathematical Modeling, Data Analysis, Operations Research, Optimization Algorithms

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

McKinsey & Company
Opportunities in business and data analytics
European Space Agency (ESA)
Internships in satellite mission planning

Career opportunities

Data Scientist
In tech, finance, or government
Urban Planner
Using data to design and improve cities
Policy Analyst
Using data to inform government or NGO decisions
Research Scientist
In academia or industry

Jobs and projects

Problem-Solving
Tackling complex, multi-faceted problems
Programming
Competency in languages like Python or R
Critical Thinking
Evaluating the assumptions and limitations of models
Communication
Explaining complex social concepts to a lay audience

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
- Social Theory: Understanding the history of sociological thought.
- Social Networks: Analyzing the structure and dynamics of social networks.
- Data Analysis: Interpreting and using data to inform social models.
- Urban Sociology: Understanding the social dynamics of urban spaces.

Listed courses

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

Computational Sociology and Social Data Science (M.Sc.)

01Advanced Social Theory
1. FoundationsFoundations of Advanced Social Theory
  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 Social Theory.
  The learner can you will become a trusted advisor in the world of data-driven decision-making, as applied to Advanced Social Theory.
2. MethodsMethods in Advanced Social Theory
  The learner can apply a method from Advanced Social Theory to a documented case.
  The learner can select an appropriate method from Advanced Social Theory for a stated problem.
3. ApplicationApplication of Advanced Social Theory
  The learner can evaluate a practice of Advanced Social Theory against a stated criterion.
  The learner can transfer Advanced Social Theory to a new documented context.
02Computational Sociology and Social Network Analysis
1. FoundationsFoundations of Computational Sociology and Social Network Analysis
  The learner can explain the core terms of Computational Sociology and Social Network Analysis.
  The learner can distinguish related ideas inside Computational Sociology and Social Network Analysis.
2. MethodsMethods in Computational Sociology and Social Network Analysis
  The learner can apply a method from Computational Sociology and Social Network Analysis to a documented case.
  The learner can select an appropriate method from Computational Sociology and Social Network Analysis for a stated problem.
3. ApplicationApplication of Computational Sociology and Social Network Analysis
  The learner can evaluate a practice of Computational Sociology and Social Network Analysis against a stated criterion.
  The learner can transfer Computational Sociology and Social Network Analysis to a new documented context.
03Data Science for Sociologists
1. FoundationsFoundations of Data Science for Sociologists
  The learner can explain the core terms of Data Science for Sociologists.
  The learner can distinguish related ideas inside Data Science for Sociologists.
2. MethodsMethods in Data Science for Sociologists
  The learner can apply a method from Data Science for Sociologists to a documented case.
  The learner can select an appropriate method from Data Science for Sociologists for a stated problem.
3. ApplicationApplication of Data Science for Sociologists
  The learner can evaluate a practice of Data Science for Sociologists against a stated criterion.
  The learner can transfer Data Science for Sociologists to a new documented context.
04Research Seminar in Social Analytics
1. FoundationsFoundations of Research Seminar in Social Analytics
  The learner can explain the core terms of Research Seminar in Social Analytics.
  The learner can distinguish related ideas inside Research Seminar in Social Analytics.
2. MethodsMethods in Research Seminar in Social Analytics
  The learner can apply a method from Research Seminar in Social Analytics to a documented case.
  The learner can select an appropriate method from Research Seminar in Social Analytics for a stated problem.
3. ApplicationApplication of Research Seminar in Social Analytics
  The learner can evaluate a practice of Research Seminar in Social Analytics against a stated criterion.
  The learner can transfer Research Seminar in Social Analytics to a new documented context.
05The Ethics of Social Data
1. FoundationsFoundations of The Ethics of Social Data
  The learner can explain the core terms of The Ethics of Social Data.
  The learner can distinguish related ideas inside The Ethics of Social Data.
2. MethodsMethods in The Ethics of Social Data
  The learner can apply a method from The Ethics of Social Data to a documented case.
  The learner can select an appropriate method from The Ethics of Social Data for a stated problem.
3. ApplicationApplication of The Ethics of Social Data
  The learner can evaluate a practice of The Ethics of Social Data against a stated criterion.
  The learner can transfer The Ethics of Social Data to a new documented context.
06Optimization Workshops
1. FoundationsFoundations of Optimization Workshops
  The learner can explain the core terms of Optimization Workshops.
  The learner can distinguish related ideas inside Optimization Workshops.
2. MethodsMethods in Optimization Workshops
  The learner can apply a method from Optimization Workshops to a documented case.
  The learner can select an appropriate method from Optimization Workshops for a stated problem.
3. ApplicationApplication of Optimization Workshops
  The learner can evaluate a practice of Optimization Workshops against a stated criterion.
  The learner can transfer Optimization Workshops to a new documented context.
07Data Modeling Case Studies
1. FoundationsFoundations of Data Modeling Case Studies
  The learner can explain the core terms of Data Modeling Case Studies.
  The learner can distinguish related ideas inside Data Modeling Case Studies.
2. MethodsMethods in Data Modeling Case Studies
  The learner can apply a method from Data Modeling Case Studies to a documented case.
  The learner can select an appropriate method from Data Modeling Case Studies for a stated problem.
3. ApplicationApplication of Data Modeling Case Studies
  The learner can evaluate a practice of Data Modeling Case Studies against a stated criterion.
  The learner can transfer Data Modeling Case Studies 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 sociologist who believes that society is a complex system of interconnected individuals and institutions. My work focuses on using modern data analysis techniques to understand the structures and dynamics of society, from global institutions to everyday interactions.

Applied mentorship

I am a data modeler who is passionate about using mathematical principles 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 operations 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: 'The Power of Social Network Analysis in the Digital Age' - An accessible essay on how social network analysis can be used to understand the flow of information and influence in society. · • Blog Post: 'Why Every Sociologist Needs a Mathematical Model' - Discusses how mathematical models are crucial for understanding and addressing social issues. · • Conference Paper: 'A New Method for Modeling the Impact of Social Media on Political Polarization' - Presented at the International Conference on Social Informatics, detailing a breakthrough in understanding the social dynamics of online behavior. · • Journal Article: 'The Role of Urban Spaces in Social Inequality' - Published in the Journal of Urban Sociology, this paper provides a framework for understanding how urban design can impact social inequality. · • Standard Article: 'A Look at the Future of Social Analytics in Public Policy' - A multi-part series for a policy magazine on how data and AI are transforming public policy. · • Book: 'The Social Network: A Guide to Social Analytics' - A comprehensive textbook on the theoretical and practical applications of social network analysis. · • Total Score: 28/30 · • Kairos Badge: 🥇

R / 02

Mentor practice lens

• Article: 'The Power of Mathematical Modeling in Everyday Life' - A short, impactful piece on how mathematical models are used to solve common problems, from traffic jams to grocery store layouts. · • Guide: 'A Beginner's Guide to Optimization Algorithms' - A practical tutorial for new students on the basics of a key area of applied mathematics. · • Essay: 'The Art of Data Storytelling for Applied Mathematicians' - An essay on why communication and visualization are crucial skills for anyone who builds mathematical models. · • Total Score: 28/30 · • Kairos Badge: 🥇

Adaptive capability

Professor superpower

GAF-powered Systemic Prediction

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. Marco Gallo

Social Theory, Social Networks, Urban Sociology, Social Analytics

Meet your professor Open the classroom

AI Super Mentor

Dr. Trevor Huxley

Mathematical Modeling, Data Analysis, Operations Research, Optimization Algorithms

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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Computational Sociology and Social Data Science (M.Sc.)

Academic focus

Practical focus

McKinsey & Company

European Space Agency (ESA)

Data Scientist

Urban Planner

Policy Analyst

Research Scientist

Problem-Solving

Programming

Critical Thinking

Communication

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Marco Gallo

Dr. Trevor Huxley

Find the program that expands your universe.