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Course 2023-2024 a.y.


Department of Social and Political Sciences

Course taught in English

Go to class group/s: 31

DSBA (8 credits - II sem. - OP  |  SECS-S/04)
Course Director:

Classes: 31 (II sem.)

Synchronous Blended: Lezioni erogate in modalità sincrona in aula (max 1 ora per credito online sincrona)

Mission & Content Summary

This course offers a comprehensive exploration of modeling and simulation techniques within the realm of Data Science. It places emphasis on population dynamic modeling, agent-based modeling (ABM), and the foundational principles of network science. Students will develop crucial skills for analyzing and interpreting intricate systems, enhancing their ability to navigate decision-making processes. The practical applications of these methodologies span across public and private sectors, allowing for an in-depth exploration of underlying mechanisms in diverse processes. Moreover, students will learn to formulate projections for future scenarios based on alternative assumptions. The course provides innovative tools drawn from the fields of demography, epidemiology, and network science, empowering individuals to effectively describe, assess, and address complex choices, ultimately leading to the identification of optimal solutions.


This course provides a comprehensive exploration of concepts and methodologies in decision analysis and modeling, emphasizing their expanding applications across various firms and organizations. The key objectives of the course include:

  1. Understand the Foundations:
  • Explore the theoretical foundations of modeling and simulation.
  • Acquire a comprehensive understanding of population dynamic modeling, agent-based modeling, and network science principles.

2. Population Dynamic Modeling:

  • Introduce techniques for modeling dynamic changes within populations.
  • Address applications of population dynamic models in data-driven scenarios.

3.Agent-Based Modeling (ABM):


  • Develop a strong grasp of the principles of agent-based modeling.
  • Apply ABM to simulate interactions and behaviors within complex systems.

4. Network Science Basics:

  • Introduce fundamental concepts of network science.
  • Understand the role of networks in data science applications.

5. Application of Models:

  • Apply population dynamic models to analyze and predict demographic trends.
  • Utilize agent-based models for simulating individual behaviors and interactions.
  • Explore network science applications in real-world scenarios.

6. Integration with Data Science:

  • Integrate modeling and simulation techniques into the broader context of data science.
  • Explore ways to leverage models for predictive analytics and decision-making.

7. Hands-On Experience:

  • Provide hands-on experience with relevant tools and software for implementing models.
  • Engage in practical exercises and projects to reinforce theoretical concepts.

8. Project Work:

  • Collaborate on a final project applying learned techniques to a real-world data science problem.
  • Present findings and solutions, demonstrating proficiency in modeling and simulation.

9. Critical Analysis:

  • Foster critical thinking skills in evaluating the appropriateness of different modeling techniques.
  • Encourage thoughtful analysis of simulation outcomes and their implications.

Intended Learning Outcomes (ILO)
At the end of the course student will be able to...
  • Develop the ability to scrutinize complex systems using modeling and simulation techniques.
  • Utilize acquired skills in population dynamic modeling, agent-based modeling, and network science to practical data science scenarios.
  •  Demonstrate proficiency in navigating decision-making processes within the context of data-driven analyses.
  • Investigate underlying mechanisms in diverse processes, allowing for a comprehensive exploration of real-world applications.
  • Develop the capacity to formulate projections for future scenarios based on alternative assumptions.
  • Acquire innovative tools from demography, epidemiology, and network science to address complex choices effectively.
  • Describe: Articulate insights into intricate systems and their components using appropriate terminology.
  • Assess: Evaluate the impact of various modeling techniques on the analysis of data science problems.
  • Identify: Recognize optimal solutions by identifying patterns and trends in simulated scenarios.
  • Empower: Gain the confidence to apply modeling and simulation methodologies in both public and private sectors.
At the end of the course student will be able to...
  • Apply Modeling Techniques: Apply advanced modeling and simulation techniques to address and resolve complex challenges encountered in professional practice.
  • Utilize Data Science Skills: Utilize acquired skills in population dynamic modeling, agent-based modeling, and network science to analyze and solve real-world problems in a professional context.
  • Integrate Decision-Making Skills: Integrate modeling and simulation skills into decision-making processes, demonstrating the ability to navigate and contribute to data-driven decision frameworks.
  • Formulate Informed Projections: Formulate informed projections for future scenarios by applying alternative assumptions and considering various factors.
  • Evaluate and Adapt: Evaluate the effectiveness of different modeling techniques and adapt them to diverse data science problems encountered in professional practice.
  • Apply Soft Skills: Apply soft skills such as effective teamwork, clear communication, and collaboration when working on modeling and simulation projects in professional settings.
  • Demonstrate Analytical Thinking: Demonstrate analytical thinking by recognizing patterns and trends in simulated scenarios, leading to the identification of optimal solutions.
  • Communicate Effectively: Communicate insights into complex systems and modeling outcomes clearly and effectively to diverse stakeholders in a professional context.
  • Exhibit Confidence: Exhibit confidence in the application of modeling and simulation methodologies across public and private sectors, contributing to effective problem-solving.
  • Translate Theory to Practice: Translate theoretical knowledge into practical solutions, demonstrating the ability to solve real-world problems encountered in professional practice.

Teaching methods
  • Face-to-face lectures
  • Guest speaker's talks (in class or in distance)
  • Exercises (exercises, database, software etc.)
  • Individual assignments
  • Group assignments
  • Interactive class activities on campus/online (role playing, business game, simulation, online forum, instant polls)

Classical face-to-face lectures focus on the presentation and the discussion of Modelling and Simulation techniques covered by the course, with a main attention to methodology, theory, and possible applications. To improve the learning experience and motivate the interaction, illustrative case studies and in-class exercises may also be considered.


A series of lab sessions, with the students working on their own laptop, are also provided. These classes, typically (but not always), consist of two main parts:

  1. The students are guided in the implementation of Modelling and Simulation techniques on Python
  2. After the guided implementation, students are asked to finish the proposed tasks during class and/or independently at home (individual assignment)

For each lab session, students are provided with a Notebook Jupiter file with instructions on the exercises to be done in class and revised at home. The solution of each practical session is provided after the class.

One Group Assignment is required throughout the course. The assignment aims to develop a comprehensive simulation project, creating and presenting a model-based solution to a complex problem, or conducting a joint analysis of a dataset. This approach fosters teamwork, communication, and shared problem-solving skills.



Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
  • Group assignment (report, exercise, presentation, project work etc.)
  •   x  

    Assessment is based on:

    A group project (30%):

    • The project can be submitted once only. The maximum grade available to students who do not submit a project is 21/30. Max four people per group

     A written exam (i.e. 70%):

    • The exam consists of some programming and some short open questions.  The exam covers material covered in the lectures, computer classes, in the text books and other set of readings provided by the Professor. The exam can be done in one shot at the end of the course or through two partial exams.
    • xam can be done in one shot at the end of the course or through two partial exams.

    Teaching materials

    Programming codes, reading materials and selected chapters of relevant books are uploaded on the e-learning platform. 

    Last change 11/12/2023 15:33