Introduction to Complexity Science from Coursera

This course explores the features of complexity science. Our world is connected by an abundance of complex systems. Across all levels of organizations from physical, biological world to the social world, we may think of the connectivity between individual elements and how they interact and influence each other. For example, how humans transmit pandemics within a group, how cars interact in the traffic system and how networks connect in governmental organizations. Although these systems are diverse and different, they have surprisingly huge features in common.

In the past several decades, the study of complexity science has been increasing. It is widely acknowledged that an innovative, integrated and analytical way of thinking is essential for understanding the complex issues in the human societies. In this course, we will aim to give everyone a comprehensive introduction of the complex systems, to talk about the resilience, robustness and sustainability of the systems and to learn basic mathematical methods for complex system analysis, for example regime shifts and tipping points, the agent-based modelling, the dynamic and network theories. Most importantly, we will implement the theories into practical applications of cities and health to help students gain practice in complex systems way of thinking.

What's inside

Syllabus

Course Overview and Week 1: Introduction to Complex Systems

An overview of what is covered in the first topic: an introduction to complex systems, explaining how complexity science has evolved, how it has been applied in society, and why it is important to gain a basic understanding of complex systems. Like for all sciences, complexity science is not a spectators' sport. After learning models and methods from the lectures, you will need to try some of these out to develop a practical feel for what they mean and what they can do. This is where the Jupyter Notebook exercises come in. In this course week, we will try out two Jupyter Notebook exercises, on: (1) the Nagel-Schreckenberg model of vehicular traffic, and (2) the Game of Life.

Week 2: Robustness, Resilience, and Sustainability

In this 2nd topic, we look at how robustness, resilience and sustainability can be defined for complex systems, and some case studies that showcase these attributes.

Week 3: Regime Shifts and Tipping Points

In this third topic, we move on to looking at regime shifts and tipping points and their applications in forecasting.

Week 4: Introduction to Agent-Based Modeling

Next, we look at Agent-Based Modeling - what it is, how it works, why it is used and how to use it. We then try a Jupyter Notebook exercise on Schelling’s Segregation Model.

Week 5: Introduction to Static Complex Network

Lastly, we look at complex networks and their attributes before looking at different network models. We end this topic with a Jupyter Notebook exercise on epidemics on complex networks.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores features of complex systems, a growing study in human societies and the ability to analyze and apply innovative thinking to complex issues

Taught by Cheong Siew Ann, who is a recognized instructor in the field of complexity science

Offers a comprehensive introduction to complex systems, including resilience, robustness, and sustainability

Covers practical applications of complex systems thinking in the areas of cities and health, providing hands-on experience for students

Utilizes Jupyter Notebook exercises to help students develop a practical understanding of complex systems models and methods

Requires students to come in with some background knowledge in mathematics and computer science

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Complexity Science with these activities:

Explore Resources on Network Science

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Exploring resources on network science will supplement the course material and broaden your knowledge.

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Watch online tutorials or videos on network science concepts and applications.
Read articles or blog posts from reputable sources to gain insights into the latest advancements in network science.

Compile a Glossary of Complex Systems Terms

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Creating a glossary will improve your understanding and retention of complex systems terminology.

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Review course materials and identify key terms and concepts in complex systems.
Define and explain each term in your own words, using clear and concise language.
Organize the terms alphabetically or by category for easy reference.

Practice Jupyter Notebook Exercises

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Completing the Jupyter Notebook exercises will help you develop hands-on skills in analyzing complex systems.

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Work through the Jupyter Notebook exercise on the Nagel-Schreckenberg model of vehicular traffic.
Complete the Jupyter Notebook exercise on Schelling’s Segregation Model.

Four other activities

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Participate in Virtual Study Groups

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Participating in virtual study groups will foster collaboration and enhance your understanding of complex systems.

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Join or create a virtual study group with fellow students.
Schedule regular meetings to discuss course material, share insights, and work on assignments together.

Mentor Peers in Complex Systems Concepts

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Mentoring peers will deepen your understanding of complex systems and enhance your communication skills.

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Identify opportunities to assist fellow students in understanding complex systems concepts.
Provide clear explanations, share resources, and answer questions related to the course material.

Develop an Infographic on Complex System Applications

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Creating an infographic on complex system applications will reinforce your understanding and enhance your communication skills.

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Research case studies of complex systems applications in different fields, such as healthcare, transportation, and finance.
Identify key insights and data points that highlight the benefits and challenges of these applications.
Design and create an visually engaging infographic that presents your findings in a clear and concise manner.

Develop a Simulation Model of a Complex System

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Creating a simulation model of a complex system will provide hands-on experience in designing and analyzing such systems.

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Identify a specific complex system to model, such as a traffic network or a biological ecosystem.
Develop a conceptual model of the system, including its components, interactions, and behaviors.
Implement the model using a simulation software or programming language, such as NetLogo or Python.
Run simulations and analyze the results to gain insights into the system's behavior.

Career center

Learners who complete Introduction to Complexity Science will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists employ complex systems science in developing and implementing data-driven solutions to complex problems. An understanding of complex systems, as taught in this course, enables Data Scientists to better understand the behavior of complex systems and design interventions that can improve their performance.

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Systems Analyst

Systems Analysts use their knowledge of complex systems to identify and solve problems in complex systems. The course provides a foundation in complex systems science, which can help Systems Analysts to better understand the behavior of complex systems and identify potential problems.

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Operations Research Analyst

Operations Research Analysts use complex systems science to develop and implement solutions to complex problems in areas such as logistics, supply chain management, and healthcare. The course in Introduction to Complexity Science can help Operations Research Analysts to develop the skills needed to analyze complex systems and design effective solutions.

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Risk Analyst

Risk Analysts use complex systems science to identify, assess, and mitigate risks. The course in Introduction to Complexity Science can help Risk Analysts to develop the skills needed to understand and analyze complex systems and identify potential risks.

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Epidemiologist

Epidemiologists use complex systems science to study the spread of diseases and develop strategies to prevent and control them. The course in Introduction to Complexity Science can help Epidemiologists to develop the skills needed to understand the complex dynamics of disease transmission and develop effective strategies to control them.

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Urban Planner

Urban Planners use complex systems science to plan and develop sustainable cities. The course in Introduction to Complexity Science can help Urban Planners to develop the skills needed to understand the complex dynamics of cities and develop effective plans for their development.

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Transportation Engineer

Transportation Engineers use complex systems science to design and manage transportation systems. The course in Introduction to Complexity Science can help Transportation Engineers to develop the skills needed to understand the complex dynamics of transportation systems and design effective solutions to improve their performance.

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Environmental Engineer

Environmental Engineers use complex systems science to develop and implement solutions to environmental problems. The course in Introduction to Complexity Science can help Environmental Engineers to develop the skills needed to understand the complex dynamics of environmental systems and design effective solutions to improve their performance.

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Chemical Engineer

Chemical Engineers use complex systems science to design and operate chemical plants. The course in Introduction to Complexity Science can help Chemical Engineers to develop the skills needed to understand the complex dynamics of chemical plants and design effective solutions to improve their performance.

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Mechanical Engineer

Mechanical Engineers use complex systems science to design and operate mechanical systems. The course in Introduction to Complexity Science can help Mechanical Engineers to develop the skills needed to understand the complex dynamics of mechanical systems and design effective solutions to improve their performance.

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Electrical Engineer

Electrical Engineers use complex systems science to design and operate electrical systems. The course in Introduction to Complexity Science can help Electrical Engineers to develop the skills needed to understand the complex dynamics of electrical systems and design effective solutions to improve their performance.

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Computer Engineer

Computer Engineers use complex systems science to design and operate computer systems. The course in Introduction to Complexity Science can help Computer Engineers to develop the skills needed to understand the complex dynamics of computer systems and design effective solutions to improve their performance.

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Software Engineer

Software Engineers use complex systems science to design and develop software systems. The course in Introduction to Complexity Science may be useful for Software Engineers who want to develop a deeper understanding of the complex dynamics of software systems and design more effective software solutions.

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Web Developer

Web Developers use complex systems science to design and develop web applications. The course in Introduction to Complexity Science may be useful for Web Developers who want to develop a deeper understanding of the complex dynamics of web applications and design more effective web solutions.

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Database Administrator

Database Administrators use complex systems science to design and manage databases. The course in Introduction to Complexity Science may be useful for Database Administrators who want to develop a deeper understanding of the complex dynamics of databases and design more effective database solutions.

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