Complexity Explorer

Introduction to Complexity (Complexity Explorer)

Offered by Santa Fe Institute,

In this re-offering of our popular introductory course, you'll learn about the tools used by scientists to understand complex systems.

The topics you'll learn about include dynamics, chaos, fractals, information theory, self-organization, agent-based modeling, and networks. You’ll also get a sense of how these topics fit together to help explain how complexity arises and evolves in nature, society, and technology. There are no prerequisites. You don't need a science or math background to take this introductory course; it simply requires an interest in the field and the willingness to participate in a hands-on approach to the subject.

Go to Class

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Coursera

EIT Digital

Geometric Algorithms (Coursera)

CS: Theory Computer Science

Course Information: In many areas of computer science such as robotics, computer graphics, virtual reality, and geographic information systems, it is necessary to store, analyze, and create or manipulate spatial data. This course deals with the algorithmic aspects of these tasks: we study techniques and concepts needed for the design and analysis of geometric algorithms and data structures. Each technique and concept will be illustrated on the basis of a problem arising in one of the application areas mentioned above.

Jun 12th 2026

3 Weeks

Algorithms Data Structures Geometric Algorithms

Coursera

University of Pennsylvania

Gamification (Coursera)

CS: Design & Product

Gamification is the application of game elements and digital game design techniques to non-game problems, such as business and social impact challenges. This course will teach you the mechanisms of gamification, why it has such tremendous potential, and how to use it effectively.

Jun 8th 2026

5-12 Weeks

Game Business Social

Coursera

University of California, San Diego,Higher School of Economics - HSE University

Data Structures (Coursera)

CS: Software Engineering CS: Theory

A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments.

Jun 8th 2026

5-12 Weeks

Algorithms Data Structures Priority Queues

Coursera

University of Illinois at Urbana-Champaign

Unordered Data Structures (Coursera)

CS: Software Engineering Computer Science

The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary.

Jun 10th 2026

4 Weeks

Algorithms Data Structures Graph Algorithms

Coursera

École Polytechnique Fédérale de Lausanne

Parallel programming (Coursera)

CS: Software Engineering CS: Programming

With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm.

Jun 8th 2026

4 Weeks

Algorithms Data Structures Parallel Computing

Coursera

Johns Hopkins University

Practical Machine Learning (Coursera)

Statistics & Data Analysis Data Science

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates.

Jun 8th 2026

4 Weeks

Algorithms Machine Learning Regression

Coursera

The State University of New York

International Cyber Conflicts (Coursera)

Security & Networking

By nature, cyber conflicts are an international issue that span across nation-state borders. By the end of the course, you will be able to apply the knowledge gained for analysis and management of international cyber incidents and conflicts including for activities such as development of policy related to cybercrime and cyberwarfare. Management of cyber incidents and conflicts requires an interdisciplinary perspective including an understanding of: 1) characteristics of the cyber threats and conflicts themselves, 2) international efforts to reduce and improve cyber security, and 3) psychological and sociopolitical factors.

Jun 8th 2026

5-12 Weeks

Networks Cybercrime Computer Science

Coursera

University of Washington

Data Manipulation at Scale: Systems and Algorithms (Coursera)

Statistics & Data Analysis Data Science

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales.

Jun 8th 2026

4 Weeks

Algebra Algorithms Databases

Coursera

DeepLearning.AI

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Coursera)

Data Science

This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow.

Jun 8th 2026

3 Weeks

Algorithms Machine Learning Neural Networks

Coursera

University of California, San Diego,Higher School of Economics - HSE University

Algorithms on Strings (Coursera)

CS: Software Engineering CS: Theory

World and internet is full of textual information. We search for information using textual queries, we read websites, books, e-mails. All those are strings from the point of view of computer science. To make sense of all that information and make search efficient, search engines use many string algorithms. Moreover, the emerging field of personalized medicine uses many search algorithms to find disease-causing mutations in the human genome.

Jun 8th 2026

4 Weeks

Algorithms Data Structures Strings

Coursera

École normale supérieure

Approximation Algorithms Part I (Coursera)

CS: Software Engineering CS: Programming

How efficiently can you pack objects into a minimum number of boxes? How well can you cluster nodes so as to cheaply separate a network into components around a few centers? These are examples of NP-hard combinatorial optimization problems. It is most likely impossible to solve such problems efficiently, so our aim is to give an approximate solution that can be computed in polynomial time and that at the same time has provable guarantees on its cost relative to the optimum.

Jun 8th 2026

5-12 Weeks

Algorithms Computer Science Linear Programming

Coursera

University of California, San Diego

Machine Learning With Big Data (Coursera)

Statistics & Data Analysis Data Science

Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

Jun 8th 2026

5-12 Weeks

Algorithms Machine Learning Big Data