Coursera

Blockchain Scalability and its Foundations in Distributed Systems (Coursera)

Offered by The University of Sydney,

Blockchain promises to disrupt industries once it will be efficient at large scale. In this course, you will learn how to make blockchain scale. You will learn about the foundational problem of distributed computing, consensus, that is key to create blocks securely. By illustrating limitations of mainstream blockchains, this course will indicate how to improve the technology in terms of security and efficiency. In particular, this course will help you: understand security vulnerabilities of mainstream blockchains; design consensus algorithms that tolerate attacks, and; design scalable blockchain systems.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

Syllabus

WEEK 1
Consensus in blockchain
This week, we will start our exploration of the blockchain and its relation to distributed computing. In particular, we will learn the basics on blockchain abstraction and the importance of consensus between machines in order to mitigate the risks of having an attacker spend the same asset twice - an undesirable situation called 'double-spending'.

WEEK 2
Blockchain fundamentals
This week we will learn how classic blockchains try to solve consensus. We will look at the failures and delays blockchain systems must consider, and how these events may prevent machines from reaching consensus.

WEEK 3
Consensus fundamentals
This week, we will investigate how to solve the consensus problem. To do this, we will first investigate how to solve consensus when failures cannot occur in the network. Later on, we will learn how to solve consensus when processes may fail, first through crash failures and then when processes can behave arbitrarily.

WEEK 4
Making blockchains secure
This week, we will investigate how delays in the network can impact the security of the blockchain. We will illustrate this using network attacks that allow hackers to steal digital assets in the past and that every blockchain designer should be aware of.

WEEK 5
Making blockchains scale
This week, we will explore how to design a scalable and secure blockchain algorithm. What makes a blockchain algorithm scalable and secure? To be secure, we need a consensus algorithm that works over the Internet and is not affected by unexpected communication delays. To be scalable, we need to get rid of the bottlenecks.

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

University of Illinois at Urbana-Champaign

Cloud Computing Applications, Part 1: Cloud Systems and Infrastructure (Coursera)

Security & Networking

Welcome to the Cloud Computing Applications course, the first part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data! In this first course we cover a multitude of technologies that comprise the modern concept of cloud computing. Cloud computing is an information technology revolution that has just started to impact many enterprise computing systems in major ways, and it will change the face of computing in the years to come.

Jun 1st 2026

4 Weeks

Cloud Web Services Cloud Computing

Coursera

INSEAD

Introduction to Blockchain Technologies (Coursera)

Business

In this first course of the specialization, we will discuss the limitations of the Internet for business and economic activity, and explain how blockchain technology represents the way forward. After completing this course, you will be able to explain what blockchain is, how it works, and why it is revolutionary.

Jun 3rd 2026

5-12 Weeks

Business Cryptography Mining

Coursera

Northeastern University

Data Privacy Fundamentals (Coursera)

Security & Networking

This course is designed to introduce data privacy to a wide audience and help each participant see how data privacy has evolved as a compelling concern to public and private organizations as well as individuals. In this course, you will hear from legal and technical experts and practitioners who encounter data privacy issues daily.

Jun 3rd 2026

3 Weeks

Security Algorithms Ethics

Coursera

University of California, San Diego

Mastering the Software Engineering Interview (Coursera)

CS: Software Engineering

You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice!

Jun 1st 2026

4 Weeks

Algorithms Object-Oriented Software Engineering

Coursera

University of Illinois at Urbana-Champaign

Cluster Analysis in Data Mining (Coursera)

Statistics & Data Analysis Data Science

Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.

Jun 1st 2026

4 Weeks

Algorithms Clustering Data Mining

Coursera

Princeton University

Algorithms, Part I (Coursera)

CS: Software Engineering CS: Theory

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

Jun 1st 2026

5-12 Weeks

Programming Java Algorithms

Coursera

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

Advanced Algorithms and Complexity (Coursera)

CS: Software Engineering CS: Theory

You've learned the basic algorithms now and are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision.

Jun 1st 2026

5-12 Weeks

Algorithms Data Structures Machine Learning

Coursera

Stanford University

Shortest Paths Revisited, NP-Complete Problems and What To Do About Them (Coursera)

CS: Software Engineering CS: Theory

The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).

Jun 1st 2026

4 Weeks

Algorithms Data Structures Dynamic Programming

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 3rd 2026

4 Weeks

Algorithms Data Structures Graph Algorithms

Coursera

University of Minnesota

Nearest Neighbor Collaborative Filtering (Coursera)

Data Science

In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user.

Jun 1st 2026

4 Weeks

Algorithms Machine Learning Data Science

Coursera

University of Michigan

Securing Digital Democracy (Coursera)

Security & Networking

In this course, you'll learn what every citizen should know about the security risks--and future potential — of electronic voting and Internet voting. We'll take a look at the past, present, and future of election technologies and explore the various spaces intersected by voting, including computer security, human factors, public policy, and more.

Jun 1st 2026

5-12 Weeks

Digital Security Operating Systems

Coursera

Duke University

Java Programming: Solving Problems with Software (Coursera)

CS: Software Engineering CS: Programming

Learn to code in Java and improve your programming and problem-solving skills. You will learn to design algorithms as well as develop and debug programs. Using custom open-source classes, you will write programs that access and transform images, websites, and other types of data. At the end of the course you will build a program that determines the popularity of different baby names in the US over time by analyzing comma separated value (CSV) files.

Jun 1st 2026

4 Weeks

Programming Java Algorithms