Hands-on quantum error correction with Google Quantum AI
This course is aimed at first year undergraduates to experienced researchers looking for an overview of quantum computing and a more detailed understanding of quantum error correction, with specific focus on the type of quantum error correction successfully demonstrated in 2024 at Google. Questions and projects are provided to build familiarity with the theory and software tools used in the field. The course starts with an overview of various quantum computing technologies, followed by detailed sections on quantum states and circuits, quantum errors and error detection, the mathematical formalism of stabilizers and the surface code, and finally the software tools Stim and Crumble, both of which are actively used by researchers in the field.
With the world population expected to hit 9.7 billion by 2050, the global food system faces growing challenges to meet demand sustainably. This course examines key issues in food and agriculture, guided by industry experts and researchers. Agriculture is a dynamic, evolving sector. Upon completing this course, you'll have a comprehensive understanding of the modern challenges and innovations shaping the journey from farm to table.
This course is part of the College of ACES suite of online programs, including the 4-week graduate-level course that can be stacked toward the Agribusiness and Sustainable Food Production Economics graduate certificate and a graduate degree. To learn more about online programs from the College of ACES and explore ways to apply your Coursera work toward a degree program at the University of Illinois, visit ACES Online at acesonline.illinois.edu.
Infrastructure Project Finance and Cash Flow Modeling
Master the concepts and analytical techniques used in modern project finance by learning how to evaluate infrastructure projects, assess risks, structure financing, and build practical financial models. This course provides a comprehensive introduction to project finance while progressively developing the modeling skills required to analyze real-world investment opportunities.
The course begins with the foundations of project finance, introducing learners to project structures, lifecycle stages, infrastructure financing principles, and the unique characteristics that distinguish project finance from traditional corporate finance. Learners will also explore key project risks and understand how these risks influence financing decisions.
As the course progresses, learners focus on risk management and financial structuring. Topics include risk identification, risk mitigation strategies, repayment mechanisms, and financing structures used to support large-scale infrastructure and development projects. Learners will understand how lenders and investors evaluate project risk when determining financing terms.
Practical modeling skills are developed through dedicated Excel-based modules covering essential functions, lookup techniques, pivot tables, and financial modeling workflows commonly used in project finance analysis. Learners will apply these skills to Build-Operate-Transfer (BOT) project models and other infrastructure financing scenarios.
Advanced sections explore financial instruments such as swaps, forward rate agreements (FRAs), and derivatives used to manage project finance risks. The course concludes with cash flow modeling, cash waterfall structures, Internal Rate of Return (IRR) analysis, and investment evaluation techniques used by project finance professionals.
What makes this course unique is its integrated approach that combines project finance theory, risk management, Excel modeling, infrastructure finance, and investment analysis into one practical learning experience. By the end of the course, learners will be able to build project finance models, evaluate project feasibility, analyze financing structures, and make informed investment decisions using industry-standard methodologies.
Introduction to C++ Programming: Build a Calculator
By the end of this project, you will be able to create a simple calculator as well as an advanced one, which will be achieved by learning the basic concepts of C++ such as variables, DataTypes, if conditions, and more programming concepts. By applying these concepts you can also create programs that users can interact with. These programming concepts can also be applied using other Programming Languages such as Java and Python, not just C++.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Introduction to Java and Object-Oriented Programming
This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.
This course is designed to introduce students, working professionals and the community to the exciting field of cybersecurity. Throughout the MOOC, participants will engage in community discourse and online interaction. Participants will gain knowledge and understanding of cybersecurity and its domains. They will engage with expertly produced videos, gain insight from industry experts, participate in knowledge assessments, practice assessing their environmental awareness, and gain access to materials that address governance and risk management, compliance, business continuity and disaster recovery, cryptography, software development security, access control, network security, security architecture, security operations, and physical and environmental security. Learning will be assessed using strategies aligned to knowledge and understanding.
You do not need prior experience in IT security to do well in this course. All you need is a willingness to learn. We will point you to relevant open and free resources to assist you.
At the end of this course, you will have the option of applying for undergraduate level course credit at Kennesaw State University (dependent on admission to Kennesaw State University as a degree seeking or a non-degree seeking student). More information is available in final module "Receive Course (undergraduate) Credit at Kennesaw State University".
The Blockchain Architecture - Intermediate Concepts course is designed for individuals with a basic understanding of digital technologies and foundational knowledge of blockchain principles and platforms. This course will delve deep into the realm of blockchain technology. Building upon the foundational knowledge obtained in the introductory course, this intermediate course offers a detailed look into security considerations, risk management strategies, and an overview of blockchain architecture. The course modules also explore the intricacies of smart contracts, legal and ethical implications of blockchain technology, and various governance models prevalent in the industry.
After completing this course, you should be able to:
- Explain the importance of security in blockchain implementation and identify potential implementation goals
- Identify the risks involved in blockchain adoption and develop a basic risk management plan
-Identify the legal and ethical implications of blockchain implementation across different industries
- Describe the function, creation, and deployment of smart contracts
- Differentiate between protocol-level and network-level security aspects
- Compare blockchain regulations in various locations globally
- Identify best practices for documenting blockchain solutions
- Describe how documentation increases solution clarity and alignment
Welcome to Motion Planning for Self-Driving Cars, the fourth course in University of Toronto’s Self-Driving Cars Specialization.
This course will introduce you to the main planning tasks in autonomous driving, including mission planning, behavior planning and local planning. By the end of this course, you will be able to find the shortest path over a graph or road network using Dijkstra's and the A* algorithm, use finite state machines to select safe behaviors to execute, and design optimal, smooth paths and velocity profiles to navigate safely around obstacles while obeying traffic laws. You'll also build occupancy grid maps of static elements in the environment and learn how to use them for efficient collision checking. This course will give you the ability to construct a full self-driving planning solution, to take you from home to work while behaving like a typical driving and keeping the vehicle safe at all times.
For the final project in this course, you will implement a hierarchical motion planner to navigate through a sequence of scenarios in the CARLA simulator, including avoiding a vehicle parked in your lane, following a lead vehicle and safely navigating an intersection. You'll face real-world randomness and need to work to ensure your solution is robust to changes in the environment.
This is an intermediate course, intended for learners with some background in robotics, and it builds on the models and controllers devised in Course 1 of this specialization. To succeed in this course, you should have programming experience in Python 3.0, and familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses) and calculus (ordinary differential equations, integration).
This course will teach the learner how to create an infographic, step-by-step. Infographics are used to visually share complex numeric or written information in one glance. The learner will start by gathering the information and goals for creating an infographic and then walk through each step of creating the infographic in Canva. By the end, the learner will have the ability to share a complete infographic.
In this 2.5-hour long project-based course, you will learn how to create and manage custom dashboards in Wrike to plan, execute and track both your personal work, team actions and multiple projects. You will use system and custom widgets, and create widgets directly from your tasks filtered list views.
This course explores the ethical implications of data analytics. It connects old ideas – privacy, surveillance, power, justice, accountability, corporate responsibility, stakeholder theory – with new technologies and cases, such as the use of machine learning to predict crime. The course will prepare you to evaluate strategic arguments about the ethics of data analytics and to relate data analytics to ethical concepts so that you approach newer, ambiguous capabilities of technology and artificial intelligence with a critical eye.
This is a self-paced lab that takes place in the Google Cloud console.
In this lab, you will explore some of the features of Cloud Healthcare API (HCAPI) to stream HL7v2 messages into HCAPI datastores and convert HL7v2 to FHIR and import the FHIR data into BigQuery for analytical use.
