Explore Courses

This course can also be credited toward ECEA 5347, part of CU Boulder's Electrical Engineering graduate program. Rapid Prototyping is the second of three courses in the Embedded Interface Design (EID) specialization, an online version of the EID course taught on campus in the Embedded Systems Engineering graduate program. This course focuses on rapid prototyping of devices and systems and the associated methods, practices, and principles to help ensure that your embedded interface designs meet the needs and desires of users. The course includes an introduction to rapid prototyping, prototyping device and system user interfaces, device prototyping, and device design considerations and perspectives. Course content ranges from general design best practices to embedded device-specific topics covering the different types and specifics of user interfaces, but all are presented to support embedded device development. The course includes hands-on projects that allow you to try out some standard software development techniques to prototype graphical user interfaces for devices using Qt and HTML. This course may be credited toward ECEA 5347, part of CU Boulder's Master of Science in Electrical Engineering program.

This course introduces the fundamentals of high-performance and parallel computing. It is targeted to scientists, engineers, scholars, really everyone seeking to develop the software skills necessary for work in parallel software environments. These skills include big-data analysis, machine learning, parallel programming, and optimization. We will cover the basics of Linux environments and bash scripting all the way to high throughput computing and parallelizing code. We recommend you are familiar with either Fortran 90, C++, or Python to complete some of the programming assignments. After completing this course, you will be familiar with: The components of a high-performance distributed computing system Types of parallel programming models and the situations in which they might be used High-throughput computing Shared memory parallelism Distributed memory parallelism Navigating a typical Linux-based HPC environment Assessing and analyzing application scalability including weak and strong scaling Quantifying the processing, data, and cost requirements for a computational project or workflow This course can be taken for academic credit as part of CU Boulder's Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder's departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

This course may also be credited toward ECEA 5709 in the CU Boulder Master of Science in Electrical Engineering program. It is course #5 in the Power Electronics Modeling and Control specialization. This course focuses on the modeling and control of grid-operated power electronics. Upon completion of this course, you will be able to understand, analyze, model, and design low-harmonic rectifiers and inverters that connect DC loads or DC sources, such as photovoltaic arrays, to a single-phase AC grid. We strongly recommend that students complete the CU Boulder Power Electronics Specialization, as well as Courses 1 (Modeling and Simulation of Averaged Switches) and 4 (Current Mode Control) before enrolling in this course (the course numbers below are for CU Boulder MS-EE students): ● Introduction to Power Electronics (ECEA 5700) ● Converter Circuits (ECEA 5701) ● Converter Control (ECEA 5702) ● Modeling and Simulation of Averaged Switches (ECEA 5705) ● Current Mode Control (ECEA 5708) Upon completion of this course, you will be able to: ● Understand the principles of low harmonic, high power factor rectifiers and inverters ● Model and design current shaping and voltage control loops in rectifiers with correction Power Factor Correction (PFC) ● Model and design control loops in single-phase DC to AC inverters ● Design photovoltaic systems connected to a single-phase AC grid ● Use computer tools and simulators to validate rectifier and inverter designs

This course can also be credited toward ECEA 5318, part of the CU Boulder Electrical Engineering Master of Science program. The final course focuses on the hands-on creation of an application that uses real-time machine vision and multiple real-time services to synchronize internal Linux state with an external clock via observation. Compare actual performance with theoretical performance and conduct analysis to identify schedule jitter and mitigate any latency accumulation. Validation of the final project will involve comparing system timestamp logs to a large set of images that can be encoded into a movie. The final report will be peer-reviewed, and the captured images and video will be uploaded for scenario evaluation. Course learning outcomes: ● Outcome 1: Decompose a problem and a set of core real-time requirements into Linux POSIX real-time software modules and threads ● Outcome 2: Analyze services in terms of C (execution time), T (request period), and D (delivery deadline) to determine feasibility and margin for meeting requirements ● Outcome 3: Design and build a solution for a native Linux system equipped with a webcam to verify and demonstrate system synchronization using machine vision processing

This course provides students with a solid foundation in international supply chain, logistics, and foreign exchange. Foreign exchange is included in this course because it plays such a vital role in planning and executing international operations. The first half of the course examines the critical roles that supply chain management, sourcing, logistics, and transportation play in today's global business. The second half of the course focuses on the risks associated with foreign exchange and how to mitigate these risks, both financial and non-financial.

Everyday Excel Part 3 (Projects) is a continuation of Everyday Excel Parts 1 and 2. It is a project-based course in which you will apply what you have learned so far to more complex, somewhat open-ended projects (open-ended in the sense that they can be solved in more than one way). Each student will complete 3 warm-up projects (chosen from 3), 3 intermediate projects (chosen from 6), and 3 main (more complex) projects (chosen from 5). The projects have been designed to cover a wide range of interests and topic areas, and are expected to appeal to students of varying skill levels. We hope that this project-based course will greatly enhance the application of Excel tools, techniques, and functions to real-world projects.

You may be facing a world unlike any other generation. The COVID-19 public health crisis has created a climate of pervasive uncertainty, heightened anxiety, and social isolation that coincides with a critical time in your own learning and development. The COVID-19 pandemic has also exacerbated long-standing inequities and disparities, and recent events in the United States have laid bare the historical and contemporary realities of racism and systemic oppression. These challenges make the start of college unprecedentedly challenging, made even more so by the fact that our university environment is itself complex and rapidly changing. These challenges also present unique opportunities for interdisciplinary and experiential learning. This course aims to help you navigate this world and new learning environments with knowledge and skill. We designed this course using participatory methods, meaning we asked students to take a key role as partners with faculty in developing the content, structure, and learning experiences. Together, we aimed to combine faculty expertise with student perspectives and experiences to make the course material relevant to your lives. In this course, we will explore perspectives on illness and society, as well as systemic racism and inequality, with scholars from the natural and social sciences, arts and humanities, education, communications and media, and engineering. Drawing on these disciplines to examine the complex issues of a global pandemic and systemic racism will help you build conceptual and practical skills that you will use throughout your college experience and beyond. As well as exploring new and ongoing health and social crises, we will also explore the science and practice of individual and community wellbeing. You will gain skills around emotions, relationships, common mental health issues, and learning in the age of Zoom and remote learning. You will learn collaborative, compassionate, and anti-racist practices to ensure well-being in society. Welcome to the course "Health, Society, and Well-being in the Age of COVID-19"!

Suffering is an inescapable yet poorly understood feature of the human condition. This course examines how societies respond to the puzzling reality of human suffering. Among the questions it addresses are: What is suffering? What kinds of human suffering are unique to the modern world? Have the meanings and depictions of suffering changed over time? Do digital media draw our attention to the suffering of distant people? In addition to introducing students to the scholarly literature and debates on these topics, Suffering and the Human Condition will equip them with a method for studying human suffering that identifies the actors who perpetuate it.

Although all humans require care to develop and thrive, it is rarely the focus of academic studies. This course enriches the learner's understanding of this critical yet underappreciated facet of their lives by addressing such questions as: What is care? Who has traditionally provided it? How valued is care work? Does money take the care out of care work? In addition to advancing learner's knowledge of the place of care in modern society and controversies surrounding it, Thinking About Care will give learners the opportunity to learn a strategy that surveys existing approaches to care.

Computational applications involving large amounts of data—the field of data science—impact the lives of most people in the United States and around the world. This impact includes the advice that Internet systems give us, the information that is available about us online, the methods used for security and surveillance, the data used in healthcare, and much more. In many cases, these are influenced by artificial intelligence and machine learning techniques. This course examines some of the ethical issues surrounding data science, with the primary goal of making data science professionals aware and sensitive to the ethical considerations that may arise in their careers. This is accomplished by discussing ethical frameworks, examining various data science applications that give rise to ethical issues, reading current media and scholarly articles, and learning from the views and experiences of students and computing professionals. Ethical Issues in Data Science can be taken for academic credit toward the CU Boulder Master of Science in Data Science (MS-DS) program offered on Coursera. The MS-DS is an interdisciplinary degree that includes faculty from CU Boulder’s Applied Mathematics, Computer Science, Data Science, and other departments. The MS-DS program, which is outcome-based and requires no application, is ideal for individuals with broad educational and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://

This course covers the fundamentals of algorithm design and analysis, as well as array sorting algorithms, data structures such as priority queues, hash functions, and applications such as Bloom filters. Searching, Sorting, and Indexing Algorithms can be taken for credit toward the CU Boulder Master of Science in Data Science (MS-DS) program offered on Coursera. The MS-DS is an interdisciplinary degree that features faculty from CU Boulder’s Applied Mathematics, Computer Science, Information Science, and other departments. The MS-DS program, which is outcome-based and requires no application, is ideal for individuals with a broad educational background and/or professional experience in computer science, computer science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

The goal of this course is to help anyone interested in data science understand cybersecurity risks and the tools/techniques that can be used to mitigate these risks. We will review the differences between confidentiality, integrity, and availability, and introduce students to relevant cybersecurity tools and techniques, including cryptographic tools, software resources, and policies that will be needed for data science. We will examine key authentication and access control tools and techniques to help data producers, curators, and users ensure data security and privacy. This course can be taken for academic credit toward CU Boulder's Master of Science in Data Science (MS-DS) program, offered on Coursera. The MS-DS is an interdisciplinary degree that includes faculty from CU Boulder's Applied Mathematics, Computer Science, Information Science, and other departments. The MS-DS is a learning outcomes-based, no-application program that is ideal for individuals with broad educational and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.