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Launching a career in music is an exhilarating and transformative journey that requires passion, dedication, and a strong commitment to artistic growth. This course is designed to equip musicians with the knowledge and skills to navigate the business side of their music career. This course covers a range of essential topics that will empower musicians to take control of their artistic journey and thrive in the industry. In this course, you will gain invaluable insights into effectively managing a successful gigging career. From identifying suitable venues to taking charge of bookings, you will learn the essential skills needed to navigate the dynamic world of live performances. By utilizing the Business Model Canvas template, you will develop a strategic approach to planning and executing your career goals, ensuring you stay on track and achieve long-term success. Setting up, promoting, and growing your teaching studio is another essential aspect covered in the course. Learn how to establish a thriving teaching practice, identify your Unique Selling Proposition (USP), attract and retain students, and utilize marketing techniques to promote your teaching services. After completing this course, you have the skills and knowledge necessary to become a self-sufficient music entrepreneur and to unleash your full potential as a musician in today's dynamic music industry. This course can be taken by itself or as part of the “Musician's Professional Toolbox” specialization.

Are you a musician with big dreams and a passion for creating music? Are you ready to take your musical career to the next level? If so, the “Musician's Professional Toolbox” may be just what you need! In this specialization you will learn career development skills and tools for freelance musicians. Gain critical skills in self-assessment, branding, marketing, promotion, gigging, and teaching studio setup. Define your artistic vision, set meaningful goals, and expand your professional network. From personal and artistic development to branding, promotion, and strategic career management, you'll come away from this sequence of courses equipped with the tools to successfully navigate the music industry. Applied learning project Learners will produce professional materials to help launch and support careers as musicians, including cover letters, resumes, and headshots. Additional assignments allow learners to prepare to network successfully, build and maintain clientele, develop a personal brand and mission statement, and more.

This course can also be credited as ECEA 5612, part of the CU Boulder electrical engineering master's program. This course examines commonly used approximation methods in quantum mechanics. These include time-independent perturbation theory, time-dependent perturbation theory, the tight-binding method, the variational method, and the use of a finite set of bases. In each case, a specific example is given to illustrate how the method works. At the end of this course, students will be able to: 1. use time-dependent perturbation theory to obtain first- and second-order corrections to energies and wave functions; 2. use time-dependent perturbation theory to obtain transition rates; 3. use the tight coupling method, the variational method, and finite basis sets to obtain approximate solutions to various problems in quantum mechanics.

This course can also be credited as ECEA 5611, part of the CU Boulder electrical engineering master's program. This course introduces the quantum mechanical concept of the angular momentum operator and its relationship to the rotation operator. The course then introduces angular momentum operators, their eigenvalues, and eigenfunctions. Finally, the theory of angular momentum addition is reviewed. Upon completion of this course, students will be able to: 1. describe and analyze angular momentum states using quantum-mechanically defined angular momentum operators, 2. solve eigenvalues ​​of angular momentum, and 3. add angular momenta quantum-mechanically.

This course may also be credited toward ECEA 5610, part of the CU Boulder electrical engineering master's degree program. This course covers fundamental concepts and topics in quantum mechanics, including basic concepts, one-dimensional potential problems, time evolution of quantum states, and basic linear algebra. It provides undergraduate-level foundational knowledge and builds on it to more advanced topics. Upon completion of this course, students will be able to: 1. demonstrate a thorough understanding of the fundamental concepts of quantum mechanics, including wave-particle duality, operators and wave functions, and the evolution of quantum states; 2. master the mathematical apparatus necessary to study quantum mechanics; 3. acquire the fundamental knowledge necessary to study more advanced quantum mechanics and its applications.

This specialization is designed for engineers who want to gain a fundamental understanding of quantum mechanics, which is the basis of modern electrical, mechanical, and quantum engineering. Over the course of three courses, you will learn (1) basic concepts such as superposition and entanglement of quantum states, measurement in quantum mechanics, and the uncertainty principle, (2) the mathematical tools needed to describe and manipulate quantum states, (3) an advanced theory of angular coordinates, tools needed to describe and manipulate quantum states, (3) an advanced theory of angular momentum, and (4) approximation techniques that are widely applicable in many areas. Applied learning project Throughout all courses, you will complete homework assignments that will allow you to practice and test your knowledge. Additionally, each course contains at least one peer-reviewed assignment that requires you to delve deeper into the material. There are no project-based assignments in this specialization.

In this course, you will learn how to analyze data in terms of process stability and statistical control, and why having a stable process is essential before conducting statistical hypothesis testing. You will plot statistical process control graphs for continuous and discrete data using R. You will analyze data sets for statistical control using probability-based control rules. Additionally, you will learn how to evaluate a process in terms of its ability to meet specifications, both internal and external, and make decisions about process improvement. This course can be taken for 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.

This course is an introduction to Viking religion as recorded in Old Norse and Scandinavian literature from the medieval period. You will learn about the various written sources and what they can teach us about pre-Christian religion in Northern Europe during the Viking Age. The course covers the main collection of Old Norse poetry about the pre-Christian Norse gods, the Poetic Edda. We will also work with the main collections of stories about the Old Norse gods in prose: Snorri Sturluson's Edda and Saxo's History of the Danes.

In this course, you will learn how to build models for decision making. We will start with cluster analysis, a data reduction technique that is very useful for market segmentation. You will learn the basics of Monte Carlo simulation, which is useful for modeling. A key element of decision making is identifying the best course of action. You will learn how it helps you identify. The fact is that you do not need to know any language or advanced statistics. Analytic Solver Platform Analytics Solver Platform

WC Boulder Developing the industrial infrastructure of the future is a critical task. This course provides a practical look at the consumer IoT to highlight the broader space for potential and growth in embedded systems applications. The Industrial Internet of Things (IIoT) is also known as Industry 4.0. As Cisco's CEO put it: “The IoT is a $19 trillion market. The IIoT is a critical subset that includes the digital oilfield, advanced manufacturing, grid automation, and smart cities.” This is Part 1 of the specialization. The intent is to closely examine the application areas and technologies and explore opportunities to work in the IIoT field. The structure of the course is intentionally broad but shallow. We will cover many topics, but we will not go into extreme depth on any one topic area. There is one exception: we will look at the “Internet of Things.” In this course, students will learn: Industry 4.0 Key application areas (e.g., manufacturing and IIoT) Wire communication protocols * Computer security information: Encryption techniques to ensure data integrity and authentication, and In the past,

This course is designed for anyone who wants to understand the fundamentals of software engineering as it applies to building large software systems that leverage big data. You’ll be introduced to the software engineering concepts needed to build and scale large, distributed systems with large amounts of data. Starting with software engineering best practices and loosely coupled microservices for working with data, the course will take you through the evolution of a distributed system over time. This course can be taken for academic credit toward CU Boulder’s MS in Data Science or MS in Computer Science programs, both offered on Coursera. These fully accredited degrees offer focused courses, short 8-week sessions, and paid tuition. Admission to the program is based on three prerequisite courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals.

Have you ever wanted to build a database but didn’t know where to start? This course will give you a step-by-step guide. We’ll take you from a rough idea to a workable relational database. Once you’ve embarked on this journey, practicing with real-world mini-case studies, you’ll feel confident and comfortable designing relational databases. Let’s get started! Designing Relational Databases 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 features faculty from CU Boulder’s Applied Mathematics, Computer Science, Information Science, and other departments. An outcomes-based program with no application requirement, the MS-DS 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.