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In this course, you will learn all about Structured Query Language ("SQL"). We will cover the origins of this language and its conceptual underpinnings. But primarily, we will focus on learning all of the standard SQL commands, their syntax, and how to use these commands to perform data analysis in a relational database. We will learn not only the SELECT statement for retrieving data and creating analytical reports, but also the DDL ("Data Definition Language") and DML ("Data Manipulation Language") commands needed to create and maintain database objects. Structured Query Language (SQL) can be taken for academic credit in 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, 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.

In this course, you will learn to analyze measurement systems for process stability and capability and why having a stable measurement process is imperative prior to performing any statistical analysis. You will analyze continuous measurement systems and statistically characterize both accuracy and precision using R software. You will perform measurement systems analysis for potential, short-term and long-term statistical control and capability. Additionally, you will learn how to assess a discrete measurement and perform analyzes for internal consistency, concordance between assessors, and concordance with a standard. Finally, you will learn how to make decisions on measurement systems process improvement. This specialization 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.

Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability. By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field. Learners are encouraged to complete this specialization in the order the courses are presented. This specialization 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. Applied learning project Learners develop an understanding of how to manage, describe, and analyze continuous and discrete data using examples from business and industry. They explore how to assess processes for sources of variation through time as well as determine process capability with respect to customer requirements. Learners gain familiarity with the analysis procedures to assess measurement systems for continuous and discrete data in order to make decisions regarding the capability and acceptability of the measurement system. Assignments require learners to perform analyzes for various data types and scenarios, interpret results, and make appropriate decisions.

In the Machine Learning specialization, we cover supervised learning, unsupervised learning, and the fundamentals of deep learning. You’ll apply ML algorithms to real-world data, learn when to use each model and why, and improve the performance of your models. Starting with supervised learning, we’ll cover linear and logistic regression, KNN, decision trees, ensemble methods like Random Forest and Boosting, and kernel methods like SVM. We’ll then move on to unsupervised methods, including dimensionality reduction methods (e.g. PCA), clustering, and recommender systems. We’ll finish with a deep learning foundation, including choosing a model architecture, building/training neural networks using libraries like Keras, and practical examples of CNNs and RNNs. This specialization can be completed for credit through CU Boulder’s MS in Data Science or MS in Computer Science programs, both offered on Coursera. These fully accredited degrees offer targeted courses, short 8-week sessions, and tuition-based learning. Admission is based on three prerequisite courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: Applied learning project In this specialization, you'll build a movie recommendation system, identify cancer types from RNA sequences, use CNNs for digital pathology, practice NLP techniques on disaster tweets, and even create your own dog images using GANs. You'll complete a final project on supervised, unsupervised, and deep learning to demonstrate your mastery of the course.

This course is part 1 of the specialization Advanced Spacecraft Dynamics and Control. It is a direct continuation of the Coursera specialization Spacecraft Dynamics and Control. This first course focuses on nonlinear attitude feedback control using a range of angular momentum devices. The course provides a comprehensive review of prerequisite material. Next it develops equations of motion of a spacecraft with momentum exchange devices such as reaction wheels (RWs), control momentum gyroscopes (CMGs) and variable speed control moment gyroscopes (VSCMGs). The course discusses developing a complex spacecraft simulation with a number VSCMGs and how to approach debugging such complex software. The use of the work/energy theorem is discussed to assist with debugging the simulation by validating angular momentum, energy, changes in momentum and mechanical power. Further, the use of null motion is explored to reconfigure the attitude control devices to avoid singularities and gimbal lock. The redundancy is exploited to seek control solutions that avoid classical CMG singularities.

Understanding renewable energy technologies is important, but it is equally important to know how to effectively implement these technologies to provide renewable energy to end users, which is the topic of this course. Upon completion of the course, you will understand how to develop and manage renewable energy projects, large and small, from conception to start-up and operation. This course will teach you how to plan, implement, and operate renewable energy projects such as solar photovoltaic (PV), wind farms, and geothermal generators. We will first describe how to develop a feasibility study for a renewable energy project and decide whether to proceed with it. We will then look at selecting a project site and evaluating alternative renewable energy technologies and technology providers. Next, we will look at methods for financing and organizing renewable energy projects. The course concludes with a look at project construction, operation, and maintenance, power restoration, and plant decommissioning. These opportunities will complement your understanding of the use and deployment of renewable energy - join us! Please note that this course is the third of four Coursera courses in the Renewable Energy Specialization: 1. Fundamentals of Renewable Energy Technologies 2. Renewable Energy Systems 3. Renewable Energy Projects 4. The Future of Renewable Energy

This specialization is designed for students who want to gain a deep understanding of renewable energy concepts, tools, and applications. This knowledge can be used to advance in your current job, to transition into the renewable energy industry, and to promote sustainability at home and in your community. New technologies, new market structures, and new business models make renewable energy a dynamic, entrepreneurial, and exciting field. This specialization focuses on pragmatic, fact-based information, including real-world costs and specifications, market trends, and web-based analysis tools. We use lectures, case studies, and interviews with industry leaders to provide a comprehensive, up-to-date education in renewable energy.

Are you interested in data science but lack the math knowledge to get started? Has math always been a difficult subject that you've been avoiding? This course will teach you the most fundamental Calculus concepts you'll need for a career in data science, without a ton of unnecessary proofs and techniques you'll probably never use. Think of it as a data science fast track with accessible methods and user-friendly concepts that will lead you to a real understanding of the most important ideas in calculus. We'll cover some basic algebra, talk about what a derivative is, calculate some simple derivatives, and apply derivative concepts to graphing and maximizing functions. This course is designed to prepare students to succeed in the Statistical Modeling for Data Science Application program, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program.

Are you interested in data science but lack the math skills to get started? Has math always been a difficult subject that you've been avoiding? This course will teach you the most fundamental linear algebra you'll need for a career in data science, without a ton of unnecessary proofs and concepts you'll probably never use. Think of it as a fast track to data science, with accessible methods and user-friendly concepts that will help you truly understand the most important ideas in linear algebra. This course is designed to prepare students to succeed in the Statistical Modeling for Data Science Application program, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program.

This capstone course is the 3rd and final course of the specialization Advanced Spacecraft Dynamics and Control. It assumes you have completed the prior courses on "Attitude Control with Momentum Exchange Devices" and "Analytical Mechanics for Spacecraft Dynamics". This project course investigates the dynamics of a complex spacecraft system where there is a rigid hub onto which a hinged panel is attached. This simulates a spacecraft with a time varying geometry. First, the three-dimensional kinematics of this system are explored. Analytical relationships of the body and panel position and velocity states are derived, and the center of mass properties of this system are explored. Next, a simplified system is used to use Lagrange's equations of motion to predict the dynamical response. With these differential equations we are then able to apply attitude control torques and investigate the rotational response if the spacecraft hub has a spring-hinged panel attached. Two open-loop control torque solutions are under investigation. The classical minimum time bang-bang control solution is applied first, illustrating how such a control can yield unwanted panel oscillations. Finally, a filtered version of the bang-bang control is applied to illustrated how the panel oscillations can be significantly reduced at the cost of a slightly longer nominal maneuver time.

Antiracism III is the third course in CU Boulder’s Antiracism Specialization on Coursera. You’ll be asked to apply and deepen what you’ve learned about race, racism, identity, and inequality in the first and second years to a more global context in which systemic racism and white supremacy are understood as a human rights issue. You’ll also be asked to consider how you might apply antiracist practices in your own life, and you’ll be asked to design a community outreach project that draws on your own experiences and skills as an antiracist advocate. Antiracism III will be available Fall 2021. Course logo image: Taylor Brandon, 10/11/2020. Available on Unsplash at https://unsplash.com/photos/HRfGpPrzmNg

Antiracism is a three-course specialization designed for anyone interested in the study of race and racism, particularly in the context of the United States, who wants to become an antiracist advocate, or who seeks to implement antiracist practices in their daily lives. Antiracism I is an ideal introduction for those new to critical race theory and seeking a basic understanding of race and racism in the United States. Antiracism II builds on the foundations presented in the first course and explores the historical and linguistic constructions of race and racism in the United States, as well as intersectionality theory. The second course also challenges students to apply its content to cultural contexts outside the United States, culminating in an interview project that focuses on deepening conversational nuance as students engage with topics of race and racism with family, friends, coworkers, or peers. Antiracism III then puts the theory presented in the first two courses into practice. Students will create a practical action plan to combat racism and design a community outreach and engagement project. Applied learning project Learn, speak, act! The Antiracism assignments and projects take you from learning the basics of talking about race and racism to having more nuanced and historically informed conversations about identity in your cultural context. The specialization culminates in an invitation to reflect on how you can be an advocate for antiracist views and to design a community outreach project that fits your circumstances and skills.