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This Specialization on advanced spacecraft dynamcis and control is intended for experienced spacecraft dynamics and GNC engineers and researchers. It is assumed the viewer has completed the prior spacecraft dynamics specialization already. Through 3 courses we cover the topics of momentum-based attitude dynamics and control, we derive analytical methods to model complex spacecraft systems, and finally conclude with a captstone project course. After this course you will be prepared to model the dynamics of spacecraft systems with time varying components (reacton wheels, CMS, deployable panels, etc.). Applied learning project The learners will develop complex spacecraft dynamics simulations of a vehicle with several variable speed control moment gyroscopes (CMGs) attached. This challenging software development allows the engineer to create high fidelity attitude simulation and control development. Further, the capstone project explores spacecraft with time varying geometry for both 3D and planar rotation cases. This is applicable to modeling flexing or deploying solar panels.

Are you interested in data science but lack the mathematical background to do so? Has math always been a difficult subject that you have avoided? This course will provide an intuitive understanding of the fundamentals of integral calculus, including integration by parts, area under a curve, and integral evaluation. The course also covers root-finding methods, matrix factorization, and partial derivatives. This course is designed to prepare students to successfully complete the Statistical Modeling for Applications to Data Science course, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program.

Network analysis is a long-standing methodology used to understand the relationships between words and actors in the broader networks in which they exist. This course covers network analysis as it pertains to marketing data, specifically text datasets and social networks. Learners walk through a conceptual overview of network analysis and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project. 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.

Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets are also relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. 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 explore conceptual overviews of text classification, text topic modeling, and semantic network analysis and dive into real-world datasets through instructor-led tutorials. Learners also conduct a major project for each of the three key methods.

If you are a budding programmer interested in data science, a data scientist working closely with content experts, or a software developer looking to get started with the database layer of the stack, this specialization is for you! We will focus on the relational database, which is the most widely used type of database. Relational databases have dominated the database software market for nearly four decades and are a core, fundamental part of software development. In this specialization, you will learn about database design, the fundamentals of database software, and how to use Structured Query Language (SQL) to work with databases. The specialization will conclude with an overview of future trends in databases, including non-relational databases (NoSQL) and Big Data. Upon completion of this specialization, you will be well prepared to design and build efficient and effective relational databases, populate them with data, and work with them using SQL. Applied learning project In Designing Relational Databases and Structured Query Language (SQL) , you'll complete coding exercises, review what you've learned, and describe the problems you've encountered. Then, in Advanced Topics and Future Trends in Database Technologies , you'll develop a big data project plan and explore the advantages and disadvantages of nonrelational databases.

Data science is rapidly evolving, creating career opportunities in a variety of fields. This specialization is designed for students starting a career in data science. Students will gain a brief overview of the fundamental mathematics that is critical to data science. Topics include algebra, calculus, linear algebra, and some essential numerical analysis techniques. Expressway to Data Science is also an excellent preparatory course for students preparing for CU Boulder’s Master of Science in Data Science program. This specialization is designed to prepare students for success in the Statistical Modeling for Applications to Data Science program , which is part of CU Boulder's Master of Science in Data Science (MS-DS) program. Logo courtesy of ThisisEngineering RAEng on Unsplash.com Applied learning project Students will take quizzes in each course to test their understanding of the content as they progress. This specialization does not include projects or final exams, as it is designed to cover the content quickly to prepare students for the higher-level math required in Data Science.

Ever since our ancestors walked onto the African savannah, humans have sought, explored, and wondered about the world around us. Today, and certainly for most, science is the vehicle that guides us along our path to understanding nature. It can take us from the subatomic realms to the most distant galaxies. Thanks in large part to geology, science allows us to push back the fog of time and peer into the billions of years of the past, aptly called “deep time.” Come aboard! This is a journey of discovery - we'll learn about the origins of science and geology itself, about the oceans, atmosphere, and crust of our planet. We'll then look at how geologists have studied the rise and fall of the Rocky Mountains, and we'll conclude by looking at not only the power of science, but recognizing its inherent costs and responsibilities. Those who earn the certificate will demonstrate their knowledge through a series of short assessments and discussion questions, and will be prepared to teach or apply the material.

This course is a gentle introduction to R programming, designed for 3 types of learners. It’s right for you if: - You want to do data analysis but don’t know any programming - You know programming but are new to R - You know some R programming but want to master the tidyverse verbs You’ll learn how to visualize and analyze data in a reproducible way and use features that make your code easy to read and understand. You’ll use RMarkdown to create beautiful documents and reports that run your code fresh every time you run it, and capture your thoughts about the data as you work. This course was designed for non-technical students to help prepare them for more advanced data science courses by providing an introduction to programming and the R language. I’m so excited for you to join me on this journey! The course logo was created using sticker images from the RStudio store. Visit https://swag.rstudio.com/s/shop.

This course continues our gentle introduction to R programming, designed for 3 types of learners. It is suitable for you if: - You want to do data analysis, but don't know programming; - You know programming, but are not very familiar with R; - You know R programming, but want to learn more about tidyverse verbs. It is best taken after the first course of the specialization, or if you are already familiar with ggplot, RMarkdown, and basic writing of R functions. You will learn how to use readr to read data, dplyr to analyze data, and stringr and forcats to work with strings and coefficients.

This course consists of four modules covering some of the most in-depth and advanced areas of database technology, followed by a look at the future of database software and where the industry is headed. Advanced Topics and Future Trends in Database Technologies 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. Based on learning outcomes and requiring no application, the MS-DS program is ideal for individuals with broad educational 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.

Have you ever wanted to read Shakespeare's Romeo and Juliet? This course will provide a guided reading of the script and expose you to the perspectives of actors who've performed in the play. It primarily consists of interviews with 14 professional actors who have participated in at least one production of Romeo & Juliet somewhere in the US, at theaters and Shakespeare festivals including the American Shakespeare Center, American Players Theatre, and the Arkansas, Colorado, Illinois, New Jersey, and Utah Shakespeare Festivals. In completing this course, learners will be able to describe the plot of Romeo & Juliet, identify major characters and themes, analyze and interpret heightened text, and consider multiple theatrical approaches to the play. This course strives to be of interest to multiple audiences, but we think it may particularly appeal to... Shakespeare enthusiasts and the Shakespeare curious in all walks of life Actors, theater practitioners, and teachers of Shakespeare everywhere Secondary school and college students interested in expanding their studies of Shakespeare Interested readers with little or no familiarity with Shakespeare Logo image courtesy of https://picryl.com/media/romeo-and-juliet-12

How many times have you decided to learn a programming language, only to get stuck somewhere along the way, get frustrated, and give up? This specialization is for those with little or no programming experience who want to use Python as a data science tool. The first course will introduce you to programming languages, using Python as an example. You will learn how to use variables and operators, as well as input/output and control flow elements, to create simple Python programs. The pace will be very slow, so you will feel comfortable learning Python as fast or as slow as you like. Are you ready? Let's go! Logo image courtesy of Mourizal Zativa. Available on Unsplash here: https://unsplash.com/photos/gNMVpAPe3PE