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In the capstone course of the Statistical Modeling for Data Science program, students will explore a broad set of more advanced statistical modeling tools. These tools will include generalized linear models (GLM), which will introduce classification (via logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semiparametric generalized additive models (GAMs). Particular emphasis will be placed on developing a solid conceptual understanding of these tools. Ethical issues that arise when using complex statistical models will also be addressed. 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 a broad educational background 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. Logo adapted from a photo by Vincent Ledvina on Unsplash

Statistical modeling is at the heart of data science. Well-designed statistical models allow data scientists to draw conclusions about the world based on the limited information contained in their data. In this three-credit sequence, students will add some intermediate and advanced statistical modeling techniques to their data science toolbox. Specifically, students will gain skills in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Particular emphasis will be placed on analyzing real data using the R programming language. This specialization can be completed for credit as part of 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. 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, information science, mathematics, and statistics.

Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of these topics is a field in its own right. This specialization provides a “taste” of each of these areas, allowing the student to determine if any of these areas are something they want to explore further. In this specialization, students will learn about the various applications of data science and how to apply the steps of the data science process to real-world data. They will be introduced to the ethical issues that every data scientist should be aware of when conducting analysis. The cybersecurity area allows the data scientist to understand how to protect their data from loss. This specialization can be completed for credit as part of 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. 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, information science, mathematics, and statistics.

Fandom, Social Media, and Authenticity in the Digital Age focuses on the interrelated dynamics of identity, representation, interpretation, and self-reflection. It asks how, where, and with whom we inhabit fandom and popular culture in the digital age. We will examine the role of digital media in the production of celebrity and fandom, using social media tools and platforms as a constant example. More broadly, the course is about what media scholar Henry Jenkins has called “participatory culture,” the processes by which we bring ourselves into digital spaces, establish trust and authenticity (or contest these notions), and negotiate identities in and among others. Along the way, you will develop skills in critical self-reflection and communication, as well as analysis and interpretation. You will audit your own social media use and consider what you have learned in the context of the major social media platforms of our era. Course-Level Learning Outcomes Identify and describe the relationships between fandom, popular culture, and digital media as each influences our identities and interactions with others Apply the principles of critical self-reflection and close reading to personal and professional writing and online publications Engage with communities related to pop culture and fandom, using online platforms to make connections and synthesize knowledge Reflect on your personal relationships to social media, identity, representation, and social interaction online Course 1 Skills: Personal development Analysis Digital literacy Writing Social media Close reading

"Comic Books, Geek Culture, and the Fandom Imaginary" explores some of the conventional framings of “fandom” (from comic book obsessives to cosplayers) and the cultural histories that sustain it. It also explores the dark side of these dynamics, looking at what can happen when fandom turns toxic in pop culture spaces. You will continue to apply frameworks of self-reflection and close reading/analysis to the study of contemporary popular culture, and explore how those frameworks provide tools for understanding the self as much as our cultural surroundings. The course asks the fundamental question: how can what we love - what we read, what we watch, what we share - contribute to making the world a better place? Along the way, you'll continue to develop your skills in honing and representing your fandom, specifically engaging in activities designed to help you find your fellow fans, help you find your people, and connect with them outside and beyond the course. Course-Level Learning Outcomes Define and describe the dynamics of geek culture (fantasy, science fiction, comic books, graphic novels), as well as its relationships to fandom, popular culture, and digital media Apply critical self-reflection and close reading frameworks through analytical writing and creative expression Engage with pop culture and fandom communities by leveraging online platforms to make connections and synthesize learning Reflect on your personal relationship to geek culture (fantasy, science fiction, comic books, graphic novels), and how popular culture intersects with memory and nostalgia Course 3 Skills: Analysis Self-Reflection Critical Thinking Communication Culture Close Reading

How might what we love - what we watch, what we read, what we post - make our communities healthier and more vibrant? This question guides Fandom and Popular Culture in the Digital Age . In our networked and information-saturated world, three vital needs emerge: representing identity in digital media, interrogating the products of culture, and engaging meaningfully with digital communities. This course addresses these needs by inviting you into a space of reflection at the intersection of fandom, popular culture, and digital media . More than at any point in human history, these forces have put at our fingertips the means to create and disseminate ourselves in the world. We will explore the products of popular culture and trace the fan networks that sustain and derive value from them. Along the way, we will train you to recognize relationships between popular culture and the political and commercial forces that shape its publication, distribution, and consumption. This course will equip you with the tools necessary to boost your digital media fluency and define, shape, and evolve your identity as a fan within digital spaces. But ultimately, this is a celebration - a celebration of you and your communities, what you love, and how that love might bring people together to change the world. Together, we can make this a community-driven online resource for our shared endeavor of analyzing and understanding fandom and popular culture. Applied learning project The projects across this specialization challenge learners to reflect deeply on their experiences, practice skills of close reading, analysis, and interpretation, and express their fandom in thoughtful and creative ways. Learners will practice digital literacy and professional communication skills through reflecting on their personal fandom in blog-style writing, express their creativity by making something relevant to their fandom, and engage with their fandom directly through online platforms.

Antiracism II is a bridge course between Antiracism I and Antiracism III that focuses on race, racism, and strategies for being antiracist. Antiracism II is designed for those who have previously taken Antiracism I or have a basic understanding of racial issues affecting the United States and the world. Antiracism I focused on how to engage in open dialogue and conversations about race and racism. Antiracism II is designed to help students engage in their own conversations about race, as well as gender and sexuality. Antiracism II will expand your knowledge and critical thinking by focusing on short interviews with leading scholars and activists in the field. Coupled with additional videos and readings, these interviews will demonstrate the power of open dialogue and self-narrative, setting you on the path to becoming an ally in the fight against racism. Interviews are central to the course, culminating in a capstone project in which students construct and conduct their own individual interview. The final project will test dialogic communication skills while affirming the importance of intimate conversations about race, gender, and sexuality. This is challenging work in turbulent times that can bring up unsettling feelings and emotions. Antiracism II can serve as a bridge to confront your personal, individual relationship with the social demands that are troubling us nationally and globally. The remedy is to allow yourself to be uncomfortable in order to reach solutions. We are all in this together. Peace & Love Shawn Course logo image by Emmanuel Gido, 08/31/2020. Available on Unsplash at https://unsplash.com/photos/SAjZSZUA690

This course introduces statistical inference, sampling distributions, and confidence intervals. Students will learn how to identify and construct good coefficient estimates, moment estimators, maximum likelihood estimators, and methods for constructing confidence intervals that extend to more general settings. This course may 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. 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, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from a photo by Christopher Burns on Unsplash.

While many would prefer to leave science behind and no longer engage with it after completing required science classes in school, the reality is that engaging with science is an integral part of our daily lives. Moreover, engaging with scientific information is essential to the safety and well-being of human society. We face the challenge of learning scientific evidence on issues ranging from conservation to public health and making decisions that impact not only our families but also the communities around us. However, despite decades of science education reform and concerted efforts to improve scientific literacy, many members of the general public lack the confidence, skills, or interest to discuss scientific issues, preferring to negotiate their scientific decisions with non-scientific stakeholders such as politicians or pop culture figures. These challenges are persistent and destructive. How can we communicate scientific information more effectively to the general public? With other scientists? This course is designed for scientists, science teachers, and science communicators who are interested in rethinking how we communicate science to a variety of audiences, especially the general public. Topics include evidence-based considerations about human cognition and effective communication across a variety of media, including oral presentations, written forms of communication, technology, and the arts. The goal of this course is to equip students with the skills needed to design and implement effective science communication practices. Students will be able to identify key principles of human cognition that underlie science communication, describe the pitfalls of “popular” science communication, and apply best practices in science communication across a variety of media.

The energy revolution is in full swing. Renewable energy sources are growing at a staggering rate – especially in the power sector. Wind turbines and solar photovoltaic (PV) systems account for the majority of new power plants built worldwide and are essential to building a low-carbon, sustainable energy future. As a result, countless new opportunities are opening up for renewable energy. This course provides a solid foundation in the fundamentals of renewable energy. We'll start with how electricity is measured, how electricity systems work, and how renewable technologies like wind turbines and solar PV work. We'll then cover the technical and market fundamentals: how and why renewables are driving change in electricity systems around the world, how electricity systems are changing to accommodate new renewables, and what it all means for those seeking to understand and participate in the global transformation of electricity systems. This global transformation is just beginning; with the knowledge in this course, you'll be ready to jump on board. Course logo credit: Wind Turbine icon courtesy of Vectors Point from the Noun Project.

Data is everywhere. Charts, graphs, and other types of data visualizations help people make sense of that data. This course explores the design, development, and evaluation of these data visualizations. Combining aspects of design, computer graphics, HCI, and data science, you’ll gain hands-on experience creating visualizations, using research tools, and telling stories about data. Topics include user-centered design, web visualizations, data cognition, and design evaluation. This course can be taken for 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 is based on three prerequisite courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals.

This course covers the main techniques used in data mining including frequent pattern analysis, classification, clustering, outlier analysis, as well as complex data analysis and research perspectives in data mining. This course can be taken for 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 tuition-based programs. Admission is based on three prerequisite courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie and available on Unsplash: https://unsplash.com/photos/jbJp18srifE