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This course introduces the fundamental steps of the data mining process, including data understanding, data preprocessing, data warehousing, data modeling, interpretation and evaluation, and real-world applications. 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.

Fundamental algorithms on tree data structures, binary search trees, self-balancing trees, graph data structures, and basic graph traversal algorithms. This course also covers advanced topics such as k-d trees for spatial data and algorithms for spatial data. Trees and Graphs: Fundamentals 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 with faculty from CU Boulder's Applied Mathematics, Computer Science, Information Science, and other departments. The MS-DS program, which is outcomes-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.

This course will provide a foundational set of statistical modeling tools for data science. Specifically, students will be introduced to the methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, fairness, and various strategies for variable selection and model comparison. It will also cover the misuse of statistical models and the ethical implications of such misuse. 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 Departments of 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 Logo adapted from a photo by Vincent Ledvina on Unsplash

This course provides a general introduction to the field of data science. It was designed for aspiring data scientists, content specialists who work with data scientists, or anyone interested in learning what data science is and what it is used for. Weekly topics include an overview of the skills needed to work as a data scientist; the process and pitfalls associated with data science; and the practice of data science in the professional and academic worlds. This course is part of CU Boulder's Master of Science in Data Science program and was developed collaboratively by academics and industry professionals to provide students with an inside look at this exciting, evolving, and increasingly important discipline. Data Science as a Field can be taken for academic 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, 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://www.coursera.org/degrees/master-of-science-data-science-boulder.

In "Fandom, Community, and Identity in Popular Music," you will explore the intersections of fandom and popular culture using pop music as a framework. You will apply your skills of self-reflection and close reading/analysis to a few case studies of pop musicians and the ways in which their art inhabits and evolves in pop culture spaces. You will learn about what it means to be a music fan, and therefore to be a fan in general. Along the way, you’ll have the chance to express your creativity as a fan by making something to contribute back into your fandom. This project will ask you to apply your self-reflection and close reading frameworks, and will ground your studies of fandom and pop culture in your lived experience (both physical and virtual). Course-Level Learning Outcomes * Define and describe the dynamics of popular music and its relationship with 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 popular music, popular art, and how popular culture intersects with memory and nostalgia Course 2 Skills: * Analysis * Evaluation * Critical Thinking * Communication * Creativity * Close Reading

Understand the fundamentals of probability and its relationship to statistics and data science. We’ll learn what it means to calculate probability, independent and dependent outcomes, and conditional events. We’ll explore discrete and continuous random variables and see how this fits into data mining. Finally, we’ll look at Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance to all of statistics and data science. 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 features faculty from CU Boulder’s Applied Mathematics, Computer Science, Information Science, and other departments. The MS-DS program, which is outcomes-based and requires no application, 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 Logo adapted from a photo by Christopher Burns on Unsplash.

This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to undecidability (NP-completeness) and the use of linear/integer programming solvers to solve optimization problems. We also cover some advanced topics in data structures. 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 fee-based 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.

The future of renewable energy is bright but uncertain. Will it continue to grow rapidly? Is current growth sufficient to achieve climate stabilization? How do technologies like electric vehicles and heat pumps relate to it? This course will shed light on the many confusing and sometimes contradictory claims and projections about renewable energy. We will look at emerging renewable energy technologies and approaches, such as floating wind turbine platforms and building-integrated photovoltaics (PV), and highlight key opportunities and limitations. We will take a closer look at the possible future of enabling technologies such as energy storage, electric vehicles and hydrogen, which can support and enhance renewables. We will then cover key trends and emerging approaches such as distributed energy and electrification, and explain how they impact the future of renewables. Renewable energy, supported by enabling technologies such as electric vehicles and storage, will eventually dominate energy systems around the world. In this course, you will learn about the current state and likely future paths of renewable energy. With this knowledge, you will be able to pinpoint the opportunities in this dynamic industry and be at the forefront of change. Course logo image: Wind Turbine icon courtesy of Vectors Point from the Noun Project.

In this course, you will be introduced to Computer Vision as a field of study and research. We will first examine several computer vision problems and proposed approaches to them from a classical computer vision perspective. We will then introduce Deep Learning methods and apply them to some of the same problems. We will analyze the results obtained and discuss the advantages and disadvantages of both types of methods. We will use tutorials so that you can practice some of the modern machine learning tools and software libraries. Examples of computer vision problems where deep learning can be applied include: image classification, image classification with localization, object detection, object segmentation, face recognition, activity or pose estimation. 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 the Coursera platform. These fully accredited degrees offer focused courses, short 8-week sessions, and a fee-based 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.

In this course, students will begin with a macro-level view of the current state of the world, covering topics such as climate change, plastic pollution, social inequality, and the economic systems that have brought us to where we are today. Students will explore how such an economy cannot sustain itself and how a rapid transition to something different is needed. We will define sustainability, the meaning of sustainable development, and the UN Sustainable Development Goals. Recognising the need for change, we will begin by looking at energy, and more specifically electricity generation, as it is widely believed to be the main cause of climate change. We will show why this is so and look at ways to reduce carbon emissions, for example by switching from coal and natural gas to renewable energy sources such as wind and solar. The concept of a carbon footprint and how to calculate it is introduced. Students will have the opportunity to calculate their personal carbon footprint for different energy options (coal, natural gas or renewables) and also explore what would happen to their personal carbon footprint if they switched to renewable energy sources.

In this course, you will learn various supervised ML algorithms and forecasting tasks applied to different data. You will learn when to use each model and why, as well as how to improve model performance. We will cover models such as linear and logistic regression, KNN, decision trees, and ensemble methods such as Random Forest and Boosting, kernel methods such as SVM. Prior knowledge of coding or scripting is required. We will use Python extensively throughout the course. To take this course, you will need a solid foundation in Python or enough previous coding experience in other programming languages ​​to quickly master Python. We’ll learn how to use data science libraries like NumPy, pandas, matplotlib, statsmodels, and sklearn. This course is designed for programmers who are new to these libraries. Previous experience with these libraries is helpful but not required. College-level math skills, including calculus and linear algebra, are required. We hope you’ll find the math clear but not intimidating. 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 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

Marketing data often requires categorization or labeling. In today’s age, marketing data can also be very big, or larger than what humans can reasonably tackle. In this course, students learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students walk through a conceptual overview of supervised machine learning 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.