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In this course, students will be introduced to the field of statistics, including where data comes from, study design, data management, and data exploration and visualization. Students will identify different types of data and learn to visualize, analyze, and interpret summary data for both univariate and multivariate data. Students will also learn the differences between probability and non-probability sampling of large populations, how sample estimates vary, and how to make inferences about large populations based on probability sampling. At the end of each week, students will apply the statistical concepts they have learned using Python in the course environment. During these labs, students will be introduced to the various uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos will guide students through the creation of visualizations and data management in Python. This course uses the Jupyter Notebook environment within Coursera.

In this course, we will learn the basic principles of using data to evaluate and analyze theories. We will analyze both categorical and quantitative data, starting with single population methods and extending them to comparing two populations. We will learn how to construct confidence intervals. We will also use sample data to evaluate whether a theory about the value of a parameter is consistent with the data. The focus will be on the correct interpretation of inference results. At the end of each week, students will apply what they have learned using Python in the course environment. During these labs, students will work through tutorials focused on specific examples to help reinforce the week’s statistical concepts, which will include further in-depth exploration of Python libraries including Statsmodels, Pandas, and Seaborn. This course uses the Jupyter Notebook environment within Coursera.

In this course, we will expand our study of statistical inference methods by focusing on the art and science of fitting statistical models to data. We will build on concepts introduced in Statistical Inference (Course 2) to emphasize the importance of linking research questions and data analysis methods. We will also focus on the various purposes of modeling, including making inferences about relationships between variables and making predictions for future observations. This course will introduce and explore a variety of statistical modeling methods, including linear regression, logistic regression, generalized linear models, hierarchical and mixed-effects (or multilevel) models, and Bayesian inference methods. All methods will be illustrated on a variety of real-world data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the research design underlying the data (refer to Course 1, Understanding and Visualizing Data with Python). During labs, students will work through tutorials focused on specific examples to help reinforce the week's statistical concepts, which will include further in-depth exploration of Python libraries including Statsmodels, Pandas, and Seaborn. This course uses the Jupyter Notebook environment within Coursera.

In this course, you will be introduced to classes, instances, and inheritance. You will learn how to use classes to represent data in a concise and natural way. You will also learn how to override built-in methods and how to create "inheritable" classes that reuse functionality. You will also learn how to design classes. Finally, you will be introduced to a good programming habit - writing automated tests for your own code. This course is best suited for you if you are already familiar with the basics of Python, which are covered in Python Fundamentals and Python Functions, Files, and Dictionaries (courses 1 and 2 of the Programming in Python 3 Specialization). Completion of Data Collection and Processing with Python (course 3 of this Specialization) is optional, but knowledge of how to retrieve and process complex nested data will be helpful. This is the fourth of five courses in the Programming in Python 3 Specialization.

For CME Attendees: For information on expected deliverables, presenters, disclosures, and CME credit, please visit: https://umich.cloud-cme.com/course/courseoverview?P=0&EID=3551 This course will empower non-prescribers to directly impact the ongoing opioid crisis in the United States by expanding knowledge and tools that will change practice and policy. The course will educate you about the opioid epidemic and provide information and research on evidence-based strategies for prevention, intervention, education, or policy. This open-ended course is primarily intended for non-prescribers in health care, behavioral health, dentistry, and social services, as well as graduate students in these fields. Others, such as educators and physicians, may also benefit from this course. As a learner, you may select any or all of the modules and topics that interest you. You can take the course in a linear or non-linear structure according to your preferred viewing order. This course is taught by experts in opioid prevention, intervention, treatment, and policy. Through lectures, discussions and interviews, knowledge checks and quizzes, and additional reading and exercises, you will explore the topics that are most relevant to your job or practice. Upon completion of the course, participants will be able to educate their patients about the safe use of opioids and their abuse prevention, identify risk factors for opioid misuse and abuse, and refer patients to additional evidence-based resources. The course was developed by three programs at the University of Michigan, including the Institute for Healthcare Policy and Innovation (IHPI), the Opioid Prescribing Engagement Network (OPEN), and the CDC-funded University of Michigan Injury Prevention Center. The course consists of 6 "lessons" or modules of varying lengths, totaling approximately 16 hours. This equates to 16 AMA PRA Category 1™ credits and 16 Michigan Board of Social Work Continuing Education credits. Course modules include: 1. Epidemiology of the Opioid Crisis 2. Understanding Pain 3. Preventing Abuse and Misuse 4. Working with Patients and the Public 5. Addiction Treatment and Recovery 6. Public Policy Continuing Medical Education (CME) and Michigan Board of Social Work Continuing Education (CE) credits You must be signed in and registered on Coursera to view and view content. To earn CME credit, you must log in and register for MiCME here: https://umich.cloud-cme.com/course/courseoverview?P=0&EID=3551 The University of Michigan Medical School is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education to physicians. The University of Michigan Medical School awards a maximum of 16 AMA PRA Category 1™ credits for this content. Physicians should only claim credit commensurate with the extent of their participation in the activity. Maintenance of Certification in Anesthesiology™ and MOCA® are registered trademarks of the American Board of Anesthesiology®. This activity is part of the CME component of the American Board of Anesthesiology’s updated Maintenance of Certification in Anesthesiology™ (MOCA®) program, known as MOCA 2.0®. For a list of all MOCA 2.0 requirements, visit the ABA website at www.theABA.org. Course approved for CME credit 2/16/23, credit available through 1/31/26. This course is approved by NASW-Michigan Social Work Continuing Education Collaborative Approval #112723-00 for 16 CE Hours. This is an on-demand course approved for 12 months.

Have you ever heard that computers "think"? Believe it or not, computers don't really think. Instead, they do exactly what we tell them to do. Programming is "telling a computer what to do and how to do it." Before you can think about programming a computer, you need to figure out exactly what you want the computer to do. Thinking about problems in this way is computational thinking. Computational thinking allows us to take complex problems, understand what the problem is, and develop solutions. We can present these solutions in ways that both computers and humans can understand. The course includes an introduction to computational thinking and a broad definition of each concept, a series of real-world examples illustrating how computational thinking can be used to solve complex problems, and a student project that asks you to apply what you've learned about computational thinking to a real-world situation. This project will be completed in stages (and in phases) and will include a final disaster recovery plan that you will share with other students like you. This course is designed for anyone who is just starting to program, thinking about programming, or just wants to understand a new way to think critically about problems. No prior programming knowledge is required. The examples in this course may seem particularly relevant to a high school audience, but they have been designed to be understandable to anyone. You will learn to: - Identify the components of computational thinking, including abstraction, problem definition, decomposition, pattern recognition, algorithms, and solution evaluation - Learn computational thinking concepts in practice through a series of real-world examples - Develop solutions by applying computational thinking concepts to real-world problems

This course explores specific strategies for community organizing to achieve social justice in a diverse democratic society. The course covers core social justice concepts and practical steps for bringing people together to set goals and priorities, assess community assets and needs, develop action plans, and build support for their implementation. The course includes information on how to form core groups, build organizational capacity, and formulate a strategy for engaging constituents and allies. Because everyone learns differently, the course provides a variety of learning activities, information about successful programs, individualized instruction and small group exercises, and practical resources for problem solving and program planning. You can use these exercises with individuals, a small group, or several groups as part of a community campaign. The course is based on work with people seeking community change in metropolitan Detroit, an area that is becoming both more segregated and more diverse. But it is also designed for anyone who wants to create change in communities everywhere. If you come away from the course with ideas in your head and plans in hand, our goal will have been achieved.

In this course, you will learn about new ways to make consumer-to-business (C2B), consumer-to-consumer (C2C), and business-to-business (B2B) payments. You will examine existing payment system technologies, examine their strengths and weaknesses, and understand how technological innovations are changing these traditional systems. You will learn about front-end innovations such as digital wallets and mobile payments, and discover back-end innovations such as tokenization, mobile money, and new payment infrastructure.

The sudden rise and subsequent fall of Bitcoin and other cryptocurrencies has focused global attention on cryptocurrencies as a means of payment. Blockchain technology underlies Bitcoin and has been touted as the next big, transformative technology. In this course, we will first discuss the technical underpinnings of blockchain, covering key concepts such as decentralization and consensus algorithms. We will then examine blockchain as an asset, looking at the dynamics of cryptocurrency markets. Finally, we will look at blockchain as a business solution, with a focus on understanding the business cases where blockchain makes and does not make sense.

Explore the benefits and challenges of using disruptive technologies to raise capital. Advances in technology have both systematized and democratized consumer and business access to capital. In this course, you will explore how technology has changed access to consumer credit and access to seed capital for businesses. You will explore disruptive technologies in the areas of credit scoring, tokenized capital raising, and the evolution of crowdfunding for both debt and equity financing. You will also learn to differentiate between cryptocurrency-based capital raising models.

This course will teach you how to use your written words to become more persuasive. You'll learn creative ways to use syntax, effective storytelling techniques, and a clever method for organizing complex information. You'll also have the opportunity to both improve your punctuation skills and add a little style and sophistication to the way you compose everything from sentences to taglines. In addition, you'll have access to a wide range of books and other resources that you can use even after you complete the course. These include: (1) readings and exercises that were provided to students who took the in-person version of this course at the University of Michigan and the University of Chicago; (2) two digital libraries containing excellent writing from a variety of journalists, scholars, novelists, poets, historians, and entrepreneurs; and (3) a monthly newsletter, "Good Sentences." Social media has only accelerated the ways in which we all must learn to use our written work to communicate, compete, and create—sometimes all at once. So join us for this first course in a four-part series, and experience the many benefits, both personal and professional, of being "good with words."

This second course in the Word-Fine: Writing and Editing Series will help you become an effective architect of information, in both sentences and paragraphs. You'll learn that the traditional advice, "Show, don't tell," is incomplete, and that skilled writers actually switch from showing to telling. You'll also learn more about the time-management techniques introduced in the first course in the series, including "deep work," "studio time," and the "Animal Farm principle." As with the other three courses in this series, you'll have access to a wide range of books and other resources that you can use even after you complete the course. These include: (1) readings and exercises provided to students who have taken the in-person versions of this course at the University of Michigan and the University of Chicago; (2) two e-libraries of excellent writing from a variety of journalists, scholars, novelists, poets, historians, and entrepreneurs; and (3) a monthly "Good Sentences" e-newsletter.