Leland Stanford Junior University, commonly referred to as Stanford University or simply Stanford, is a private research university in Stanford, California in the northwestern Silicon Valley near Palo Alto. It is one of the most prestigious universities in the world.
Principles of Computing teaches the essential ideas of Computer Science for a zero-prior-experience audience. Computers can appear very complicated, but in reality, computers work within just a few, simple patterns. This course demystifies and brings those patterns to life, which is useful for anyone using computers today.Participants play and experiment with short bits of "computer code" to bring to life to the power and limitations of computers. Everything works within the browser, so there is no extra software to download or install. The course also provides a general background on computers today: what is a computer, what is hardware, what is software, what is the internet. No previous experience is required other than the ability to use a web browser.
Este curso presenta el enfoque del Natural Capital Project (NatCap) para utilizar la información de los servicios del ecosistema para informar decisiones.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThis course should be taken after the Symptom Management course and continues building your primary palliative care skills – communication, psychosocial support, goals of care, and symptom management. You will explore transitions in care such as survivorship and hospice. You will learn how to create a survivorship care plan and how to best support a patient. The course also covers spiritual care and will teach you how to screen for spiritual distress. Finally, you will learn the requirements for hospice care and practice discussions difficult conversations related to end-of-life care.Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThis course takes a deep dive into the challenges families and friends of a patient with serious illness face and how you can care for and support them as a provider, social worker or family friend.Supporting Families and Caregivers especially focuses on the children of a patient with serious illness and their caregiver, and teaches you the best way to empower them to get the support they need. By the end of this course, you will be able to provide critical avenues of support for the people who are instrumental to your patients care, wellbeing and quality of life.Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.
Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.This course is also a part of our Data Analyst Nanodegree.
Viêm gan vi rút B và C mạn tính là nguyên nhân hàng đầu dẫn tới ung thư gan. Tiêm vắc xin dự phòng viêm gan B và xét nghiệm sớm, theo dõi và điều trị đúng cách viêm gan B và C mạn có thể giúp phòng ngừa và giảm nguy cơ mắc xơ gan, ung thư gan.HIỂU RÕ VỀ VIÊM GAN B VÀ C là khoá học miễn phí qua mạng dành cho nhân viên y tế. Khoá học nhằm cung cấp thông tin về các loại viêm gan vi rút và đường lây truyền, tiêm vắc xin và dự phòng viêm gan B, các kỹ năng về chẩn đoán, theo dõi và điều trị viêm gan B và C mạn. Mục đích của khoá học nhằm nâng cao kiến thức và kỹ năng của nhân viên y tế về dự phòng và kiểm soát viêm gan B và C mạn để tăng tỷ lệ tiêm vắc xin phòng viêm ga B, giảm gánh nặng bệnh tật do viêm gan B, C và ung thư gan.Nội dung khoá học dựa trên các số liệu và hướng dẫn kỹ thuật cập nhật nhất từ Tổ chức y tế thế giới và Bộ y tế.Khoá học do Trung tâm gan Á Châu Đại học Stanford phối hợp với Cục y tế dự phòng Bộ y tế biên soạn.
n this course, Tina Seelig reveals a set of tools and conditions that we each control - our Innovation Engine - that allows us to increase our own creativity and that of our teams and organizations. She shows that just as the scientific method demystifies the process of discovery, there is a formal process for unlocking the pathway to invention.
It’s a special moment in U.S. history in which income inequality has reached unprecedented levels, poverty remains extreme, and racial and gender inequalities are intransigent.Why is there so much inequality and poverty? How might they be reduced? Find out from the country’s top scholars in “America’s course” on poverty and inequality.So what makes this course different?Comprehensive: Features the 40 key research results that underlie our country’s policy and its new science of poverty and inequality.Up-to-date: Highlights the most recent findings and results on poverty and inequality.Scholar-direct delivery: The country’s leading scholars present their own research.Quick: Each video is short (approximately 5 minutes) and jargon-free.Modular: The course is divided into 8 standalone modules.Easy to follow: Each module is introduced and explained by David B. Grusky, the director of the Stanford Center on Poverty and Inequality, and Lindsay Owens, Stanford University Ph.D. and Economic Policy Advisor in the office of Senator Elizabeth Warren.Excellent readings: Each video is paired with readings that elaborate the videos.Accessible: It's free, open to the public, and without any prerequisites.Course CreditsAmerica's Poverty and Inequality Course was developed by the Stanford Center on Poverty and Inequality; videos were produced by Ashley Tindell of Film Archer. We gratefully acknowledge the help of our funders: the American Sociological Association, the Stanford University Institute for Research in the Social Sciences, the Canadian Institute for Advanced Research, the U.S. Department of Health and Human Services (Office of the Assistant Secretary for Planning and Evaluation), and the Elfenworks Foundation. We would also like to thank Marion Coddou for her work in helping to develop the course.
This course aims to provide a firm grounding in the foundations of probability and statistics. Specific topics include:1. Describing data (types of data, data visualization, descriptive statistics)2. Statistical inference (probability, probability distributions, sampling theory, hypothesis testing, confidence intervals, pitfalls of p-values)3. Specific statistical tests (ttest, ANOVA, linear correlation, non-parametric tests, relative risks, Chi-square test, exact tests, linear regression, logistic regression, survival analysis; how to choose the right statistical test)The course focuses on real examples from the medical literature and popular press. Each week starts with "teasers," such as: Should I be worried about lead in lipstick? Should I play the lottery when the jackpot reaches half-a-billion dollars? Does eating red meat increase my risk of being in a traffic accident? We will work our way back from the news coverage to the original study and then to the underlying data. In the process, participants will learn how to read, interpret, and critically evaluate the statistics in medical studies.The course also prepares participants to be able to analyze their own data, guiding them on how to choose the correct statistical test and how to avoid common statistical pitfalls. Optional modules cover advanced math topics and basic data analysis in R.PREREQUISITESThere are no prerequisites for this course.Participants will need to be familiar with a few basic math tools: summation sign, factorial, natural log, exponential, and the equation of a line; a brief tutorial is available on the course website for participants who need a refresher on these topics.
Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination. ***Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!***Why Take This Course?Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives. It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the state-of-the-art research fields from robotics, medical diagnosis, and quantum science. In this course you’ll learn the basics and applications of AI, including: machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.### Part I: Fundamentals of AI - Overview of AI - Statistics, Uncertainty, and Bayes networks - Machine Learning - Logic and Planning - Markov Decision Processes and Reinforcement Learning - Hidden Markov Models and Filters - Adversarial and Advanced Planning ### Part II: Applications of AI - Image Processing and Computer Vision - Robotics and robot motion planning - Natural Language Processing and Information Retrieval
OverviewInternet Enduring Material Sponsored by the Stanford University School of Medicine. Presented by the Department of Psychiatry and Behavioral Sciences at Stanford University School of MedicineThis CME activity provides a practical approach to the identification and screening of suspected psychosis. Narrative storytelling and didactic pieces provide a unique insight into the mind of a patient experiencing the early signs and symptoms of psychosis. Case scenarios will be used to demonstrate skills in talking to young people, and their families, about psychosis. Early warning signs will be reviewed along with high-yield screening questions to support understanding, identifying and treating psychosis in adolescents and young adults.Intended AudienceThis course is designed for family practice, primary care, pediatrics and psychiatry physicians, nurse practitioners, physician assistants and school social workers.AccreditationIn support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. ****Credit Designation American Medical Association (AMA)The Stanford University School of Medicine designates this enduring material for a maximum of 2.00 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity. ****If you would like to earn CME credit from Stanford University School of Medicine for participating in this course, please review the information here prior to beginning the activity.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThis course is designed to help learners around the world become more sustainable eaters. Together, we’ll explore key topics, like how food production impacts the environment and why meat production and protein consumption are often at the center of the debate around sustainability. We’ll introduce the pros and cons of different kinds of agriculture, fishing and food packaging, with a focus on how we can make more environmentally friendly decisions on a daily basis. We’ll also look ahead and explore some of the technology innovations that could become increasingly important as we look at the future of food for a growing global population. If this is the first course you’ve ever taken on food and sustainable eating, you’ll come away with concrete tips for how you can make food choices that will protect the world we hand over to the next generation. Our planet needs many people making small changes in the right direction and we’re here to help with that. If you’re an expert in food sustainability, we hope to offer you some tools that could help you to communicate key messages to others in simple, digestible ways. Whatever your level, we hope you’ll join this discussion as we explore, together, the ways in which we can all become more sustainable eaters.The beautiful story animations were scripted by Lucas Oliver Oswald and animated by Janine Van Schoor.Special thanks to: Lucas Oliver Oswald, William Bottini, Desiree Labeaud, Christopher Gardner, Sejal Parekh, Arielle Wenokur, Janine Van Schoor, Ann Doerr, Perry Pickert and the fantastic team at Friday Films.
OverviewThis online CME activity provides a practical approach to the diagnosis and management of migraine for primary care providers and general neurologist. We will cover key concepts in the diagnosis and management of migraine through an online interactive video-based course with cases to reinforce your knowledge. Migraine is a type of chronic headache disorder which requires ongoing maintenance to prevent attacks. It is one of the most disabling conditions and a common disorder evaluated by primary care physicians and general neurologists. The diagnosis of migraine and standard of care treatment approaches including pharmacologic and nonpharmacologic strategies will be reviewed. Patient and family counseling as well as educational resources for patients will also be provided upon completion of the course.AccreditationIn support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. ****Credit Designation American Medical Association (AMA)The Stanford University School of Medicine designates this enduring material for a maximum of 1.5 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity. ****If you would like to earn CME credit from Stanford University School of Medicine for participating in this course, please review the information here prior to beginning the activity.Additional InstructorsNada Hindiyeh, MD
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeAlgorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.About the instructor: Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThis course teaches scientists to become more effective writers, using practical examples and exercises. Topics include: principles of good writing, tricks for writing faster and with less anxiety, the format of a scientific manuscript, peer review, grant writing, ethical issues in scientific publication, and writing for general audiences.