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All rights reserved. I have experience or a degree. Google Get on the fast track to success Yes, no matter what. Project managers are natural problem solvers. The A's involved. 6-Wei Beginners. Google Search for their career. 10 minutes 10 days 100 100 100 All rights reserved. Project Management Institute like CAPM® globally recognized. 82% get a promotion or raise within six months. ² Google Career here . ¹Burning Glass: Laboratory Insight (last 12 months: 2/1/20 - 1/31/21) Coursera Time to Information from Google. All rights reserved. Cumulative from 1/18 - 1/21. Applied learning project 140 hours of instruction and hundreds of All rights reserved. real-world projects that are critical to success in the workplace. Google who have It's a good idea. Skills you'll gain include: Develop risk management plans; Understand process improvement techniques; All rights reserved. All rights reserved. All rights reserved. Practice agile project management, with a focus on Scrum. Through a combination of videos, assessments, and hands-on activities, you'll learn All the same. All rights reserved. In the future.

This Specialization is an advanced view to the fascinating world of Supply Chain Management. When you complete the program you'll have a richer understanding of the complexities that companies are facing in today's global networked economy. The Specialization is for you, if: 1. have taken our introductory specialization in Supply Chain Management and want to learn more; 2. you have some experience in Supply Chain Management and want to understand the topic better; 3. you're fascinated by how the global economy is linked together by the flow of products, information, and finances. Applied learning project You will learn to analyze a company's supply chain and solve business problems in a realistic setting. In addition, you will solve three detailed real-life case studies showing your mastery of supply chain management. The skills you develop in this specialization will help you become an expert in the global end-to-end network of customers and suppliers.

The course provides a well-researched and comprehensive introduction to the UN system. The course consists of an introduction to the complex UN system and its history, as well as a series of snapshots of key UN functions, which are used to explore important UN themes and help students develop important analytical, communication and policy skills. The course as a whole is designed for people interested in learning more about the UN system, assuming an interest but without the necessary previous knowledge, while offering enough relevant research and new critical perspectives, and will also appeal to those with more experience or academic familiarity with the topic. The main aim of the course is to provide this broad introduction in a self-contained but in-depth manner, as well as the important practical skills needed to understand and discuss UN affairs, and perhaps lay the foundation for more active involvement in the future - either in civil society or in further study.

Clinical trials are experiments designed to evaluate new interventions to prevent or treat disease in people. Interventions evaluated may be drugs, devices (such as a hearing aid), surgeries, behavioral interventions (such as a smoking cessation program), population health programs (such as cancer screening programs), or health care delivery systems (such as hospital specialty care units). We consider clinical trials to be experiments because investigators, rather than patients or their doctors, choose the treatments patients receive. Results from randomized clinical trials are generally considered the highest level of evidence for determining the effectiveness of a treatment because the trials include features that ensure that the benefits and risks of the treatment are assessed objectively and unbiased. The FDA requires that drugs or biologics (such as vaccines) be proven effective in clinical trials before they can be marketed in the United States. This course will explain the basic principles of designing randomized clinical trials and how they should be presented. In the first part of the course, students will be introduced to the terminology used in clinical trials and some common designs used in clinical trials, such as parallel and crossover. We will also explain some of the mechanics of clinical trials, such as randomization and blinding of treatment. In the second half of the course, we will explain how clinical trial results are analyzed and interpreted. Finally, we will cover the main ethical issues involved in conducting experiments on humans.

In this course, you'll learn how to keep track of the different versions of your code and configuration files using a popular version control system (VCS) called Git. We'll also go through how to set up an account with a service called GitHub so that you can create your very own remote repositories to store your code and configuration. Throughout this course, you'll learn about Git's core functionality so you can understand how and why it's used in organizations. We'll look into both basic and more advanced features, like branches and merging. We'll demonstrate how having a working knowledge of a VCS like Git can be a lifesaver in emergency situations or when debugging. And then we'll explore how to use a VCS to work with others through remote repositories, like the ones provided by GitHub. By the end of this course, you'll be able to store your code's history in Git and collaborate with others in GitHub, where you'll also start creating your own portfolio! In order to follow along and complete the assessments, you'll need a computer where you can install Git or ask your administrator to install it for you.

Probabilistic graphical models (PGMs) provide a rich framework for encoding probability distributions in complex domains: joint (multivariate) distributions over a large number of random variables that interact with each other. These representations are at the intersection of statistics and computer science, drawing on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for state-of-the-art methods in a wide variety of applications, such as medical diagnostics, image understanding, speech recognition, natural language processing, and many, many others. They are also a fundamental tool in the formulation of many machine learning problems. Applied learning project Through a variety of lectures, quizzes, programming assignments, and exams, students in this specialization will practice and master the fundamentals of probabilistic graphical models. This specialization includes three five-week courses for a total of fifteen weeks.

Welcome to this Specialization focused on the principles of Microeconomics. This program is not merely about the study of money, but explores the functional roles of individual decision-makers, both consumers and producers, within the larger economic system. Across four comprehensive courses, we will provide an introduction to the nature and functions of product markets, the theory of the firm under various conditions of competition and monopoly, and the roles governments play in promoting the economy's efficiency. We will delve into fascinating subjects such as the environment, love and marriage, crime, labor markets, education, politics, sports, and business. This program is perfect for students, professionals interested in economics, or anyone aiming to make more informed financial decisions. The only prerequisites for this course are an open mind and a willingness to explore the fascinating world of economics from unique angles. Applied learning project By the end of this Specialization, you'll gain vital skills including understanding consumer and firm behavior, analyzing different types of market structures, applying economic principles to everyday life, and using supply and demand diagrams to better comprehend the impact of changes in supply and demand on price and quantity.

This is the sixth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn about machine learning, which uses algorithms and statistics to teach computer systems to discover patterns in data. Data professionals use machine learning to help analyze large amounts of data, solve complex problems, and make accurate predictions. You’ll focus on the two main types of machine learning: supervised and unsupervised. You'll learn how to apply different machine learning models to business problems and become familiar with specific models such as Naive Bayes, decision tree, random forest, and more. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: - Apply feature engineering techniques using Python - Construct a Naive Bayes model - Describe how unsupervised learning differs from supervised learning - Code a K-means algorithm in Python - Evaluate and optimize the results of K-means model - Explore decision tree models, how they work, and their advantages over other types of supervised machine learning - Characterize bagging in machine learning, specifically for random forest models - Distinguish boosting in machine learning, specifically for XGBoost models - Explain tuning model parameters and how they affect performance and evaluation metrics

Continue to develop your TensorFlow skills by exploring a wide range of deployment scenarios and discovering new ways to use data more effectively when training machine learning models. In this four-course specialization, you’ll learn how to put your machine learning models in the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and mobile apps. Learn how to consume built-in datasets with just a few lines of code, explore data pipelines with TensorFlow data services, use APIs to manage data partitioning, handle all types of unstructured data, and retrain deployed models with user data while preserving data privacy. Apply your knowledge to a variety of deployment scenarios and get familiar with TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries around the world are adopting artificial intelligence. This specialization from Lawrence Moroney and Andrew Ng will help you develop and deploy machine learning models across devices and platforms faster and more accurately than ever before.

In this course, we will attempt to "understand Einstein," with a particular focus on the special theory of relativity that Albert Einstein, as a twenty-six-year-old patent clerk, introduced in his "miracle year" of 1905. Our goal will be to penetrate beyond the myths and popularized representations of relativity to gain a deeper understanding of both Einstein and the concepts, predictions, and strange paradoxes of his theory. Some of the questions we will consider include: How did Einstein arrive at his ideas? What was the nature of his genius? What is the meaning of relativity? What is "special" about special relativity? Why did it initially seem like the theory was dead on arrival? What does it mean that time is a "fourth dimension"? Can time really flow more slowly for one person than for another, or can the sizes of objects change depending on their speed? Is time travel possible, and if so, how? Why can't things travel faster than the speed of light? Is it possible to travel to the center of the galaxy and back in one lifetime? Is there any evidence to definitively support the theory, or is it mostly speculation? Why didn't Einstein win a Nobel Prize for his theory of relativity? About the Instructor: Dr. Larry Lagerstrom is the Director of Academic Programs at Stanford University's Center for Professional Development, which offers graduate certificates in artificial intelligence, cybersecurity, data mining, nanotechnology, innovation, and management science. He holds degrees in physics, mathematics, and the history of science, has published a book and TED Ed video on "Young Einstein: From the Doxerl Affair to the Miracle Year," and has had over 30,000 students worldwide enroll in his online course on special relativity (this course!).

In this new course, you’ll gain evidence-based knowledge and practical tools to help you build and lead diverse, equitable, and inclusive (DEI) teams and organizations. No matter your background or where you are in the world, you’ll gain tools to help you accelerate your personal journey to leading diverse teams and organizations. This program is specifically designed for learners of all backgrounds (gender, race, country of origin, etc.) and levels of expertise (newcomers to the topic or social justice warriors). Over the course of the course, you’ll gain a better understanding of yourself and your personal identity in the workplace and develop new skills to identify privilege, implicit bias, and microaggressions in your organization and take action as an active ally and advocate for change. By listening to experts who represent a range of real-world perspectives, you’ll understand best practices for equitable organizational processes and norms, as well as inclusive behavioral practices in teams. Finally, you will learn best practices for an organizational DEI strategy, including the role of metrics in DEI work and how DEI work can be integrated into the heart of an organization. By the end of the course, you will have created a DEI action plan that you can apply to your life and workplace. Over the course of the course, you will: - Describe the organizational benefits of diversity, equity, and inclusion - Identify the conditions under which diversity is most likely to benefit teams and organizations - Deepen your understanding of various demographic differences and how identity, implicit bias, and structural inequities impact workplace dynamics - Identify best practices for creating equitable organizational processes and norms. - Include important considerations for leading inclusive teams, including conflict management skills, best practices for group decision-making, and emotion regulation. - Gain tools for implementing DEI strategies in organizations, including DEI team architecture, the role of data and metrics, and tools for integrating DEI into the heart of the organization.

Machine learning and artificial intelligence have the potential to transform healthcare and open up a world of incredible possibilities. But we will never realize the potential of these technologies unless all stakeholders have a basic understanding of healthcare and machine learning concepts and principles. This course will introduce you to the fundamental concepts and principles of machine learning as they apply to medicine and healthcare. We will cover machine learning approaches, medical use cases, metrics unique to healthcare, and best practices for designing, building, and evaluating machine learning applications in healthcare. The course will provide individuals with non-engineering backgrounds in healthcare, medical policy, pharmaceutical development, and data science with the knowledge needed to critically evaluate and use these technologies. Co-author: Jeffrey Angus Editors: Mars Huang Jin Long Shannon Crawford Auge Marques In support of improving the patient experience, 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 Accreditation Center (ANCC) to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding: 1) Issue and expiration dates; 2) Statements of accreditation and assignment of credit status; 3) Disclosure of information on financial relations for each person controlling the content of the activity.