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We have already looked at Most Popular courses of 2019, here we take look at course collection from The Johns Hopkins University that will supercharge your career and expand your knowledge.
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Quick Snapshot
All these courses are completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free.
Johns Hopkins University
Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Johns Hopkins University
Assemble the right team, ask the right questions, and avoid the mistakes that derail data science projects. In four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.
Johns Hopkins University
This specialization is intended for people working or aspiring to work in global health programming. You will learn the foundational building blocks of effective program planning, implementation, and evaluation in a variety of settings, including low- and middle-income countries and humanitarian crises.
Johns Hopkins University
With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome. This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, along with a variety of software implementation tools like Python, R, Bioconductor, and Galaxy. This Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics, for scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work. To audit Genomic Data Science courses for free, visit https://www. coursera.org/jhu, click the course, click Enroll, and select Audit. Please note that you will not receive a Certificate of Completion if you choose to Audit.
Johns Hopkins University
R is a programming language and a free software environment for statistical computing and graphics, widely used by data analysts, data scientists and statisticians. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing and scaling useful data science results and products. This Specialization will give you rigorous training in the R language, including the skills for handling complex data, building R packages, and developing custom data visualizations. You’ll be introduced to indispensable R libraries for data manipulation, like tidyverse, and data visualization and graphics, like ggplot2. You’ll learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. This Specialization is designed to serve both data analysts, who may want to gain more familiarity with hands-on, fundamental software skills for their everyday work, as well as data mining experts and data scientists, who may want to use R to scale their developing and programming skills, and further their careers as data science experts.
Johns Hopkins University
Preventable patient harms, including medical errors and healthcare-associated complications, are a global public health threat. Moreover, patients frequently do not receive treatments and interventions known to improve their outcomes. These shortcomings typically result not from individual clinicians’ mistakes, but from systemic problems – communication breakdowns, poor teamwork, and poorly designed care processes, to name a few. The Patient Safety & Quality Leadership Specialization covers the concepts and methodologies used in process improvement within healthcare. Successful participants will develop a system’s view of safety and quality challenges and will learn strategies for improving culture, enhancing teamwork, managing change and measuring success. They will also lead all aspects of a patient safety and/or quality improvement project, applying the methods described over the seven courses in the specialization.
Johns Hopkins University
This Specialization covers the fundamentals of web development with Ruby on Rails. You’ll learn everything you need to develop your own web application using Ruby on Rails, SQL and NoSQL databases, and HTML/CSS, and Javascript. We will also touch on advanced topics such as security, services using HTTP/RESTful access patterns, and user access and user experience from multiple device platforms. In the final Capstone Project, you’ll apply your skills to develop a web application that hosts uploaded photos and displays them using a map.
Johns Hopkins University
By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We’ve designed this course to be as convenient as possible without sacrificing any of the essentials. This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We’ve left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know. 1. How to describe the role data science plays in various contexts 2. How statistics, machine learning, and software engineering play a role in data science 3. How to describe the structure of a data science project 4. Know the key terms and tools used by data scientists 5. How to identify a successful and an unsuccessful data science project 3. The role of a data science manager Course cover image by r2hox. Creative Commons BY-SA: https://flic. kr/p/gdMuhT
Johns Hopkins University
Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: – A basic understanding of linear algebra and multivariate calculus. – A basic understanding of statistics and regression models. – At least a little familiarity with proof based mathematics. – Basic knowledge of the R programming language. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists’ general understanding of regression models.
Johns Hopkins University
Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: – A basic understanding of linear algebra and multivariate calculus. – A basic understanding of statistics and regression models. – At least a little familiarity with proof based mathematics. – Basic knowledge of the R programming language. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists’ general understanding of regression models.
Johns Hopkins University
This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. Upon completing this course you will be able to identify and abstract common data analysis tasks and to encapsulate them in user-facing functions. Because every data science environment encounters unique data challenges, there is always a need to develop custom software specific to your organization’s mission. You will also be able to define new data types in R and to develop a universe of functionality specific to those data types to enable cleaner execution of data science tasks and stronger reusability within a team.
Johns Hopkins University
We will learn computational methods – algorithms and data structures – for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets.
Johns Hopkins University
A food system encompasses the activities, people and resources involved in getting food from field to plate. Along the way, it intersects with aspects of public health, equity and the environment. In this course, we will provide a brief introduction to the U.S. food system and how food production practices and what we choose to eat impacts the world in which we live. We will discuss some key historical and political factors that have helped shape the current food system and consider alternative approaches from farm to fork. The course will be led by a team of faculty and staff from the Johns Hopkins Center for a Livable Future. Guest lecturers will include experts from a variety of disciplines, including public health, policy and agriculture.
Johns Hopkins University
Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.
Johns Hopkins University
The data science revolution has produced reams of new data from a wide variety of new sources. These new datasets are being used to answer new questions in way never before conceived. Visualization remains one of the most powerful ways draw conclusions from data, but the influx of new data types requires the development of new visualization techniques and building blocks. This course provides you with the skills for creating those new visualization building blocks. We focus on the ggplot2 framework and describe how to use and extend the system to suit the specific needs of your organization or team. Upon completing this course, learners will be able to build the tools needed to visualize a wide variety of data types and will have the fundamentals needed to address new data types as they come about.
Johns Hopkins University
Writing good code for data science is only part of the job. In order to maximizing the usefulness and reusability of data science software, code must be organized and distributed in a manner that adheres to community-based standards and provides a good user experience. This course covers the primary means by which R software is organized and distributed to others. We cover R package development, writing good documentation and vignettes, writing robust software, cross-platform development, continuous integration tools, and distributing packages via CRAN and GitHub. Learners will produce R packages that satisfy the criteria for submission to CRAN.
Johns Hopkins University
Data science is a team sport. As a data science executive it is your job to recruit, organize, and manage the team to success. In this one-week course, we will cover how you can find the right people to fill out your data science team, how to organize them to give them the best chance to feel empowered and successful, and how to manage your team as it grows. This is a focused course designed to rapidly get you up to speed on the process of building and managing a data science team. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We’ve left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know. 1. The different roles in the data science team including data scientist and data engineer 2. How the data science team relates to other teams in an organization 3. What are the expected qualifications of different data science team members 4. Relevant questions for interviewing data scientists 5. How to manage the onboarding process for the team 6. How to guide data science teams to success 7. How to encourage and empower data science teams Commitment: 1 week of study, 4-6 hours Course cover image by JaredZammit. Creative Commons BY-SA. https://flic. kr/p/5vuWZz
Johns Hopkins University
In this Capstone project for the Photo Tourist you will implement a Ruby on Rails web application that makes use of both a relational and NoSQL database for the backend and expose the data through services to the Internet using Web services and a responsive user interface operating in a browser from a desktop and mobile device. You will have a chance to revisit and apply what you have learned in our previous courses to build and deploy a fully functional web application to the cloud accessible to your co-workers, future employers, friends, and family. In developing the Photo Tourist web application, you will get to work with different data types and data access scenarios (e.g, fielded data display and update, image upload/download, text search, access controlled information) to provide your users the ability to show off their photos and information from trips they have taken and to seek out photos and information from trips taken by others. Using the application you develop, your users will be able to Create an account Upload and download photos to the site and make them accessible to others Provide descriptions of trips and photos that others can read Organize photos by location and trip, Find photos based on location Find photos based on text searches of descriptions Locate the place where the photo was taken on a map
Johns Hopkins University
This course covers chemicals in our environment and in our bodies and how they impact our health. It addresses policies and practices related to chemicals, particularly related to how they get into our bodies (exposures), what they do when they get there (toxicology), how we measure them (biomonitoring) and their impact on our health. Most examples are drawn from the US.
Johns Hopkins University
Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
Johns Hopkins University
In bringing about behavior change in public health, we often focus on the individual mother, student, or farmer. We should not forget the community structure and norms constrain for encouraging individual health behaviors. This course examines the community context of the changes needed to promote the public’s health. We begin by examining the various definitions of ‘community’ and the processes by which we ‘diagnose’ or seek to understand the structure and characteristics of different types of communities. An appreciation of community similarities and differences is necessary lest we fall into the trap of designing one-size-fits-all interventions. We need to recognize that no matter that outsiders may view a community as poor or neglected, we can find strengths and capacities for improvement in each community. Identifying community capacities and resources is the first step in facilitating community change. Different practical and philosophical approaches to change and therefore, examined. Specific to the change process is our recognition of the need for communities to participate in the design, implementation and evaluation of any intervention. We examine the concept of participation in an effort to see how different levels of involvement may affect sustainability of community change efforts. Finally a case study of a community participatory approach to onchocerciasis control in Africa is presented. Community Directed Intervention has subsequently been successfully applied to providing other essential primary health care services by and in the community, such as insecticide treated bednets, malaria treatment, vitamin A distribution, deworming medicines, and pneumonia and diarrhea case management.
Johns Hopkins University
This course introduces participants from the healthcare sector to gender based violence (GBV), including global epidemiology of GBV; health outcomes; seminal research; and clinical best practices for GBV prevention, support, and management. A core curriculum is supplemented by lectures that contextualize the content with specific examples and programs from around the world. The core curriculum introduces learners to a global perspective on gender based violence (GBV), and includes a review with Dr. Claudia Garcia-Moreno of the new WHO guidelines on responding to violence. Students who wish to receive Honors Recognition will complete the honors module, which expands on the core material and highlights special circumstances and programs. This is an in-depth course with 2 components: 1) Core curriculum introduces GBV from a global perspective, with an emphasis on ensuring a strong health sector response to GBV and teaching key competencies for social workers, physicians, nurses, midwives, community health workers, counselors, and other healthcare workers. Completion of the core content is required for students to pass the course. 2) Honors curriculum offered by experts from around the world helps students dive deeper into certain issues, and touches on unique populations and specialized topics. Completion of Honors curriculum is required for those students who wish to receive a Certificate of Accomplishment with Honors. After taking the course, students will be able to: â— Describe the global epidemiology of leading forms of GBV and the evidence linking GBV to poor health. â— Articulate the challenges, strategies, and WHO guidelines for integrating GBV response within the health sector. â— Describe the components of a comprehensive clinical assessment, treatment, and management of a GBV survivor. â— Describe the appropriate psychosocial support and management of a GBV survivor. Module 1 – Introduction to GBV- Epidemiology and Health Impact GBV comes in a variety of forms, each with health consequences for the survivor. An understanding of these issues helps inform a comprehensive and multi-sectorial response for preventing and responding to GBV. Module 2 – Health Care Response, Screening, and Psychosocial Support Recognition of health impacts of GBV has led to calls to address GBV within the health sector. Support for GBV survivors extends beyond clinical exam and assessment. Safety planning, harm reduction, and access to psychosocial support must be ensured. Module 3 – Clinical Care for GBV Survivors Caring for GBV survivors requires compassionate, confidential, and nondiscriminatory clinical assessment. Competent medical and forensic examination, along with appropriate documentation, is essential. Honors Module – In-Depth Information and Special Topics This module provides additional materials for those who wish to explore specialized topics and gain a more advanced grasp of the complexities of addressing gender-based violence. Core course topics are expanded upon, and special populations and programs are highlighted. Special topics include violence in humanitarian settings, against adolescents and sex workers, and human trafficking. Further information is provided on epidemiology, policy, and ethical guidelines, as well as the use of mHealth in GBV screening and care. We’ll cover the unique challenges of GBV research, and explore programs that encourage men and boys to be engaged in the prevention of violence against women. Acknowledgments This course is a project of the Johns Hopkins Center for Clinical Global Health Education. We would like to gratefully acknowledge the following collaborators: â— Centre for Enquiry into Health and Allied Themes (CEHAT) â— International Center for Research on Women’s (ICRW) Asia Regional Office â— Center on Gender Equity and Health at University of California, San Diego â— Division of Global Public Health at University of California, San Diego â— World Health Organization â— RTI International â— Swayam â— Johns Hopkins Bloomberg School of Public Health, Center for Public Health and Human Rights â— Johns Hopkins Bloomberg School of Public Health, Department of Population, Family & Reproductive Health â— Johns Hopkins School of Nursing This course is made possible through the generosity of the Ujala Foundation, the Vijay & Marie Goradia Charitable Foundation, and the Wyncote Foundation.
Johns Hopkins University
The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners.
Johns Hopkins University
Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses. This is a focused course designed to rapidly get you up to speed on doing data science in real life. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We’ve left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to: 1, Describe the “perfect” data science experience 2. Identify strengths and weaknesses in experimental designs 3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls. 4. Challenge statistical modeling assumptions and drive feedback to data analysts 5. Describe common pitfalls in communicating data analyses 6. Get a glimpse into a day in the life of a data analysis manager. The course will be taught at a conceptual level for active managers of data scientists and statisticians. Some key concepts being discussed include: 1. Experimental design, randomization, A/B testing 2. Causal inference, counterfactuals, 3. Strategies for managing data quality. 4. Bias and confounding 5. Contrasting machine learning versus classical statistical inference Course promo: https://www. youtube.com/watch?v=9BIYmw5wnBI Course cover image by Jonathan Gross. Creative Commons BY-ND https://flic. kr/p/q1vudb
Johns Hopkins University
Clinical trials are experiments designed to evaluate new interventions to prevent or treat disease in humans. The interventions evaluated can be drugs, devices (e.g, hearing aid), surgeries, behavioral interventions (e.g, smoking cessation program), community health programs (e.g. cancer screening programs) or health delivery systems (e.g, special care units for hospital admissions). We consider clinical trials experiments because the investigators rather than the patients or their doctors select the treatment the patients receive. Results from randomized clinical trials are usually considered the highest level of evidence for determining whether a treatment is effective because trials incorporates features to ensure that evaluation of the benefits and risks of treatments are objective and unbiased. The FDA requires that drugs or biologics (e.g, vaccines) are shown to be effective in clinical trials before they can be sold in the US. The course will explain the basic principles for design of randomized clinical trials and how they should be reported. In the first part of the course, students will be introduced to terminology used in clinical trials and the several common designs used for clinical trials, such as parallel and cross-over designs. We will also explain some of the mechanics of clinical trials, like randomization and blinding of treatment. In the second half of the course, we will explain how clinical trials are analyzed and interpreted. Finally, we will review the essential ethical consideration involved in conducting experiments on people.
Johns Hopkins University
Keeping patient safety and quality improvement projects on track, on time, and on budget is critical to ensuring their success. In this course, students will be introduced and given the opportunity to apply a series of tools to guide and manage patient safety and quality initiatives. These include tools for defining what success looks like, developing a change management plan, and conducting a pre-mortem to identify risks for project failure. This course will also provide tools for engaging stakeholders to ensure key players are invested in your project’s success.
Johns Hopkins University
A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.
Johns Hopkins University
Health behavior lies at the core of any successful public health intervention. While we will examine the behavior of individual in depth in this course, we also recognize by way of the Ecological Model that individual behavior is encouraged or constrained by the behavior of families, social groups, communities, organizations and policy makers. We recognize that behavior change is not a simplistic process but requires an understanding of dimensions like frequency, complexity and cultural congruity. Such behavioral analysis is strengthened through the use of a toolkit of theoretical models and practical frameworks. While many of such models and frameworks exist, in this course we will review the Health Belief Model, Social Learning Theory, Theory of Reasoned Action, the Trans-Theoretical Model and the PRECEDE Framework. After building your behavioral analysis toolkit with these examples, you will see that actual behavior change program planning uses a combination of ideas and variables from different models, theories and frameworks. Ultimately we aim to encourage course participants to apply the idea that successful programs are theory based as they go about involving people in improving their health.
Johns Hopkins University
Synbio is a diverse field with diverse applications, and the different contexts (e.g, gain-of-function research, biofuels) raise different ethical and governance challenges. The objective of this course is to increase learners’ awareness and understanding of ethical and policy/governance issues that arise in the design, conduct and application of synthetic biology. The course will begin with a short history of recombinant DNA technology and how governance of that science developed and evolved, and progress through a series of areas of application of synbio. Content will be presented in many forms, including not only reading and lectures, but also recorded and live interviews and discussions with scientists, ethicists and policy makers. Learners will have the opportunity to think, write and talk about the issues and challenges in their own work and in real-life case examples. A final project will engage students in the development of governance models for synbio.
Johns Hopkins University
Epidemiology is often described as the cornerstone science in public health. Epidemiology in public health practice uses study design and analyses to identify causes in an outbreak situation, guides interventions to improve population health, and evaluates programs and policies. In this course, we’ll define the role of the professional epidemiologist as it relates to public health services, functions, and competencies. With that foundation in mind, we’ll introduce you to the problem solving methodology and demonstrate how it can be used in a wide variety of settings to identify problems, propose solutions, and evaluate interventions. This methodology depends on the use of reliable data, so we’ll take a deep dive into the routine and public health data systems that lie at the heart of epidemiology and then conclude with how you can use that data to calculate measures of disease burden in populations.
Johns Hopkins University
Welcome to the Evidence-based Toxicology (EBT) course. In medicine and healthcare, evidence-based medicine has revolutionized the way that information is evaluated transparently and objectively. Over the past ten years, a movement in North America and Europe has attempted to translate this revolution to the field of toxicology. The Center for Alternatives to Animal Testing (CAAT) within the department of Environmental Health and Engineering at the Johns Hopkins Bloomberg School of Public Health hosts the first chair for EBT and the secretariat for the EBT Collaboration on both sides of the Atlantic. Based on the Cochrane Collaboration in Evidence-based Medicine, the EBT Collaboration was established at the CAAT to foster the development of a process for quality assurance of new toxicity tests for the assessment of safety in humans and the environment. Regulatory safety sciences have undergone remarkably little change in the past fifty years. At the same time, our knowledge in the life sciences is doubling about every seven years. Systematic review and related evidence-based approaches are beginning to be adapted by regulatory agencies like the Environment Protection Agency (EPA), the European Food Safety Authority (EFSA), and the US National Toxicology Program. They provide transparent, objective, and consistent tools to identify, select, appraise, and extract evidence across studies. This course will showcase these emerging efforts and address opportunities and challenges to the expanded use of these tools within toxicology.
Johns Hopkins University
The Executive Data Science Capstone, the specialization’s culminating project, is an opportunity for people who have completed all four EDS courses to apply what they’ve learned to a real-world scenario developed in collaboration with Zillow, a data-driven online real estate and rental marketplace, and DataCamp, a web-based platform for data science programming. Your task will be to lead a virtual data science team and make key decisions along the way to demonstrate that you have what it takes to shepherd a complex analysis project from start to finish. For the final project, you will prepare and submit a presentation, which will be evaluated and graded by your fellow capstone participants. Course cover image by Luckey sun. Creative Commons BY-SA https://flic. kr/p/bx1jvU
Johns Hopkins University
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.
Johns Hopkins University
Neuroimaging methods are used with increasing frequency in clinical practice and basic research. Designed for students and professionals, this course will introduce the basic principles of neuroimaging methods as applied to human subjects research and introduce the neuroscience concepts and terminology necessary for a basic understanding of neuroimaging applications. Topics include the history of neuroimaging, an introduction to neuroimaging physics and image formation, as well as an overview of different neuroimaging applications, including functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, perfusion imaging, and positron emission tomography imaging. Each will be reviewed in the context of their specific methods, source of signal, goals, and limitations. The course will also introduce basic neuroscience concepts necessary to understand the implementation of neuroimaging methods, including structural and functional human neuroanatomy, cognitive domains, and experimental design.
Johns Hopkins University
In this culminating project, you will deploy the tools and techniques that you’ve mastered over the course of the specialization. You’ll work with a real data set to perform analyses and prepare a report of your findings.
Johns Hopkins University
Learn to use the tools that are available from the Galaxy Project. This is the second course in the Genomic Big Data Science Specialization.
Johns Hopkins University
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
Johns Hopkins University
Do you realize that the only functionality of a web application that the user directly interacts with is through the web page? Implement it poorly and, to the user, the server-side becomes irrelevant! Today’s user expects a lot out of the web page: it has to load fast, expose the desired service, and be comfortable to view on all devices: from a desktop computers to tablets and mobile phones. In this course, we will learn the basic tools that every web page coder needs to know. We will start from the ground up by learning how to implement modern web pages with HTML and CSS. We will then advance to learning how to code our pages such that its components rearrange and resize themselves automatically based on the size of the user’s screen. You’ll be able to code up a web page that will be just as useful on a mobile phone as on a desktop computer. No “pinch and zoom” required! Last but certainly not least, we will get a thorough introduction to the most ubiquitous, popular, and incredibly powerful language of the web: Javascript. Using Javascript, you will be able to build a fully functional web application that utilizes Ajax to expose server-side functionality and data to the end user.
Johns Hopkins University
This course explores why primary health care is central for achieving Health for All. It provides examples of how primary health care has been instrumental in approaching this goal in selected populations and how the principles of primary health care can guide future policies and actions. Two of the most inspiring, least understood, and most often derided terms in global health discourse are “Health for All” and “Primary Health Care.” In this course, we will explore these terms in the context of global health, their origins and meanings, the principles upon which they rest, and examples of how these principles have been implemented at both small and large scale. We will also explore some ultra-low-cost approaches to Health for All through primary health care, and the promise that primary health care holds for eventually achieving Health for All. Each module of this course consists of approximately one hour or lecture, approximately one hour of additional readings or video presentations, and two additional hours devoted to studying for each of the quizzes, writing and evaluating two short peer-review assignments, and participating in the discussion forums. Developed in collaboration with Johns Hopkins Open Education Lab.
Johns Hopkins University
Now that you’ve carefully planned your patient safety and quality improvement project, the real work can begin. This course will introduce students to the unique challenges encountered when implementing, maintaining, and expanding a patient safety and quality initiative. Students will learn to apply lessons learned from the 4 E model and TRiP into developing specific aims for their QI project. Additionally, students will develop a plan to address the adaptive and technical challenges in their projects including whether their initiative needs to be submitted to an Institutional Review Board (IRB). Finally, students will develop plans to grow their local QI project into a system-wide project.
Johns Hopkins University
Whether you’ve traveled before or not, living and working overseas can be challenging. Learn how best to prepare and make the most of your time internationally. This course will prepare you to work and live overseas. It explores the epidemiology of common morbidity and mortality among travelers and examines key prevention, safety, and travel medicine principles and services to contextualize risks and maintain wellness. The course reviews applicable interventions, appropriate vaccines, and personal protection methods to prepare you to respond to expected and unexpected situations and will challenge you to examine travel health and safety priorities through case studies and discussions. The Honors Lesson will assist you with personal preparations for travel through the creation of a country-specific profile.
Johns Hopkins University
This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We’ll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You’ll also get an introduction to the key concepts in computing and data science that you’ll need to understand how data from next-generation sequencing experiments are generated and analyzed. This is the first course in the Genomic Data Science Specialization.
Johns Hopkins University
Neurohacking describes how to use the R programming language (https://cran.r-project.org/) and its associated package to perform manipulation, processing, and analysis of neuroimaging data. We focus on publicly-available structural magnetic resonance imaging (MRI). We discuss concepts such as inhomogeneity correction, image registration, and image visualization. By the end of this course, you will be able to: Read/write images of the brain in the NIfTI (Neuroimaging Informatics Technology Initiative) format Visualize and explore these images Perform inhomogeneity correction, brain extraction, and image registration (within a subject and to a template).
Johns Hopkins University
We will introduce methods to perform systematic reviews and meta-analysis of clinical trials. We will cover how to formulate an answerable research question, define inclusion and exclusion criteria, search for the evidence, extract data, assess the risk of bias in clinical trials, and perform a meta-analysis. Upon successfully completing this course, participants will be able to: – Describe the steps in conducting a systematic review – Develop an answerable question using the “Participants Interventions Comparisons Outcomes” (PICO) framework – Describe the process used to collect and extract data from reports of clinical trials – Describe methods to critically assess the risk of bias of clinical trials – Describe and interpret the results of meta-analyses
Johns Hopkins University
Over 500,000 people in the United States and over 8 million people worldwide are dying every year from cancer. As people live longer, the incidence of cancer is rising worldwide and the disease is expected to strike over 20 million people annually by 2030. This open course is designed for people who would like to develop an understanding of cancer and how it is prevented, diagnosed, and treated. The course introduces the molecular biology of cancer (oncogenes and tumor suppressor genes) as well as the biologic hallmarks of cancer. The course also describes the risk factors for the major cancers worldwide, including lung cancer, breast cancer, colon cancer, prostate cancer, liver cancer, and stomach cancer. We explain how cancer is staged, the major ways cancer is found by imaging, and how the major cancers are treated. In addition to the core materials, this course includes two Honors lessons devoted to cancers of the liver and prostate. Upon successful completion of this course, you will be able to: – Identify the major types of cancer worldwide. (Lecture 1) – Describe how genes contribute to the risk and growth of cancer. (Lecture 2) – List and describe the ten cellular hallmarks of cancer. (Lecture 3) – Define metastasis, and identify the major steps in the metastatic process. (Lecture 4) – Describe the role of imaging in the screening, diagnosis, staging, and treatments of cancer. (Lecture 5) – Explain how cancer is treated. (Lecture 6) We hope that this course gives you a basic understanding of cancer biology and treatment. The course is not designed for patients seeking treatment guidance – but it can help you understand how cancer develops and provides a framework for understanding cancer diagnosis and treatment.
Johns Hopkins University
Health professionals and students, family caregivers, friends of and affected individuals, and others interested in learning about dementia and quality care will benefit from completing the course. Led by Drs. Nancy Hodgson and Laura Gitlin, participants will acquire foundational knowledge in the care of persons with Alzheimer’s Disease and other neurocognitive disorders.
Johns Hopkins University
R Programming Capstone
Johns Hopkins University
This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
Johns Hopkins University
Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.
Johns Hopkins University
How will you know if your patient safety and quality project is meeting its objectives? Peter Drucker once said “What gets measured, gets managed.” In this course, students will learn why measurement is critical to quality improvement work. Equally important, they will learn which data sources provide the most meaningful information and tools for how and where to locate them. Finally, students will learn how to interpret data from their patient safety and quality projects to guide and modify them during implementation to maximize their chances of making a difference for patients.
Johns Hopkins University
In this course, you will be able develop a systems view for patient safety and quality improvement in healthcare. By then end of this course, you will be able to: 1) Describe a minimum of four key events in the history of patient safety and quality improvement, 2) define the key characteristics of high reliability organizations, and 3) explain the benefits of having strategies for both proactive and reactive systems thinking.
Johns Hopkins University
This course provides students with a set of tools and methodologies to plan and initiate a Problem Solving or Quality Improvement project. The first module presents methods for selecting, scoping and structuring a project before it is even initiated. It also introduces the project classifications of implementation and discovery. The second module describes the A3 problem solving methodology and the tool itself. Further in that same module, the student is shown tools to identify problems in flow, defects, and waste and to discover causes, brainstorm, and prioritize interventions. Module 3 shows a methodology within the implementation class. These methods are designed to overcome emotional and organizational barriers to translating evidence-based interventions into practice. The fourth and last module looks at one more way to approach improvement projects in the discovery class. These tools are specifically for new, out-of-the-box design thinking.
Johns Hopkins University
Pre-Exposure Prophylaxis (PrEP) using the antiretroviral medication emtricitibine/tenofovir approved in countries around the world is a highly effective means of reducing transmission of HIV through sexual encounters and needle sharing. This Johns Hopkins University course PrEPares you with essential information, concepts and practical advice regarding PrEP from leaders in the field. A first of its kind learning opportunity, both providers and patients learn from the same experts through content that meets the needs of both audiences, while facilitating the opportunity for a shared community space. Lessons for healthcare workers provide background on foundational and cutting-edge research and PrEP guidelines, how to initiate a PrEP program, clinical management and providing culturally sensitive sexual health and primary care to diverse communities. Lessons for PrEP enthusiasts, PrEP users or the PrEP curious provide information regarding who can benefit from PrEP, how to access services, what to expect and how to stick with your PrEP program long-term. The Association of Nurses in AIDS Care is providing 9.1 contact hours, 1.2 of which can be use towards pharmacology contact hours for this course. The Association of Nurses in AIDS Care is an approved provider of continue nursing education by the American Nurses Credentialing Center’s Commission on Accreditation OBJECTIVES: At the conclusion of the session, the participant will be able to: 1. Describe the differences between foundational PrEP studies and demonstration projects 2. Describe the basic pharmacodynamics of tenofovir/emtricitibine including mechanism of infection prevention and time to protective concentration in mucosal tissues 3. List recommendations from PrEP for Prevention of HIV Infection in the United States clinical practice guidelines, USPHS and CDC, including initial and ongoing screening and testing 4. Describe the need for PrEP as an HIV prevention tool for priority in often stigmatized populations 5. Indicate the components for integrating PrEP services into clinical practice 6. Outline guidelines for screening and treatment of sexually transmitted infections 7. Describe how to take a thorough sexual history and to engage with clients around sex in an affirming and non-judgmental manner 8. List the baseline and follow-up laboratory monitoring required 9. Explain key aspects of patient education for HIV prevention and sexual health 10. Describe protocols for ongoing PrEP services and when to discontinue FACULTY/ CREDENTIALS: Jason E. Farley, PhD, MPH, ANP-BC, FAAN, Associate Professor Johns Hopkins University School of Nursing Chris Beyrer, MD, MPH, Professor Johns Hopkins University Bloomberg School of Public Health Yusuf Ariyibi, BA, Disease Intervention Specialist Baltimore City Health Department Joyce Jones, MD, MS, Clinical Associate Johns Hopkins University School of Medicine Neha Sheth Pandit, PharmD, AAHIVP, BCPS, Associate Professor University of Maryland School of Pharmacy Pierre-Cedric Crouch, PhD, ANP-BC, ACRN, Director of Nursing San Francisco AIDS Foundation Renata Arrington Sanders, MD, Assistant Professor Johns Hopkins University School of Medicine Jenell Coleman, MD, MPH, Associate Professor Johns Hopkins University School of Medicine Michele Decker, ScD, MPH, Associate Professor Johns Hopkins University Bloomberg School of Public Health Deborah Dunn, PA-C, MBA, Physician Assistant Chase Brexton Health Care Jordan White, MS, Desmond Tutu Fellow of Public Health and Human Rights Johns Hopkins University Bloomberg School of Public Health Gregory Lucas, MD, PhD, Professor Johns Hopkins University School of Medicine Demetre Daskalakis, MD, MPH, Acting Deputy Commissioner, Division of Disease Control, NYC Dept. of Health and Mental Hygiene David Dowdy, MD, PhD, Associate Professor Johns Hopkins University Bloomberg School of Public Health Jessica LaRicci, PrEP Coordinator Johns Hopkins University School of Nursing Susan Tuddenham, MD, MPH, Assistant Professor Johns Hopkins University School of Medicine Joseph Cofrancesco, MD, MPH, FACP, Associate Professor of Medicine Johns Hopkins University School of Medicine Jill Crank, CRNP, MSN/MPH, Nurse Practitioner Evergreen Healthcare Paul Sacamano, MPH, ANP-BC, ACRN, PrEP Project Lead Johns Hopkins University School of Nursing Shima Ge, BS, PrEP Peer Navigator Johns Hopkins University School of Nursing ORIGINATION DATE: October 2, 2017 RENEWAL DATE: EXPIRATION DATE: October 2. 2019 URL: https://www. coursera.org/learn/prep/ HARDWARE/SOFTWARE: Computer Hardware; Internet connection; Browser MATERIALS: None TARGET AUDIENCE: physicians, physician assistants, nurse practitioners, registered nurses, pharmacists, health education specialists, public health workers, social workers, case managers PREREQUISITES: None FORMAT: These seminars are enduring video presentations with online discussion forum and resources. CONTACT INFORMATION: Office of The REACH Initiative, Johns Hopkins.
Johns Hopkins University
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
Johns Hopkins University
Learn to provide psychological first aid to people in an emergency by employing the RAPID model: Reflective listening, Assessment of needs, Prioritization, Intervention, and Disposition. Utilizing the RAPID model (Reflective listening, Assessment of needs, Prioritization, Intervention, and Disposition), this specialized course provides perspectives on injuries and trauma that are beyond those physical in nature. The RAPID model is readily applicable to public health settings, the workplace, the military, faith-based organizations, mass disaster venues, and even the demands of more commonplace critical events, e.g, dealing with the psychological aftermath of accidents, robberies, suicide, homicide, or community violence. In addition, the RAPID model has been found effective in promoting personal and community resilience. Participants will increase their abilities to: – Discuss key concepts related to PFA – Listen reflectively – Differentiate benign, non-incapacitating psychological/ behavioral crisis reactions from more severe, potentially incapacitating, crisis reactions – Prioritize (triage) psychological/ behavioral crisis reactions – Mitigate acute distress and dysfunction, as appropriate – Recognize when to facilitate access to further mental health support – Practice self-care Developed in collaboration with Johns Hopkins Open Education Lab.
Johns Hopkins University
This course introduces a set of public health problems experienced by people affected by natural disasters and/or conflict. It discusses the many changes in people’s lives when they are uprooted by a disaster, ranging from changes in disease patterns, access to health care, livelihoods, shelter, sanitary conditions, nutritional status, etc. We will explore what humanitarian interventions could look like if we want to mitigate the effects of disasters. The course content is a mix of theoretical knowledge and many practical examples from recent disasters. We think this course is unique because it contains so many practical ‘real-life’ examples and is taught be instructors and guest lecturers who together have over 200 years of experience in this field. The course consists of 10 modules totaling approximately 9-10 hours of delivered content with an additional 2-3 hours of self-work (quizzes and writing and evaluating a short peer-review assignment) as well as lively discussions forums. The course has been designed in a way that each module builds on the lessons of previous modules. However, modules can be accessed in any order and some can stand alone. You do not have to pay for this course if you choose to enroll without a certificate. Sometimes referred to as auditing, enrolling without a certificate means that you will have access to all of the videos, quizzes, assignments, and discussions. The only difference is that you will not receive a certificate upon completion. Click the Enroll Without A Certificate link to sign up and begin the course. Even if you enroll in a session that has yet to begin, you may access most of the course materials right away by clicking the Preview Course Materials link. However, you will have to wait for the session to begin before posting on the discussion forum or accessing the final peer-reviewed assessment. Visit the Learner Help Center for details about session schedules.
Johns Hopkins University
This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University.
Johns Hopkins University
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Johns Hopkins University
You already know how to build a basic web application with the Ruby on Rails framework. Perhaps, you have even taken Course 1, “Ruby on Rails: An Introduction” (we highly recommend it) where you relied on external web services to be your “data layer”. But in the back of your mind, you always knew that there would come a time when you would need to roll up your sleeves and learn SQL to be able to interact with your own relational database (RDBMS). But there is an easier way to get started with SQL using the Active Record Object/Relational (ORM) framework. In this course, we will be able to use the Ruby language and the Active Record ORM framework to automate interactions with the database to quickly build the application we want. In Rails with Active Record and Action Pack, we will explore how to interact with relational databases by using Active Record, a Ruby gem, which Rails uses by default for database access. We will then take a look at what role Active Record plays in the overall request-response cycle, when a client (the browser) requests data from the server, as well as how to submit the data to the server. Of course, when accessing data, security is of paramount importance! We will talk about vulnerabilities such as SQL injection, as well as how to secure access to data by authenticating and authorizing users accessing the data. Take this course to build a Ruby on Rails application with Active Record to automate the detailed SQL interactions with our database.
Johns Hopkins University
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
Johns Hopkins University
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.
Johns Hopkins University
In this course, we will explore MongoDB, a very popular NoSQL database and Web Services concepts and integrate them both with Ruby on Rails. MongoDB is a used to handle documents with a pre-defined schema which will give the developers an ability to store, process and use data using it’s rich API. The modules will go in-depth from installation to CRUD operations, aggregation, indexing, GridFS and various other topics where we continuously integrate MongoDB with RailsRuby. We will be covering the interface to MongoDB using the Mongo Ruby API and the Mongoid ORM framework (the MongoDB access counterpart to RDBMS/ActiveRecord within Rails). The last portion of the course will focus on Web Services with emphasis on REST, its architectural style and integration of Web Services with Rails. Core concepts of Web Services like request/response, filters, data representation (XML/JSON), web linking and best practices will covered in depth. This course is ideal for students and professionals who have some programming experience and a working knowledge of databases.
Johns Hopkins University
Did you ever want to build a web application? Perhaps you even started down that path in a language like Java or C#, when you realized that there was so much “climbing the mountain” that you had to do? Maybe you have heard about web services being all the rage, but thought they were too complicated to integrate into your web application. Or maybe you wondered how deploying web applications to the cloud works, but there was too much to set up just to get going. In this course, we will explore how to build web applications with the Ruby on Rails web application framework, which is geared towards rapid prototyping. Yes, that means building quickly! At the conclusion of this course, you will be able to build a meaningful web application and deploy it to the “cloud” using a Heroku PaaS (Platform as a Service). Best of all, it will almost feel effortless… Really! “But wait”, you will say, “there is no way that we can build a useful application if there is no database involved. You need the data for an application to be useful.” Great point! But what if… instead of getting the data from the database, we get it from the internet by tapping into one of the web services out there that readily provides data needed by our application? “Ok, but that’s probably very complicated”, you will say. Take this course and you will be pleasantly surprised at just how easy it is!
Johns Hopkins University
Safety culture is a facet of organizational culture that captures attitudes, beliefs, perceptions, and values about safety. A culture of safety is essential in high reliability organizations and is a critical mechanism for the delivery of safe and high-quality care. It requires a strong commitment from leadership and staff. In this course, a safe culture is promoted through the use of identifying and reporting patient safety hazards, accountability and transparency, involvement with patients and families, and effective teamwork.
Johns Hopkins University
Do you want to write powerful, maintainable, and testable front end applications faster and with less code? Then consider joining this course to gain skills in one of the most popular Single Page Application (SPA) frameworks today, AngularJS. Developed and backed by Google, AngularJS is a very marketable skill to acquire. In this course, we will explore the core design of AngularJS 1.x (latest version of AngularJS 1), its components and code organization techniques. We will enhance the functionality of our web app by utilizing dependency injection to reuse existing services as well as write our own. We will create reusable HTML components that take advantage of AngularJS data binding as well as extend HTML syntax with a very powerful feature of AngularJS called directives. We’ll set up routing so our SPA can have multiple views. We will also learn how to unit test our functionality. At the end of this course, you will build a fully functional, well organized and tested web application using AngularJS and deploy it to the cloud.
Johns Hopkins University
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
Johns Hopkins University
An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
Johns Hopkins University
Systems science has been instrumental in breaking new scientific ground in diverse fields such as meteorology, engineering and decision analysis. However, it is just beginning to impact public health. This seminar is designed to introduce students to basic tools of theory building and data analysis in systems science and to apply those tools to better understand the obesity epidemic in human populations. There will also be a lab in which students will use a simple demonstration model of food acquisition behavior using agent-based modeling on standard (free) software (netlogo). The central organizing idea of the course is to examine the obesity epidemic at a population level as an emergent properties of complex, nested systems, with attention to feedback processes, multilevel interactions, and the phenomenon of emergence. While the emphasis will be on obesity, the goal will be to explore ways in which the systems approach can be applied to other non-communicable diseases both nationally and internationally. Topics will include: a) the epidemiology of obesity across time and place, b) theories to explain population obesity, c) the role of environments and economic resources in obesity c) basic concepts and tools of systems science, d) modeling energy-balance related behaviors in context, e) agent-based models, systems dynamic models, and social network models
Johns Hopkins University
This course provides an introduction to systems thinking and systems models in public health. Problems in public health and health policy tend to be complex with many actors, institutions and risk factors involved. If an outcome depends on many interacting and adaptive parts and actors the outcome cannot be analyzed or predicted with traditional statistical methods. Systems thinking is a core skill in public health and helps health policymakers build programs and policies that are aware of and prepared for unintended consequences. An important part of systems thinking is the practice to integrate multiple perspectives and synthesize them into a framework or model that can describe and predict the various ways in which a system might react to policy change. Systems thinking and systems models devise strategies to account for real world complexities. This work was coordinated by the Alliance for Health Policy and Systems Research, the World Health Organization, with the aid of a grant from the International Development Research Centre, Ottawa, Canada. Additional support was provided by the Department for International Development (DFID) through a grant (PO5467) to Future Health Systems research consortium. © World Health Organization 2014 All rights reserved. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by the World Health Organization to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising from its use. Johns Hopkins University Bloomberg School of Public Health has a non-exclusive license to use and reproduce the material.
Johns Hopkins University
In this culminating course in the Patient Safety and Quality Improvement Specialization, you will apply the skills you have acquired across the previous six courses to address a realistic patient safety issue confronting Mercy Grace, a 500-bed urban hospital that is part of a larger hospital system. Based on the scenario provided, you will assess the situation and work through the problem using a variety of tools and strategies. You will have the opportunity to identify defects, root causes, and potential mitigation strategies; you will create a project implementation plan for addressing the issue in the form of an A3; you will identify risks of project failure and design a change management plan; you will identify means of converting the project from local to system-wide; and you will identify quality and safety measurements that will be used in evaluating the success of the project’s implementation.
Johns Hopkins University
In this course you will get an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
Johns Hopkins University
The People, Power, and Pride of Public Health provides an engaging overview of the incredible accomplishments and promise of the public health field. The first module includes interviews with legendary public health figures whose work led to millions of lives saved with vaccines, air bags and car seats, and the federal Women Infants and Children (WIC) nutrition program. The second module brings key public health tools to life – including use of data, communications, and policy – through discussions with experienced professionals who have used these tools to save lives. The third module includes a “Carpool Karaoke”-style trip through Baltimore County, Maryland with NACCHO President Dr. Umair Shah to see and hear real public health workers talking about how they serve their communities. Learners will come away from this course with a deeper understanding of the public health field and a greater enthusiasm for their own work in public health. Preview the course on YouTube: goo. gl/RXKbUr
Johns Hopkins University
This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you the knowledge of R needed to make useful contributions in those settings. As the first course in the Specialization, the course provides the essential foundation of R needed for the following courses. We cover basic R concepts and language fundamentals, key concepts like tidy data and related “tidyverse” tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks. Upon completing this course, learners will have fluency at the R console and will be able to create tidy datasets from a wide range of possible data sources.
Johns Hopkins University
Unix forms a foundation that is often very helpful for accomplishing other goals you might have for you and your computer, whether that goal is running a business, writing a book, curing disease, or creating the next great app. The means to these goals are sometimes carried out by writing software. Software can’t be mined out of the ground, nor can software seeds be planted in spring to harvest by autumn. Software isn’t produced in factories on an assembly line. Software is a hand-made, often bespoke good. If a software developer is an artisan, then Unix is their workbench. Unix provides an essential and simple set of tools in a distraction-free environment. Even if you’re not a software developer learning Unix can open you up to new methods of thinking and novel ways to scale your ideas. This course is intended for folks who are new to programming and new to Unix-like operating systems like macOS and Linux distributions like Ubuntu. Most of the technologies discussed in this course will be accessed via a command line interface. Command line interfaces can seem alien at first, so this course attempts to draw parallels between using the command line and actions that you would normally take while using your mouse and keyboard. You’ll also learn how to write little pieces of software in a programming language called Bash, which allows you to connect together the tools we’ll discuss. My hope is that by the end of this course you be able to use different Unix tools as if they’re interconnecting Lego bricks.
Johns Hopkins University
This course familiarizes students with the novel concepts being used to revamp regulatory toxicology in response to a breakthrough National Research Council Report “Toxicity Testing in the 21st Century: A Vision and a Strategy.” We present the latest developments in the field of toxicology-the shift from animal testing toward human relevant, high content, high-throughput integrative testing strategies. Active programs from EPA, NIH, and the global scientific community illustrate the dynamics of safety sciences.
Johns Hopkins University
Volunteer community health workers (CHWs) are a major strategy for increasing access to and coverage of basic health interventions. Our village health worker training course reviews the process of training and continuing education of CHWs as an important component of involving communities in their own health service delivery. Participants will be guided through the steps of planning training and continuing education activities for village volunteers. The course draws on real-life examples from community-directed onchocerciasis control, village health worker programs, community case management efforts, peer educators programs and patent medicine vendor training programs, to name a few.
Johns Hopkins University
Over 500,000 people in the United States and over 8 million people worldwide are dying from cancer every year. As people live longer, the incidence of cancer is rising worldwide, and the disease is expected to strike over 20 million people annually by 2030. Everyone has been, or will be touched by cancer in some way during their lifetime. Thanks to years of dedication and commitment to research we’ve made enormous advances in the prevention and treatment of cancer, But there is still a lot of work to be done. In this course, physicians and scientists at the Johns Hopkins School of Medicine explain how cancer spreads or metastasizes. We’ll describe the major theories of metastasis and then describe the biology behind the steps in metastasis. The course also describes the major organs targeted by metastasis and describes how metastases harm the patient.
Johns Hopkins University
Welcome to Understanding Prostate Cancer. My name is Ken Pienta, Professor of Urology and Oncology at the Johns Hopkins School of Medicine. I have been studying prostate cancer and treating patients with prostate cancer for over 25 years. Over 1,000,000 men worldwide and 230,000 men in the United States are diagnosed with prostate cancer every year. Three hundred thousand men worldwide and 30,000 men in the US are dying from prostate cancer every year. As people live longer, the incidence of prostate cancer is rising worldwide and prostate cancer continues to be a major health problem. Thanks to years of dedication and commitment to research we’ve made enormous advances in the treatment of prostate cancer, But there is still a lot of work to be done. In this Understanding Prostate Cancer course, I will provide an introduction to the biology of prostate cancer as well as how it is identified and treated at various stages of the disease. I’ve put together this course in order to introduce you to the essentials of prostate cancer. By the time you finish this course you’ll be able to Define risk factors for prostate cancer Understand current prostate cancer screening guidelines Understand prostate cancer staging Understand treatments for localized prostate cancer Understand treatments for advanced prostate cancer Understand treatments to alleviate the symptoms caused by prostate cancer This Understanding Prostate Cancer Course should be helpful to anyone who wants to develop a deeper understanding of prostate cancer biology and treatment. It should be useful to students who are interested in a deeper understanding of the science of cancer. It should also be helpful to health care providers, data managers, and educators who wish to develop a better understanding of prostate cancer and how it affects individuals. The course is not designed for patients seeking treatment guidance. For those of you who might be thinking about a career in cancer research or patient care, I hope this course will inspire you to pursue that path! The course is divided into five modules organized to facilitate learning. I’m glad that you decided to join this course. I hope that you will develop a basic understanding of prostate cancer. I hope that it will help you in whatever field you work. If you are a student, I hope that what you learn here will help you begin a career in cancer biology research and contribute to the worldwide effort to save lives.
Johns Hopkins University
Welcome to our course on Understanding and Strengthening Health Systems for Global Health. During the course we will provide you with an overview of the main elements or building blocks of a health system based on the World Health Organization’s guidance. You will have the opportunity to explore four main areas of health systems in global health with particular reference to low and middle income countries. The first area focuses on understanding health service organizations, the challenges. Our second module looks at WHO’s six major building blocks or health systems components with particular reference to primary health care and the need for community participation in planning, delivery and assessment of these systems components. in our third module we examine the specific systems component of human resource development and capacity building. The fourth area consists of health policy making and advocacy with stakeholders. This course is geared toward learners who are already involved in managing health and development programs on the ground in low and middle income countries or who are preparing for such a management role. The main lectures will span a four-week period with approximately 2-4 hours of viewing learning materials per week. We have one peer graded essay wherein you will use skills in ‘organizational’ diagnosis to better understand a challenge in an organization where you are or have worked. There are also quizzes. We hope you will engage with your fellow learners in discussion forums to learn from each other.
Johns Hopkins University
Public Mental Health is the application of the principles of medicine and social science to prevent the occurrence of mental and behavioral disorders and to promote mental health of the population. This course illustrates the principles of public health applied to depressive disorder, including principles of epidemiology, transcultural psychiatry, health services research, and prevention. It is predicted that by 2020 depressive disorder will be the most important cause of disease burden in the entire world! Every human being suffers from feeling depressed at some point or other, but only about one fifth of the population will experience an episode of depressive disorder over the course of their lives. This course illuminates the public health approach to disease, and the particular complexities of applying this approach to mental disorders, using depression as the exemplar.
Johns Hopkins University
This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We’ve left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to…. 1. Describe the basic data analysis iteration 2. Identify different types of questions and translate them to specific datasets 3. Describe different types of data pulls 4. Explore datasets to determine if data are appropriate for a given question 5. Direct model building efforts in common data analyses 6. Interpret the results from common data analyses 7. Integrate statistical findings to form coherent data analysis presentations Commitment: 1 week of study, 4-6 hours Course cover image by fdecomite. Creative Commons BY https://flic. kr/p/4HjmvD
Johns Hopkins University
This specialization is intended for public health and healthcare professionals, researchers, data analysts, social workers, and others who need a comprehensive concepts-centric biostatistics primer. Those who complete the specialization will be able to read and respond to the scientific literature, including the Methods and Results sections, in public health, medicine, biological science, and related fields. Successful learners will also be prepared to participate as part of a research team.
Johns Hopkins University
This Specialization is intended for health professionals, administrators, health IT staff, vendors, startups, and patients who need or want to participate in the health IT/informatics process. Throughout the five courses of this Specialization, you will learn about the social and technical context of health informatics problems, how to successfully implement health informatics interventions, how to design a health informatics solution for decision support, and how to answer a health informatics problem through data retrieval and analysis.
Johns Hopkins University
This capstone course in the Health Informatics Specialization will allow learners to create a comprehensive plan for an informatics intervention of their choosing, and that will demonstrate to current or future employers the new skills obtained through the completion of this series of five courses in Health Informatics.
Johns Hopkins University
Do a lot of people in your neighborhood all seem to have the same sickness? Are people concerned about high rates of cancer? Your community may want to explore the possibility of a disease cluster, which happens when there is a higher number of cases of disease than expected. When communities hear about cases of disease in their neighborhood, they are rightfully concerned. However, the results of investigations by the health department often find no evidence of a cluster. This course will help you understand what a disease cluster is and how it is studied. The goal is to empower community (or citizen) scientists, and to help build better relationships between communities and health officials.
Johns Hopkins University
Biostatistics is an essential skill for every public health researcher because it provides a set of precise methods for extracting meaningful conclusions from data. In this second course of the Biostatistics in Public Health Specialization, you’ll learn to evaluate sample variability and apply statistical hypothesis testing methods. Along the way, you’ll perform calculations and interpret real-world data from the published scientific literature. Topics include sample statistics, the central limit theorem, confidence intervals, hypothesis testing, and p values.
Johns Hopkins University
Do you dream of being a CMIO or a Senior Director of Clinical Informatics? If you are aiming to rise up in the ranks in your health system or looking to pivot your career in the direction of big data and health IT, this course is made for you. You’ll hear from experts at Johns Hopkins about their experiences harnessing the power of big data in healthcare, improving EHR adoption, and separating out the hope vs hype when it comes to digital medicine. Whether you’re a nurse, pharmacist, physician, other allied health professional or come from a non-clinical background-you know that Health Informatics skills are in demand. This newly launched 5-course specialization by JohnsHopkins faculty members provides a solid foundation for anyone wanting to become a leader in one of the hottest fields in healthcare. As health informaticians, we need to be very clear in our understanding of the current state (as-is), the future state (to-be) and any unintended consequences that can result from our interventions. Prior to introducing large scale change, we need to assess whether a healthcare organization is truly ready for change. This involves taking into account an organization’s current culture and values. Successfully leading change through health informatics also requires strategic planning and careful financial considerations. Proper workflow redesign and a clear change management strategy are of utmost importance when introducing new technologies and in ensuring their successful adoption and proper use. By the end of this course, students will become familiar with examples of successful and failed attempts at change in health informatics, and the reasons for each. Students will be armed with tools to help optimize their chances for successfully leading change in their respective organizations.
Johns Hopkins University
Biostatistics is the application of statistical reasoning to the life sciences, and it’s the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, you’ll extend simple regression to the prediction of a single outcome of interest on the basis of multiple variables. Along the way, you’ll be introduced to a variety of methods, and you’ll practice interpreting data and performing calculations on real data from published studies. Topics include multiple logistic regression, the Spline approach, confidence intervals, p-values, multiple Cox regression, adjustment, and effect modification.
Johns Hopkins University
This course is an introduction to the multiple ways our changing climate affects global population health, and to promising policy and practice responses. More intense storms, heatwaves, and rising seas mean many, particularly the most vulnerable, now face growing risks of weather-related injury, illness, mental stress and even death. Because people care deeply about health outcomes, public health has great potential to convey the urgency of reducing greenhouse gas emissions and adapting to a warmer, more unpredictable climate. The main message of the course is that public health must therefore “lean in” and become a more central player in climate change mitigation and adaptation. Because climate-related health risks happen mainly at the local level, the course focuses on cities – increasingly key players in climate change policy. Starting with an overview of the science consensus suggesting we have 10-20 years to prevent risks associated with exceeding 1.5°C of global warming and put in place adaptive policies, the course provides interactive lectures, expert interviews and case studies that build practical knowledge. In the final assignment, participants apply course tools and strategies to a city of their choice, preparing them to contribute to climate mitigation and building health resiliency in their own local context.
Johns Hopkins University
Biostatistics is the application of statistical reasoning to the life sciences, and it’s the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, we’ll focus on the use of simple regression methods to determine the relationship between an outcome of interest and a single predictor via a linear equation. Along the way, you’ll be introduced to a variety of methods, and you’ll practice interpreting data and performing calculations on real data from published studies. Topics include logistic regression, confidence intervals, p-values, Cox regression, confounding, adjustment, and effect modification.
Johns Hopkins University
Biostatistics is the application of statistical reasoning to the life sciences, and it is the key to unlocking the data gathered by researchers and the evidence presented in the scientific literature. In this course, we’ll focus on the use of statistical measurement methods within the world of public health research. Along the way, you’ll be introduced to a variety of methods and measures, and you’ll practice interpreting data and performing calculations on real data from published studies. Topics include summary measures, visual displays, continuous data, sample size, the normal distribution, binary data, the element of time, and the Kaplan-Meir curve.
Johns Hopkins University
Health data are notable for how many types there are, how complex they are, and how serious it is to get them straight. These data are used for treatment of the patient from whom they derive, but also for other uses. Examples of such secondary use of health data include population health (e.g, who requires more attention), research (e.g, which drug is more effective in practice), quality (e.g, is the institution meeting benchmarks), and translational research (e.g, are new technologies being applied appropriately). By the end of this course, students will recognize the different types of health and healthcare data, will articulate a coherent and complete question, will interpret queries designed for secondary use of EHR data, and will interpret the results of those queries.
Johns Hopkins University
For clinical data science to be effective in healthcare-to achieve the outcomes desired-it must translate into decision support of some sort, either at the patient, clinician, or manager level. By the end of this course, students will be able to articulate the need for an intervention, to right size it, to choose the appropriate technology, to describe how knowledge should be obtained, and to design a monitoring plan.
Johns Hopkins University
Improving health and healthcare institutions requires understanding of data and creation of interventions at the many levels at which health IT interact and affect the institution. These levels range from the external “world” in which the institution operates down to the specific technologies. Data scientists find that, when they aim at implementing their models in practice, it is the “socio” components that are both novel to them and mission critical to success. At the end of this course, students will be able to make a quick assessment of a health informatics problem-or a proposed solution-and to determine what is missing and what more needs to be learned.
Johns Hopkins University
Reducing Gun Violence in America: Evidence for Change is designed to provide learners with the best available science and insights from top scholars across the country as well as the skills to understand which interventions are the most effective to offer a path forward for reducing gun violence in our homes, schools, and communities. Through this course, you will learn how to: 1. Appreciate the scope of gun violence and the importance of considering the issue across a variety of contexts. 2. Describe the role of law and policy in addressing gun violence at the federal, state, and local levels. 3. Compare the effectiveness of gun violence policies and highlight the importance of changing the way we talk about gun violence. 4. Describe state standards for civilian gun carrying and use and how those standards affect crime and violence. 5. Describe how firearm design is regulated, the effective and just enforcement of firearm laws, and strategies for reducing police-involved shootings. 6. Identify and explain evidence-based programs to reduce gun violence and understand public opinion on gun policy. As a student of this course, it’s important to recognize that you are part of an international learning community. We understand that gun violence can be a difficult issue to discuss and we all have our own set of opinions and beliefs. However, we ask you to please remember that gun violence is something that affects every person differently. It’s important to understand that this course is intended to generate productive and meaningful conversation. We ask students of this course to abide by the rules outlined and expected of you by Coursera’s Code of Conduct Policy. Please help us promote a healthy, productive, and sustainable learning environment for all and practice the following principles: Be polite. Treat your fellow learners with respect. Insulting, condescending, or abusive words will not be tolerated. Do not harass other learners. Polite debate is welcome as long as you are discussing the ideas or the evidence, not attacking the person. Be sensitive. Remember that Coursera is a global forum with learners from many different cultures and backgrounds. Be kind, thoughtful, and open-minded when discussing race, religion, gender, sexual orientation, or controversial topics since others likely have differing perspectives. Post appropriate content. Content that violates the Honor Code or Terms of Service is not permitted. You may not post inappropriate (e.g, pornographic or obscene) content. Do not post copyrighted content. Do not advertise or promote outside products or organizations. Do not spam the forums with repetitive content. Please note that violations of Coursera’s Code of Conduct, Honor Code, or Terms of service are not permitted and may result in: Deletion of posted content. Removal from the course. Losing access to the Coursera site. *It is important to also note that certain policies and laws mentioned in this course are likely to change and develop over time. As a result, we understand that course content may need to be updated to reflect such changes. While we will strive to do this as quickly as possible, we appreciate your patience. All information included in the course is reported accurately at the time of recording and we intend to update lectures as soon as possible and to the best of our ability. The development of this course was made possible through generous financial support provided by the David and Lucile Packard Foundation, an organization committed to improving the lives of children, enabling the creative pursuit of science, advancing reproductive health, and conserving and restoring the earth’s natural systems.
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