The Global Scholarly Directory.
Discover world-class academic programs curated for the modern intellectual. Search through 19877+ degrees and professional certificates.
University of Maryland, College Park
Applied Public Health Informatics for Leaders
Public Health Informatics is critical for informed decision-making for those who affect public health. The purpose of this course, Applied Public Health Informatics for Leaders, is to provide leaders and aspiring leaders with an overview of key principles, tools, methodologies, data sources, terminologies, and policy issues as they relate to the importance of public health informatics to public health leaders. An overview of national e-Health, public health, and health care reform priorities and strategies, and their implications for public health leadership will help you to learn the landscape and for effective application.
Fractal Analytics
Structured Approach to Problem Solving
This course is an introductory course that equips you with the concepts and tools of problem thinking. By the end of this course, you will be able to: 1. Explain the different stages of a data science project 2. Discuss some of the tools and techniques used in data science. 3. Apply structured thinking to solving problems and avoid the common traps while doing so 4. Apply human-centric design in problem-solving. This course also acts as the first stepping-stone for aspiring data scientists. This course is a part of the program titled Fractal Data Science Professional Certificate. This course is tailored for learners seeking to enhance their analytical and critical thinking abilities. Ideal for students, professionals, and anyone intrigued by systematic approaches, this course empowers you with the foundational skills needed to approach problems with clarity and confidence.
Edge Impulse
Edge AI Fundamentals
Welcome to the Edge AI Fundamentals course! The is a high-level, introductory course to help you become familiar with the concepts and vocabulary around edge AI. There are no hands-on exercises or programming required.
EDUCBA
Embedded Systems using C
Embark on a comprehensive journey into Embedded Systems with this course. Module 1, "Introduction to Embedded Systems," lays the foundation by exploring principles, architectures, and essential devices. In Module 2, "Programming Fundamentals in C," participants master C programming essentials, including operators, storage classes, and flow control structures. Transitioning to Module 3, "Advanced Concepts in C for Embedded Systems," participants delve into functions, arrays, pointers, and string manipulation techniques. By the course's conclusion, participants emerge equipped with the knowledge and skills necessary for confident navigation and programming in C for embedded systems development. Learning Outcomes: Upon completing this course, participants will: 1) Gain a solid understanding of Embedded Systems principles, architectures, and essential devices. 2) Master C programming essentials, including operators, storage classes, and flow control structures. 3) Dive deep into advanced C concepts crucial for embedded systems, such as functions, arrays, pointers, and string manipulation techniques. 4) Acquire practical skills through hands-on projects and exercises, enhancing their ability to develop embedded systems applications. 5) Develop confidence in their ability to design, program, and troubleshoot embedded systems using C. Target Learners: 1) Electrical Engineering or Computer Science Students: Those pursuing degrees in electrical engineering or computer science, particularly with an interest in hardware-software integration and low-level programming. 2) Embedded Systems Professionals: Engineers or professionals already working in the field of embedded systems who want to deepen their understanding of C programming for embedded applications and enhance their skill set. Pre-requisites: 1) Basic Programming Knowledge: Familiarity with programming concepts such as variables, loops, functions, and data structures. 2) Understanding of C Programming Language: Proficiency in the C programming language including syntax, data types, pointers, memory management, and basic file operations. 3) Fundamental Electronics Knowledge: Basic understanding of digital electronics, microcontrollers, and input/output (I/O) interfacing concepts. 4) Computer Architecture Basics: Knowledge of computer architecture fundamentals such as CPU, memory, input/output devices, and the concept of interrupts.
Illinois Tech
Systems Integration - Bachelor's
In this capstone course, students will identify, gather, analyze, and write requirements based on user needs and will then design, construct, integrate, and implement an information system as a solution to a business problem. Students will document integration architecture, methodologies, and technologies using industry best practices. User needs and user centered design will be applied in the selection, creation, evaluation, and administration of the resulting system. The system design process will take into account professional, ethical, legal, security, and social issues and responsibilities and stress the local and global impact of computing on individuals, organizations, and society. Discussion will also cover the need to engage in continuing professional development.
Coursera
Embed Everything
Embed Everything is an intermediate-level course designed for machine learning practitioners and Python developers who want to master the art of converting unstructured data into powerful numerical representations. In a world where data is king, its value is often locked away in complex formats like product descriptions, images, and documents. This course provides the key to unlocking that value. You will learn to build a complete, scalable embedding pipeline from the ground up. Through practical, hands-on labs and expert-led video lessons, you'll apply state-of-the-art pre-trained models to transform raw text and images into meaningful vector embeddings. But creating embeddings is only half the battle. You will also master the crucial skill of evaluation, using powerful visualization techniques like t-SNE and nearest-neighbor analysis to verify that your embeddings capture the true semantic meaning of your data. By the end of this course, you will have written a production-style Python script to batch-process a large dataset, a skill directly applicable to real-world scenarios like Walmart's semantic search engine. Intermediate Python and basic ML skills required. Experience with NumPy and scikit-learn is beneficial.
HEC Paris
CAPSTONE: Your Leadership Challenge
Welcome to the capstone project course in the Coursera Inspirational Leadership Specialization! In the next 6 weeks you'll apply the skills you learned in the Specialization to tackle a real world leadership challenge. To better meet your own goals of taking this Specialization, we will provide one capstone project: -Personal leadership challenge; You'll select one project to implement and will receive assessments from your Capstone peers. No matter which option you choose, the capstone project will examine your skills, including self-awareness, developing quality relationships and trust, resilience, and how making decisions entails a strong sense of responsibility. For the personal challenge, you will work on your own question addressing your capacity to grow as a leader. A challenge can be a target objective (starting a company, running a project, designing a product, winning a competition, running a marathon…) you have for yourself which puts into question what you want to achieve and how. The Capstone will help you measure the challenge, establish a process and build the steps to meet your goals. Whatever personal challenge you choose, specific questions will attest to your capacity to analyze and criticize the the problem which is faced by by yourself. The process that will drive the Business plan will follow step by step the relational circuit model developed in MOOC2 “Giving Sense to your Leadership Experience” and MOOC3 “Leading Organizations” with its two phases of Exploration and Projection. In the EXPLORATION, you will engage in a first step of holistic perception of the problem using your sensible skills to give a sense of perspective and give voice to you subjective apprehension of the situation as a whole. The second stage of exploration will involve your analytical skills to understand each and every component (elements of data relevant to the challenge) that plays a role in defining the situation. And the third stage of exploration will apply your relational skills where you will make sense and connect your subjective perception with your analysis. You will share the exploration results with peers to receive feedback before your can move on. The second phase, the PROJECTION part of your business plan leading to recommendations for the personal challenge at stake. In the projection phase you will first look at what the situation might be when you decenter the problem in time and space. Using benchmark and competitive analysis, you will work on different scenarios for the projected outcomes. Finally, with a view to build sense out of the challenge you will come up with recommendations arguing for a preferred scenario.
Universidad de Palermo
Motivando Equipos de Trabajo
Desde que abrimos los ojos estamos envueltos en una experiencia compartida. Equipos propios o ajenos que nos sumergen en nuestros mundos o nos llevan por otros espacios y tiempos. ¿Pero cuándo hay equipo? ¿Cómo activar esa inteligencia colectiva que nos hace sentir orgullosos de pertenecer? ¿Cómo elegir un modelo donde todos seamos uno? ¿Cuál es la fuente que alimenta a esta experiencia? ¿Qué es lo que sostiene al ecosistema vital del equipo? Abordaremos la respuesta a estas preguntas utilizando el modelo del Smarted, desarrollado en el Laboratorio del Disfrute desde el 2012 en la Universidad de Palermo, que explora la neurociencia aplicada a la educación. El modelo integra los conocimientos propios, que fuimos adquiriendo en la cocreación conjunta, junto con aquello que se comprende mientras se hace, sumado a lo que se alcanza cuando el aprendizaje interactúa paso a paso en el marco del disfrute. El recorrido en formato Smarted permitirá crear un conocimiento único enlazando teoría, mentoría y talento humano. Al finalizar, descubriremos juntos el secreto mejor guardado que siempre estuvo frente a nuestros ojos para gritar juntos que ¡hay equipo! Objetivos del curso: ● Conocer e identificar los elementos de un equipo. ● Reconocer modelos de equipos sustentables para aplicar a la toma de decisión anticipada. ● Identificar el talento como fuente básica de la motivación del trabajo en equipo. ● Activar el engagement como potenciador de la inteligencia colectiva. ● Integrar los conocimiento teóricos con la aplicación personalizada por medio de la neurociencia aplicada a la educación disfrutable (Smarted).
University of Geneva
Troubles du spectre de l'autisme : biologie et neurosciences
Quelles sont les causes de l’autisme ? Où en est la recherche dans le domaine de la neurobiologie de l'autisme ? Que peuvent apporter les nouvelles technologies dans notre compréhension de l’autisme et de son évolution ? Suite à un premier MOOC sur le diagnostic des Troubles du Spectre de l'Autisme (TSA), nous vous proposons un deuxième volet axé sur la biologie et les neurosciences du TSA. Différents sujets seront ainsi discutés par des experts dans les domaines scientifiques qui ont connu une formidable avancée au cours de ces dernières années et ont amené un nouveau regard sur l’autisme. Dans ce cours en ligne et gratuit, vous découvrirez l'état actuel de la recherche en termes de causes génétiques et environnementales des TSA, ainsi que des différents modèles de compréhension qui ont été proposés par la communauté scientifique jusqu'à présent. Nous aborderons également des sujets comme le neurodéveloppement, les troubles du sommeil et de l'alimentation, la pharmacologie et finalement l'apport de la neuroimagerie ou des nouvelles technologies telles que l’eyetracking. Que vous travailliez dans des domaines tels que la médecine, la psychologie, l'éducation ou l'aide à la petite enfance, ou que vous soyez une personne proche aidante, vous trouverez les réponses à de nombreuses questions dans ce MOOC.
SAS
Generative AI Using SAS
Generative Artificial Intelligence (GenAI) is a rapidly developing area of machine learning, with application across business, government, and academia. In this course, you will learn about different types of GenAI and see examples of how SAS can enhance your efforts to make the most of these techniques. Learn How To: 1. Explain what generative AI is and how it fits into the broader AI landscape. 2. Describe several types of GenAI systems. 3. Name some of the key challenges and opportunities in making a trustworthy AI system. 4. Generate synthetic data with Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GANs). 5. Explain how Large Language Models (LLMs) generate meaningful text. 6. Classify text for LLMs using Bidirectional Encoder Representations from Transformers (BERT). 7. Improve the accuracy and relevance of LLM output using Retrieval Augmented Generation (RAG). Who Should Attend: Learners who want to know more about the techniques that comprise GenAI and how to make use of them with SAS Prerequisites: Before taking this course, you should have some background in statistics and machine learning using SAS. You can gain this knowledge by taking the following courses: 1. Statistics You Need to Know for Machine Learning 2. Machine Learning Using SAS Viya
University of Michigan
Работа с базами данных в Python
В настоящем курсе рассматриваются основы структурированного языка запросов (SQL) и проектирования баз данных как отдельного этапа процесса сбора, анализа и обработки данных. В качестве системы управления базой данных в курсе используется библиотека SQLite3. Мы научимся создавать поисковых роботов, а также многоэтапные процессы сбора и визуализации данных. Для простой визуализации данных мы воспользуемся библиотекой D3.js. В данном курсе рассматриваются разделы 14–15 книги «Python для всех». Для успешного прохождения курса необходимо ознакомиться с материалами разделов учебника 1–13, а также трех первых курсов по данной специализации. В этом курсе изучается язык Python 3.
Packt
AI Agents and MLOps for Production-Ready AI
This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will gain in-depth knowledge and hands-on experience with AI agents and MLOps, crucial components for developing and deploying production-ready AI solutions. You will begin by exploring various AI agents, including AutoGen, IBM Bee, LangGraph, CrewAI, and AutoGPT. The course provides practical insights on how these frameworks can automate AI workflows and create autonomous AI agents. You will have the opportunity to implement these agents, developing AI-driven systems that can carry out tasks like decision-making, automation, and optimization. The second part of the course delves into MLOps, focusing on the operationalization of machine learning models. You’ll explore MLOps concepts such as versioning, automation, and monitoring, and how they fit into the broader context of machine learning deployment. Through hands-on exercises, you will learn to set up MLOps environments using tools like Git, Docker, and Kubernetes, and develop end-to-end machine learning pipelines. The course emphasizes the critical differences between experimentation and production in machine learning, teaching you how to build robust systems that can seamlessly move from development to deployment. The course also covers the necessary infrastructure for MLOps, including cloud platforms like AWS, GCP, and Azure, and how to containerize models using Docker. You will gain practical skills in deploying and managing machine learning models at scale using Kubernetes, ensuring your models are production-ready and scalable. This comprehensive journey will provide you with the tools to manage ML workflows, optimize deployment processes, and integrate AI agents into production environments. This course is designed for AI practitioners, data scientists, and engineers interested in taking their machine learning and AI systems to production. A basic understanding of machine learning concepts and programming is recommended, as the course focuses on applying these concepts in real-world production settings. Suitable for intermediate learners, this course provides both theoretical knowledge and practical experience in AI and MLOps. By the end of the course, you will be able to implement AI agents using advanced frameworks, set up MLOps pipelines, containerize and deploy models, and manage machine learning models in cloud and on-premise environments.
Harvard Business Review
Lead with Integrity
Integrity and inclusion are the foundations of a sustainable and successful organization. This course empowers leaders to make principled decisions and cultivate a workplace where every individual can thrive. You’ll learn to navigate complex ethical dilemmas using practical frameworks that balance competing "right-versus-right" priorities. You’ll also learn strategies to support diversity in the workplace, interrupt unconscious bias, and foster a culture of respect. Using these skills will help you boost performance, foster transparency, and earn trust.
University of Illinois Urbana-Champaign
Finance of Mergers and Acquisitions: Valuation and Pricing
This course teaches how to value and price M&A deals and to choose the optimal financing mix for an M&A deal. The course focuses on all the major types of M&A deals including strategic M&A, private equity leveraged buyouts (LBOs), and restructuring deals such as spinoffs and asset transfers.
HRCI
Compensation and Benefits
This course examines the intricacies of the total rewards package for employment. You will learn how to structure a compensation strategy and evaluate benefit trends in the market. You will also learn about different benefit types and options as well as various pay systems and HR technology. By the end of this course, you will be able to: ● Explain the most common theories and motivational principles associated with total rewards ● Evaluate how your organization wants to structure compensation strategy ● Explain how job evaluation techniques are used to align compensation strategy with actual pay ● Explain different benefit types and options ● Appraise different types of pay systems No prior experience in Human Resources is needed to be successful in this course.
University at Buffalo
The Factors that Influence the Effectiveness of Boards and the Governance Process
The third course in this Specialization introduces you to the factors that influence how effective boards of directors will be in carrying out their roles and responsibilities and hence the impact they have in shaping the success of the organization they govern. While this course has been developed with North American culture in mind, we do appreciate that, in other parts of the world, the nature of the factors that influence the effectiveness of nonprofit boards of directors may vary. Nevertheless,it is our hope that much of the course content will still be of value to those in other parts of the world. To learn more about this course, please watch the overview video by copying and pasting the following link into your web browser: https://goo.gl/aAMIfl. Keywords: Nonprofit; Nonprofit Sector; Voluntary Sector; Nonprofit Organizations, Non-Governmental Organizations, Volunteer Organizations, Leadership, Management, Governance, Board, Board of Directors, Performance, Effectiveness Course 3 Overview: Week 1: This week's questions: What factors influence the effectiveness of board meetings? What are the formal procedures and structures within the board that impact its performance? What can a board do to intentionally improve the way it structures itself and runs its decision making meetings? Week 2: This week's questions: What do we mean by board composition and development and why is it important? Why is it difficult to change the composition of the board? How do you design the ideal mix of board members? How do you locate, recruit and develop board members? Week 3: This week's questions: What do we mean by the "culture" of the board and why is it important? How do boards develop and pass along culture? Can board cultures be intentionally changed? What is the role of leadership in shaping board culture? What do effective nonprofit leaders involved in governance do? How can leadership be managed for higher performance? Week 4: At this stage, you are asked to review the course content, submit a written assignment (known lightheartedly as a BEAR (Board Effectiveness Readiness Assessment), and take two multiple choice Readiness Assurance Tests (known similarly as RATs). One RAT will assess knowledge and reading comprehension and the other will test application of knowledge within a practical case. Week 5: We will encourage you to discuss the RATs in the discussion forums and take them again should you wish to change any of your answers based on the information exchanged.