The Global Scholarly Directory.
Discover world-class academic programs curated for the modern intellectual. Search through 19877+ degrees and professional certificates.
Georgia Institute of Technology
Introduction to Electronics
This course introduces students to the basic components of electronics: diodes, transistors, and op amps. It covers the basic operation and some common applications.
Coursera
Analyze & Deploy Scalable LLM Architectures
Analyze & Deploy Scalable LLM Architectures is an intermediate course for ML engineers and AI practitioners tasked with moving large language model (LLM) prototypes into production. Many powerful models fail under real-world load due to architectural flaws. This course teaches you to prevent that. You will learn to analyze multi-stage architectures such as RAG to diagnose and quantify performance bottlenecks with evidence, not assumptions. You will then master the tools of production-grade operations, designing and writing declarative Helm charts to deploy containerized LLM applications on Kubernetes. The curriculum focuses on building resilient, scalable systems by implementing Horizontal Pod Autoscaling (HPA) to handle unpredictable traffic and managing the full deployment lifecycle with controlled rollouts and rapid rollbacks. By the end of this course, you will be able to transform fragile prototypes into robust, reliable, and scalable production services.
ESSEC Business School
Originalité et modernité du mutualisme
Bienvenue dans ce MOOC qui a été conçu pour vous accompagner dans l’exploration de l’originalité et de la modernité des mutuelles ! Que vous soyez salarié ou élu d’une mutuelle, que vous envisagiez de travailler dans ce secteur ou que vous soyez un sociétaire impliqué soucieux de comprendre ce qui se cache derrière la différence mutualiste, ce qui caractérise les mutuelles, en quoi elles sont originales et pourquoi les mutuelles sont plus que jamais des vecteurs privilégiés d’innovation sociale, ce MOOC est fait pour vous ! Le parcours de formation que vous allez suivre s’articule autour de 4 épisodes : Episode 1/ L’assurance, fonctions économiques et sociales Episode 2/ La mutuelle, une façon différente d’assurer les biens et les personnes Episode 3/ La mutuelle, un acteur engagé dans la société Episode 4/ La mutuelle face aux défis du XXIème siècle Ce que nous vous proposons avec ce MOOC, conçu en partenariat avec la MACIF, c'est de découvrir l’ensemble de ces sujets au travers du témoignage de nombreux dirigeants de mutuelles et d'experts. En fonction de vos objectifs et de vos centres d’intérêt prioritaires, vous pourrez adapter votre parcours en choisissant les interventions qui y répondent le mieux. A l'issue de ce MOOC, vous serez capable de : - Expliquer les fondamentaux de l’assurance : comment elle a émergé, comment elle fonctionne, les grands jalons qui ont marqué son histoire, le panorama de ses principaux acteurs aujourd’hui - Identifier les spécificités de la gouvernance du modèle mutualiste et l’environnement réglementaire mouvant dans lequel il évolue - Expliquer en quoi les mutuelles sont des assureurs « pas comme les autres », comment ils s’engagent au bénéfice de la société - Décrire comment les mutuelles se positionnent face aux grands défis du XIXème siècle, vieillissement et dépendance, changement climatique, gestion des données personnelles et digitalisation, nouvelles formes de mobilité et d’habitat Bonne découverte à tous ! Thierry Sibieude, professeur ESSEC, titulaire de la Chaire Innovation et Entrepreneuriat Social
University of Pittsburgh
Big Data Processing with Hadoop and Spark
Master the tools and techniques that power large-scale data processing and analytics. This course introduces the principles and frameworks of Big Data Processing with Hadoop and Spark, enabling learners to manage, process, and analyze massive datasets efficiently. You’ll start by understanding the Hadoop ecosystem, including HDFS and MapReduce, and how distributed storage and computation work together to handle data at scale. Then, you’ll explore Apache Spark, a powerful framework for fast, in-memory data processing and real-time analytics. Through guided exercises and case studies, you’ll learn how to build scalable data pipelines, optimize performance, and apply transformations for business insights. By the end of this course, you’ll be equipped to handle complex data workloads using industry-standard big data tools. Ideal for aspiring data engineers, analysts, and developers, this course bridges data management and cloud computing—preparing you to design, implement, and manage big data solutions that drive intelligent decision-making in modern organizations.
The University of Chicago
Machine Learning: Concepts and Applications
This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning. A key feature of this course is that you not only learn how to apply these techniques, you also learn the conceptual basis underlying them so that you understand how they work, why you are doing what you are doing, and what your results mean. The course also features real-world datasets, drawn primarily from the realm of public policy. It is based on an introductory machine learning course offered to graduate students at the University of Chicago and will serve as a strong foundation for deeper and more specialized study.
Packt
Introduction to FastAPI and Backend Development Fundamentals
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 a solid foundation in backend development with FastAPI, a powerful Python framework. You will start by understanding the fundamentals of REST APIs, dive into FastAPI's core features, and discover why it is the framework of choice for modern backend development. By the end of this course, you'll be ready to build efficient, scalable APIs and backend systems using Python and FastAPI. The journey starts with a deep dive into REST APIs and FastAPI, followed by practical modules on setting up and using FastAPI in a real development environment. You’ll learn how to create API endpoints, handle parameters, and manage error responses effectively. The course then progresses into database management with SQL and SQLModel, covering everything from basic CRUD operations to advanced concepts like async programming with PostgreSQL. This course is designed for aspiring backend developers, those looking to advance their skills with FastAPI, or anyone wanting to get hands-on experience with Python backend frameworks. There are no strict prerequisites, but some familiarity with Python programming and web development concepts would be beneficial. By the end of the course, you will be able to build and manage dynamic REST APIs using FastAPI, implement various HTTP methods, integrate databases with FastAPI, and apply advanced techniques like asynchronous programming with PostgreSQL.
Codio
C++ Basics: Selection and Iteration
Code and run your first C++ program in minutes without installing anything! This course is designed for learners with no coding experience, providing a solid foundation of not just C++, but core Computer Science topics that can be transferred to other languages. The modules in this course cover printing, operators, iteration (i.e., loops), and selection (i.e., conditionals). To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You'll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours.
Pontificia Universidad Católica del Perú
Design Thinking - Parte 1: Descubre tu solución de negocio con IA Generativa
En este curso aprenderás a dar los primeros pasos para diseñar una solución de negocio con propósito, aplicando el enfoque de Design Thinking y aprovechando el potencial de la Inteligencia Artificial Generativa. A lo largo de cuatro módulos, explorarás cómo identificar una necesidad social relevante, analizar a la competencia, comprender mejor a tus usuarios y reconocer sus principales puntos de dolor para sentar bases sólidas en el diseño de una propuesta innovadora. Mediante una experiencia práctica y aplicada, trabajarás las fases de empatizar y definir del proceso de Design Thinking, utilizando herramientas de IAG como apoyo para recopilar información, analizar datos y enriquecer tu comprensión del problema y del contexto. Al finalizar, contarás con una visión más clara del reto que deseas abordar y con insumos clave para avanzar hacia el diseño de una solución de negocio viable y con impacto social.
Kennesaw State University
Team Management for the 6 σ Black Belt
This course is designed for professionals interested in learning the principles of Lean Sigma, the DMAIC process and DFSS. This course is number 2 of 8 in this specialization dealing with topics in Team Management Professionals with some completed coursework in statistics and a desire to drive continuous improvement within their organizations would find this course and the others in this specialization appealing. Method of assessment consists of several formative and summative quizzes and a multi-part peer reviewed project completion regiment.
Technical University of Munich (TUM)
Digitalisation in Aeronautics
The instructors of the online course "Digitalisation in Aeronautics" present a spectrum of different aviation research and application areas, exploring the impact of digitalisation in this specific field, including the effects of digitalisation in simulating the interaction of aircraft components, in overall aircraft development and related decision-making and in the communication channels used within aircraft. A broad and varied range of applications and digital solutions are explored in detail in the individual modules of this course.
Coursera
Navigating Compliance in the Food and Beverage Industry
This comprehensive course is tailored for restaurant owners, managers, and employees new to the intricacies of compliance in the food and beverage service industry. The course delves into critical areas such as food hygiene, cleaning, health and safety, food allergy management, and related legal regulations. Participants will explore a wide range of global hotel and restaurant compliance requirements, ensuring they can effectively adapt to various environments. Topics covered include the development and implementation of employee training policies, maintaining high standards of food safety and hygiene, and ensuring legal compliance within food service settings. This course is ideal for restaurant owners, managers, and staff who are beginning their journey in the food and beverage service industry. It is also suitable for anyone seeking a foundational understanding of compliance and safety requirements in a global context. Learners will gain practical knowledge and skills to ensure their restaurants operate within the bounds of the law, maintain high standards of safety and hygiene, and protect their business from legal and litigation risks. Participants should have a foundational understanding and some experience working in food and beverage service environments, such as restaurants or hotels. This background will help learners grasp the course content more effectively and apply the concepts in real-world settings. By the end of this course, learners will be equipped to apply essential food safety requirements within a restaurant environment, ensuring a safe and compliant operation. They will also be able to manage safety and risk assessments in kitchens and dining areas to protect both staff and customers. Additionally, learners will gain the skills to implement core cleaning management strategies and effectively evaluate and manage training needs within their teams.
Whizlabs
AWS: Model Training , Optimization & Deployment
AWS: Model Training, Optimization & Deployment is the third course in the Exam Prep (MLA-C01): AWS Certified Machine Learning Engineer – Associate Specialization. This course is designed to equip learners with the skills to train, optimize, and deploy machine learning models efficiently using AWS services. Learners begin by exploring popular algorithms such as Linear Learner, XGBoost, LightGBM, and k-Nearest Neighbors (k-NN), and understand their use cases in classification and regression tasks.You’ll then dive into the model training process, learning how to configure key parameters like epochs, batch size, and steps for optimized performance. Then the learners will begin by exploring SageMaker Model Debugger and SageMaker Experiments, which help monitor training jobs and compare experiment results efficiently.You’ll then dive into cross-validation techniques and learn how to apply hyperparameter tuning using both random search and Bayesian optimization methods to improve model accuracy. Finally by exploring compute options such as Amazon ECS, Amazon EKS, and AWS Lambda, followed by infrastructure management with AWS CloudFormation.You’ll learn how to implement auto scaling policies for ML workloads and choose the right SageMaker compute instance types (CPU vs. GPU) for different deployment scenarios. This course is divided into three comprehensive modules, each containing targeted lessons and practical demonstrations. Learners will benefit from approximately 3.5 to 4 hours of expert-led video content, featuring real-world use cases and hands-on walkthroughs using AWS tools. Every module includes Graded and Ungraded Quizzes to assess conceptual understanding and application. Module 1: Model Training, Algorithms & Inference Techniques Module 2: Model Optimization, Evaluation & Tuning with SageMaker Module 3: Scalable Infrastructure & Automated ML Deployment on AWS By the end of this course, learners will be able to: Compare real-time and batch inference approaches to determine the best strategy for model deployment. Apply model optimization techniques such as hyperparameter tuning Understand and select appropriate inference strategies for deployment Explore AWS compute and orchestration services like ECS, EKS, Lambda, and CloudFormation for ML deployment. This course is ideal for ML practitioners, data scientists, and cloud developers who are looking to scale their ML workflows and gain hands-on experience with advanced features of Amazon SageMaker. It is also designed for learners preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam, focusing on the model training and deployment aspects of the certification.
University of Illinois Urbana-Champaign
Corporate Strategy
In this course you will learn how corporations create, capture, and maintain value, going beyond the management of a single line of business. It is thus a complement to (and should typically follow) a course on Business Strategy, which focuses on developing and sustaining competitive advantage for a single business unit. Here, you will be able to better understand and learn the tools to analyze and manage decisions from a corporate-level perspective, which emphasizes the management of multiple businesses and multiple stakeholders. Examples of such decisions include vertical integration, diversification, mergers and acquisitions, strategic alliances, international expansion, global strategy, corporate governance and corporate social responsibility. You will: • Understand how corporations create and capture value as multi-business enterprises. • Learn tools and frameworks to assess choices regarding corporate scope, corporate transactions and global strategy. • Learn to analyze complex business situations and develop coherent corporate strategies. • Understand the role of corporate governance and stakeholder management in modern companies. This course is part of the iMBA offered by the University of Illinois, a flexible, fully-accredited online MBA at an incredibly competitive price. For more information, please see the Resource page in this course and onlinemba.illinois.edu.
University of Minnesota
Analysis for Business Systems
Most often, organizations acquire information systems as part of a larger focus on process improvement and efficiency. These organizations need to invest in the right system to meet their needs: right functionality, right size, and for the right price. The business systems analyst role in most organizations is responsible for translating the organization’s needs into requirements, which are then used to select or build the right system for the organization. During the Analysis for Business Systems course, you’ll learn about the standard model for systems development: the systems development lifecycle, or SDLC. You will learn how to read and even create the specific deliverables that business systems analysts prepare during the SDLC. These documents provide guidance to the organization as they determine whether to build or buy and configure a system that meets the organization’s needs.
Packt
Foundations of AI Engineering
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 a comprehensive foundation in AI engineering, starting with the fundamentals of Python programming and advancing through key data science and machine learning concepts. The course emphasizes hands-on projects that will solidify your understanding of these essential skills, providing a deep dive into Python, data science tools, and mathematics necessary for machine learning. By mastering these core concepts, you'll be equipped to approach AI engineering challenges confidently. The course is structured to guide you through each key area, beginning with Python programming basics. You will learn how to work with Python syntax, data structures, functions, and file handling, all necessary for real-world applications. As you progress, you'll explore data science essentials using NumPy and Pandas, working on projects that teach you data manipulation, visualization, and analysis. The course culminates with a deeper dive into the mathematics required for machine learning, including linear algebra, calculus, and probability. This course is perfect for aspiring AI engineers, data scientists, and those interested in pursuing machine learning. No prior experience is required, though a basic understanding of programming and mathematics will be helpful. The course is designed for beginners but includes complex mathematical concepts for those ready to delve deeper. By the end of the course, you will be able to write Python code for AI-related applications, clean and manipulate data using Pandas, visualize data with Matplotlib, apply machine learning math concepts, and execute probability and statistics techniques in data analysis and model-building projects.
EDUCBA
SSIS: Design, Implement & Automate ETL Workflows
By the end of this course, learners will be able to design, implement, and optimize SQL Server Integration Services (SSIS) packages for real-world data warehousing and ETL needs. They will apply core transformations such as conditional splits, lookups, merges, and aggregations; analyze datasets using pivoting, fuzzy logic, and text mining; and automate workflows with loops, file system tasks, and advanced SQL integration. This comprehensive program blends foundational concepts with advanced techniques, guiding learners step by step from basic package creation to complex ETL pipelines. Each module provides hands-on, scenario-based practice, ensuring learners not only understand the theory but also gain practical skills to solve data challenges in business environments. What makes this course unique is its structured progression: it starts with beginner-friendly lessons, gradually builds toward advanced data flows, and culminates in enterprise-grade automation. With carefully designed practice quizzes, graded assessments, and clearly defined learning objectives, this course equips learners to evaluate, integrate, and deploy SSIS solutions confidently in professional settings.