The Global Scholarly Directory.
Discover world-class academic programs curated for the modern intellectual. Search through 19877+ degrees and professional certificates.
Google Cloud
Implementa y escala modelos de IA con Cloud Run
La inferencia de IA es el proceso de utilizar un modelo de aprendizaje automático entrenado para realizar predicciones de datos nuevos y no vistos con la aplicación de patrones aprendidos. Este curso está diseñado para desarrolladores, ingenieros de AA y científicos de datos que deseen implementar rápidamente servicios de inferencia de IA en Cloud Run. Es útil para aquellos familiarizados con soluciones de implementación de aplicaciones sin servidores basadas en la nube, pero que quizás no tengan experiencia en la ejecución de inferencia de IA utilizando productos sin servidores de Google Cloud. Se incluyen ejemplos en los que se implementa un modelo para la inferencia de IA con GPU y se integran apps de IA generativa en servicios de almacenamiento de datos.
Duke University
Pandas for Data Science
How can you effectively use Python to clean, sort, and store data? What are the benefits of using the Pandas library for data science? What best practices can data scientists leverage to better work with multiple types of datasets? In the third course of Data Science Python Foundations Specialization from Duke University, Python users will learn about how Pandas — a common library in Python used for data science — can ease their workflow. We recommend you should take this course after the first two courses of the specialization. However, if you hold a prerequisite knowledge of basic algebra, Python programming, and NumPy, you should be able to complete the material in this course. In the first week, we’ll discuss Python file concepts, including the programming syntax that allows you to read and write to a file. Then in the following weeks, we’ll transition into discussing Pandas more specifically and the pros and cons of using this library for specific data projects. By the end of this course, you should be able to know when to use Pandas, how to load and clean data in Pandas, and how to use Pandas for data manipulation. This will prepare you to take the next step in your data scientist journey using Python; creating larger software programs.
Packt
Creating an Unresourced Project with Oracle Primavera P6 PPM
Updated in May 2025. This course now 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. Embark on a transformative learning journey with our course designed to equip you with the skills to create and manage unresourced projects using Oracle Primavera P6 PPM. The course begins with a foundational introduction, outlining the purpose of planning and essential planning metrics. You will gain a deep understanding of various planning levels, from basic scheduling to advanced resource management, ensuring you can handle projects of varying complexities. As you progress, the course delves into the practical aspects of Primavera P6 PPM, guiding you through project setup, calendar definitions, and the creation of a comprehensive Work Breakdown Structure (WBS). Interactive workshops reinforce your learning, allowing you to apply concepts in real-world scenarios. Detailed modules on formatting displays and adding relationships ensure you can customize your project views and manage dependencies effectively. Concluding with advanced topics such as activity network views and constraint management, this course ensures you are well-versed in all facets of project planning without resources. By the end of the course, you will possess the expertise to navigate Primavera P6 PPM confidently, enhancing your ability to deliver successful projects on time and within scope. This course is designed for technical professionals, project managers, and aspiring project planners who have a foundational understanding of project management principles. Prior experience with project scheduling software is beneficial but not mandatory.
Coursera
Explore and Visualize Data the Python Way
In this course, you'll learn to uncover and communicate insights through powerful data visualization techniques. You'll master both static and interactive visualization tools, from Matplotlib and Seaborn to Plotly, while conducting thorough exploratory data analysis. Using the EngageMetrics and MediTrack datasets, you'll develop the skills to transform complex data into compelling visual stories that drive decision-making. Upon completion, you'll be able to: • Conduct EDA to summarize datasets by using descriptive statistics, identify patterns, and detect anomalies. • Create static visualizations such as line plots, bar charts, boxplots, histograms, and scatter plots. • Build interactive plots that allow user engagement. • Integrate EDA and visualization techniques in a two‑part challenge that requires both static and interactive outputs.
École Polytechnique Fédérale de Lausanne
Big Data Analysis with Scala and Spark (Scala 2 version)
Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout. We'll cover Spark's programming model in detail, being careful to understand how and when it differs from familiar programming models, like shared-memory parallel collections or sequential Scala collections. Through hands-on examples in Spark and Scala, we'll learn when important issues related to distribution like latency and network communication should be considered and how they can be addressed effectively for improved performance. Learning Outcomes. By the end of this course you will be able to: - read data from persistent storage and load it into Apache Spark, - manipulate data with Spark and Scala, - express algorithms for data analysis in a functional style, - recognize how to avoid shuffles and recomputation in Spark, Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Parallel Programming: https://www.coursera.org/learn/parprog1. Note that this version of the course uses Scala 2.13. You can find a more recent version of the course that uses Scala 3 here: https://www.coursera.org/learn/scala-spark-big-data
Coursera
GenAI for AWS: AI-Driven Cloud Automation & Scaling
In this course, you’ll dive into the powerful intersection of Generative AI and cloud automation. Whether you're looking to automate cloud workflows, reduce operational costs, or deploy scalable AI-driven solutions, this course gives you the hands-on skills to do just that—using Amazon Bedrock, AWS Lambda, Step Functions, and Amazon Q. Through real-world examples, guided labs, and a capstone project, you'll learn to design, deploy, and optimize AI-powered cloud architectures that are secure, efficient, and future-ready. This course is designed for cloud professionals and developers who want to bring generative AI into their AWS workflows. Whether you're a Cloud or DevOps Engineer looking to automate tasks with AI, an AI/ML Practitioner eager to deploy large language models via Amazon Bedrock, a Solutions Architect building scalable cloud infrastructures, or a Developer curious about enhancing applications with generative capabilities. To make the most of this course, you should already be familiar with the AWS Console and comfortable with basic cloud tasks like setting up users using IAM. Experience with core AWS services such as S3 and Lambda will be helpful, along with a basic understanding of AI/ML concepts like inference and model deployment. You should also be able to read and write simple JSON and interact with APIs. Lastly, knowledge of Python or another scripting language will help you complete hands-on activities and workflows more smoothly. By the end of this course, you'll have the confidence and technical know-how to design, deploy, and scale generative AI workflows using AWS services—unlocking new levels of automation, efficiency, and innovation. Whether you're solving real business challenges or exploring new AI capabilities, this course will give you the tools to make a real impact in your cloud projects. So dive in, experiment boldly, and let’s bring your GenAI-powered ideas to life!
LearnQuest
Introduction to Visual Basic Programming
This course is the second course in a series that aims to prepare you for a role working as a programmer. In this course, you will be introduced to the five main concepts in procedural programming: user input, console output, variable declaration and assignment, decision branching and iteration. Labs will allow the students to apply the material in the lectures in simple computer programs designed to re-enforce the material in the lesson. Learners will need to have a local machine with any one of the following operating systems; Windows 7 SP1 or higher, macOS 10.1.13 or higher, or almost any version of Linux from the last several years. The learner will either need to download the free community edition of Visual Studio or the open source .NET Core installation. We will walk you through the process of getting your local environment set up as part of the course.
Google Cloud
Encoder-Decoder Architecture - Français
Ce cours offre un aperçu de l'architecture encodeur/décodeur, une architecture de machine learning performante souvent utilisée pour les tâches "seq2seq", telles que la traduction automatique, la synthèse de texte et les questions-réponses. Vous découvrirez quels sont les principaux composants de l'architecture encodeur/décodeur, et comment entraîner et exécuter ces modèles. Dans le tutoriel d'atelier correspondant, vous utiliserez TensorFlow pour coder une implémentation simple de cette architecture afin de générer un poème en partant de zéro.
Coursera
Visualize Workforce Metrics
Your dashboard shows time-to-hire is down—but is that good or bad? This course teaches you to explain what three core workforce metrics actually measure—time-to-hire, absenteeism rate, and internal mobility rate—and why reading them together matters more than any single number. You'll populate a real dashboard template from HRIS data and learn how to spot when a green indicator hides a serious problem. You'll also use AI tools like ChatGPT to draft stakeholder summaries of your findings—then verify the AI's output against your actual data. Through realistic role plays where you walk a CEO through live dashboard results and coach dialogues where you handle skeptical managers, you'll build the visual literacy every HR professional needs. Designed for HR professionals new to people analytics. Basic spreadsheet familiarity helpful. Access to free ChatGPT or Claude recommended.
Packt
Foundations of 3D Modeling in Blender
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. Embark on a comprehensive journey into 3D modeling with Blender, where you'll master the essential techniques, from viewport navigation to advanced modeling skills. By exploring the Blender interface, using references, and applying modifiers, you’ll gain the confidence to build intricate models and design realistic architectural elements. Learn how to create complex structures like towers, staircases, and windows, enhancing your workflow efficiency while keeping your models clean and precise. Throughout the course, you’ll also gain a solid understanding of asset management, texture mapping, and scene composition, ensuring that you can streamline your projects and bring your designs to life with ease. As you progress, you'll delve deeper into advanced techniques, utilizing geometry nodes, shaders, and modifiers to create realistic details such as roofs, arches, and water features. You'll also refine your skills in window modeling, UV unwrapping, and advanced texturing, adding visual richness to your 3D models. This course is perfect for aspiring 3D artists, game designers, or anyone looking to break into the world of 3D modeling. A basic understanding of Blender is helpful but not required as we will start with the fundamentals and build upon them. By the end of the course, you will be able to navigate Blender's interface, create complex 3D structures, apply textures and shaders, and master advanced modeling techniques, ensuring a professional-quality portfolio of work.
Packt
AI Product Management: The Complete Handbook
Artificial Intelligence is transforming every industry, and the ability to manage AI-driven products is now one of the most in-demand skills in the tech landscape. This course provides a complete roadmap for mastering the art and science of AI product management—bridging the gap between data science, engineering, and business strategy. Throughout the course, you’ll learn how to build, launch, and scale AI-powered products effectively. You’ll explore model development, infrastructure, maintenance, and commercialization, gaining the insights needed to turn AI innovation into real business value. Unlike typical product management programs, this course uniquely combines technical foundations with hands-on, real-world product case studies. You’ll see how design, growth hacking, benchmarking, and ethical AI come together to define the future of product leadership. This course is ideal for aspiring and practicing product managers, entrepreneurs, and tech professionals who want to lead AI-native products. A basic understanding of product management and AI concepts will be helpful but not mandatory.
Coursera
Empower Patients: Autonomy Through Simulation and Role-Play
Ready to transition from theory to practice in patient-centered care? This intermediate-level course is designed for clinical educators, healthcare professionals, and curriculum developers who want to master the art of simulation-based training. You will learn to build and facilitate powerful learning experiences that equip clinicians with the skills to navigate complex ethical decisions and uphold patient autonomy. Through a hands-on, structured approach, you will design a branching digital case study that challenges learners to make decisions with real consequences in a safe, simulated environment. You will also learn to structure and lead interactive role-playing scenarios that build confidence and competence in difficult patient conversations. The course culminates in mastering a structured feedback model, ensuring that every simulation becomes a lasting learning opportunity. By the end, you will not just understand the principles of patient autonomy—you will have a complete "Simulation Starter Kit" to create and lead impactful training that empowers clinicians and transforms patient outcomes.
IndiaAI
YUVA AI for ALL
IndiaAI Mission, implemented by the Ministry of Electronics and Information Technology (MeitY), aims to strengthen India’s global leadership in AI, foster technological self-reliance, and ensure ethical, responsible, and inclusive AI adoption. YUVA AI for ALL Course from IndiaAI is a key step in that journey — designed to make AI literacy accessible to everyone, regardless of background or experience. Human Capital which touches upon aspects of advancing equitable skilling and inclusive workforce transitions is one of the keys thematics for the India - AI Impact Summit in Feb 2026. The course has been created by renowned AI experts and technologists Jaspreet Bindra and Anuj Magazine from AI & Beyond, with an aim brings global AI concepts to every Indian learner — simplified, localized and hands-on. The YUVA AI for ALL course comprises of six modules covering the foundations, applications, ethics, and future of Artificial Intelligence. Learners explore how AI can be used to learn, create, think, and plan responsibly, preparing them to engage confidently with AI in everyday life. Through real-world Indian examples, you’ll learn the art of effective prompting and gain the confidence to apply AI. Discover how AI can help you write smarter, learn faster, and work better, using the same Generative AI tools shaping industries and innovation worldwide.
Pearson
Kubernetes From Basics to Guru: Unit 4
This course is designed for IT professionals and developers eager to enhance their skills in deploying and managing cloud-native applications. Through comprehensive lessons, you will master the art of configuring security settings, utilizing custom resource definitions, and implementing effective network policies to ensure a secure and efficient Kubernetes environment. By the end of this course, you'll be equipped with the knowledge to confidently manage and scale your applications using Kubernetes controllers and operators.
University of Pennsylvania
Statistics for Data Science Essentials
Review the basics of discrete math and probability before enhancing your probability skills and learning how to interpret data with tools such as the central limit theorem, confidence intervals and more. Complete short weekly mathematical assignments.
Meta
Principles of UX/UI Design
Learn the fundamentals of User Experience (UX) research and design. Become immersed in the UX process of identifying problems and iterating and testing designs to find appropriate solutions. You’ll learn how to empathize with users through best-practice research approaches, and create common UX artifacts such as user personas. And you’ll use UX insights to make appropriate design decisions. By utilizing the User Interface (UI) design process, you will be able to create wireframes for features in Figma, and turn them into Minimum Viable Product (MVP) style prototypes from your mockups. You’ll also learn the core principles of design such as hierarchy and the use of grids. By the end of this course you will be able to: • Design digital products that create value and solve a user's problems in meaningful ways. • Recognize what constitutes successful UX UI and evaluate existing interfaces for design consistency and quality. • Identify best practice interaction design methods and evaluate and improve your own and others' designs. • Create, share and test your UX UI designs following best practice guidelines. This is a beginner course for learners who would like to prepare themselves for a career in UX UI development. To succeed in this course, you do not need prior development experience, only basic internet navigation skills and an eagerness to learn.