The Global Scholarly Directory.
Discover world-class academic programs curated for the modern intellectual. Search through 19877+ degrees and professional certificates.
Coursera
Exception Handling in Python
In this 1-hour long project-based course, you will learn the differences between an exception and syntax errors, how to raise an exception, what an AssertionError exception is within Python, how to use the try, except and else clause and how to use the finally clause and any clean-up actions. By the end of this project, you will have an understanding of error and exception handling in python. Once you have a beginner's knowledge of python programming and start coding you may find that something disrupts the normal flow of the program you have built. You may find yourself with errors in your code. This course will provide students with the knowledge behind exception handling in python and show how to write high-quality code to ensure that if your python scripts encounter a situation that it cannot cope with that the right procedures are in place to provide meaningful information and deal with those errors. Thus, ensuring that your code is efficient and robust which is an essential aspect of writing high-quality code. This project will take students through a number of examples demonstrating several of the most useful python exceptions. You will gain an understanding of exception handling in python from the in-depth examples provided.
Microsoft
Project Development in Python
Description: This course focuses on managing and executing Python projects effectively in a collaborative environment. You'll learn project management principles, DevOps practices, Agile methodologies, client communication, and career-building strategies. Benefits: Gain a comprehensive understanding of project management, DevOps, and Agile methodologies, preparing you to lead and contribute effectively to real-world Python projects. By the end of this course, you'll be able to: • Apply project management principles to Python development. • Utilize DevOps tools like Docker and Kubernetes. • Implement Agile principles for iterative development. • Understand and interpret client requirements. • Build and train basic chatbots using Python. • Create a compelling portfolio and resume. • Network effectively and prepare for job interviews. Tools/Software: Docker, Kubernetes, Prometheus, Grafana, Jira, ChatterBot, spaCy This course is for entry-Level professionals looking to build a foundational understanding and experience with Python, while seeking employment as a Python developer. No prior work experience or degree is required.
Google Cloud
Managing a GKE Multi-tenant Cluster with Namespaces
This is a self-paced lab that takes place in the Google Cloud console. This lab explores best practices in managing and monitoring a multi-tenant cluster in order to optimize your costs.
Coursera
Align AI: Ethics, Strategy & Excellence
Did you know that over 60% of organizations adopting AI struggle not with technology, but with aligning ethical practices and strategic goals across teams? Responsible AI success depends on more than just model performance—it depends on governance, purpose, and collaboration. This Short Course was created to help ML and AI professionals operationalize generative AI systems responsibly while ensuring ethical compliance, strategic alignment, and organizational excellence in enterprise environments. By completing this course, you will be able to bridge the gap between AI innovation and enterprise strategy by embedding ethical standards, defining governance structures, and designing a scalable AI center of excellence—skills you can apply immediately to guide responsible and effective AI adoption. By the end of this course, you will be able to: • Analyze the ethical implications of model decisions and recommend mitigation strategies. • Evaluate the alignment of an AI roadmap with organizational strategic objectives. • Create a charter for an AI center of excellence to standardize best practices. This course is unique because it integrates AI ethics, strategic management, and organizational design—empowering you to lead AI initiatives that are not only technologically sound but also socially responsible and strategically aligned. To be successful in this project, you should have: • Basic ML/AI concepts • Understanding of organizational strategy • Familiarity with governance frameworks • Experience in cross-functional collaboration
Google
Daten über Visualisierungen teilen
Dies ist der sechste Kurs im Google Data Analytics Certificate. In diesen Kursen lernen Sie alles, was Sie für eine Einstiegsposition in der Datenanalyse benötigen. Sie lernen, wie Sie Ihre Datenergebnisse visualisieren und präsentieren, während Sie den Datenanalyseprozess abschließen. In diesem Kurs erfahren Sie, wie Datenvisualisierungen, wie z. B. visuelle Dashboards, dazu beitragen können, Ihre Daten zum Leben zu erwecken. Außerdem lernen Sie Tableau kennen, eine Datenvisualisierungsplattform, mit der Sie effektive Visualisierungen für Ihre Präsentationen erstellen können. Bei Google tätige Fachleute für die Datenanalyse werden Sie weiterhin anleiten und Ihnen praktische Möglichkeiten zeigen, wie Sie häufige Datenanalyseaufgaben mithilfe der besten Tools und Ressourcen erledigen können. Nach Abschluss dieses Zertifikatsprogramms sind Lernende bestens gerüstet, um sich auf Einstiegspositionen in der Datenanalyse zu bewerben. Es sind keine Vorkenntnisse erforderlich. Im Verlauf dieses Kurses werden Sie: - die Bedeutung der Datenvisualisierung untersuchen; - erfahren, wie Sie mithilfe von Data Storys eine überzeugende Erzählung entwickeln - sich mit der Verwendung von Tableau zum Erstellen von Dashboards und Dashboard-Filtern vertraut machen - erfahren, wie Sie mit Tableau effektive Visualisierungen erstellen - die Prinzipien und Praktiken für effektive Präsentationen kennenlernen - erfahren, wie Sie potenzielle Einschränkungen im Zusammenhang mit den Daten in Ihren Präsentationen berücksichtigen können - Best Practices für das Beantworten von Fragen aus dem Publikum kennenlernen
DeepLearning.AI
시퀀스 모델
딥 러닝 전문화의 다섯 번째 과정에서는 시퀀스 모델과 음성 인식, 음악 합성, 챗봇, 기계 번역, 자연어 처리(NLP) 등과 같은 흥미로운 애플리케이션에 익숙해질 것입니다. 이 과정을 이수하면 순환 신경망(RNN)과 GRU 및 LSTM과 같이 일반적으로 사용되는 변형을 구축 및 훈련하고, RNN을 문자 수준의 언어 모델링에 적용하며, 자연어 처리 및 단어 임베딩에 대한 경험을 얻을 수 있으며, HuggingFace 토크나이저 및 트랜스포머 모델을 사용하여 NER 및 질문에 답하기 같은 다양한 NLP 작업을 해결합니다. 딥 러닝 전문화 과정은 딥 러닝의 기능, 과제 및 결과를 이해하고 최첨단 AI 기술의 개발에 참여할 준비를 하는 데 도움이 되는 기본 프로그램입니다. 경력을 쌓기 위한 지식과 기술을 습득할 수 있도록 도와줌으로써 AI 세계에서 최종적인 단계를 맡을 수 있는 길을 제공합니다.
PepsiCo
The Water Cycle
In order to access this course without a fee, please follow the below steps: 1. Click the blue ‘Enroll’ button 2. At the bottom of the pop-up window, click the ‘Audit the course’ option 3. For more information on auditing a course, please see details in this Learner Help Center article: https://www.coursera.support/s/article/209818613-Enrollment-options?language=en_US In this course, spread over ten modules, participants will learn about: • the water cycle • hydrology • groundwater models • human impacts on freshwater ecosystems • water governance • water law • the economics of water infrastructure • scenario planning and municipal water
Packt
AI Enhancement with Knowledge Graphs - Mastering RAG Systems
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. Unleash the potential of AI systems by mastering Retrieval-Augmented Generation (RAG) techniques with Knowledge Graphs in this comprehensive course. You'll learn how to design, build, and query advanced Knowledge Graphs while integrating them with AI systems to boost contextual understanding and improve retrieval efficiency. The course begins with a solid introduction to Knowledge Graphs, including their structure, construction, and applications. You'll set up your development environment, dive into practical Neo4j implementations, and programmatically generate Knowledge Graphs. Through guided exercises, you'll extract real-world data, transform it into graph structures, and visually explore their interconnections. Moving further, you'll explore the synergy between Knowledge Graphs and RAG systems, creating vector indexes, embeddings, and integrating them into databases. Learn advanced querying methods, visualizations, and workflows for AI-powered use cases. By the end, you'll build a RAG-powered Knowledge Graph project, combining Neo4j and LangChain, to showcase the full flow of data transformation, retrieval, and application. This course is perfect for AI enthusiasts, data engineers, and developers eager to enhance their AI models with Knowledge Graphs. Prior experience with Python and basic AI concepts is recommended. Whether you’re at an intermediate or advanced level, you'll gain valuable, industry-relevant skills.
Google Cloud
Présentation de l'IA et du machine learning sur Google Cloud
Ce cours présente les fonctionnalités d'IA et de machine learning (ML) de Google Cloud, en mettant l'accent sur le développement de projets d'IA prédictive et générative. Il explore les différentes technologies, produits et outils disponibles tout au long du cycle de vie des données à l'IA, et permet aux data scientists, aux développeurs d'IA et aux ingénieurs en ML d'améliorer leur expertise grâce à des exercices interactifs.
Coursera
Assimilating into Your New Job
In this 40 minute project-based course, you will learn how to be able to 1. Create a thank you note for the Hiring Manager/Recruiter and Contacts 2. Prepare a strategic plan for success BEFORE starting your new job 3. Develop a 30-60-90 Day Plan for your new job Assimilating into a new job is one of the most important steps of completing your job search. It requires a understanding of the company culture; build strong team member relationships and maintaining good communications with your hiring manager by setting expectations in your new role. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Packt
Foundations of Solidity and Smart Contract Development
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. This course serves as a comprehensive introduction to Solidity and smart contract development on Ethereum. Starting with the basics, you’ll become familiar with the Remix Ethereum IDE, a critical tool for writing and testing smart contracts. Through step-by-step guidance, you’ll explore core concepts such as syntax, comments, and pragma Solidity. The course ensures you are comfortable with every aspect of the IDE, making your development journey smooth and engaging. Moving deeper into Solidity, you’ll learn how to write and deploy your first smart contract. Topics such as variables, data types, and functions are covered thoroughly, ensuring you grasp the foundational elements of smart contract coding. Exercises and solutions help reinforce your learning, making each section highly interactive. The course covers critical programming structures like if-else statements, loops, and operators, equipping you with the decision-making tools essential for smart contract logic. Finally, you will dive into more advanced topics such as scope, visibility, and memory management in Solidity. As you progress, you’ll tackle more complex coding tasks like arithmetic, logical operators, and string handling, preparing you to develop fully functional and efficient smart contracts. By the end, you’ll have not only theoretical knowledge but also practical coding skills to confidently create Ethereum-based applications. This course is ideal for beginners in blockchain development, software engineers interested in decentralized applications, or anyone with basic programming knowledge. No prior experience with Solidity is necessary, but familiarity with basic programming concepts is recommended.
University of California San Diego
Big Data - Capstone Project
Welcome to the Capstone Project for Big Data! In this culminating project, you will build a big data ecosystem using tools and methods form the earlier courses in this specialization. You will analyze a data set simulating big data generated from a large number of users who are playing our imaginary game "Catch the Pink Flamingo". During the five week Capstone Project, you will walk through the typical big data science steps for acquiring, exploring, preparing, analyzing, and reporting. In the first two weeks, we will introduce you to the data set and guide you through some exploratory analysis using tools such as Splunk and Open Office. Then we will move into more challenging big data problems requiring the more advanced tools you have learned including KNIME, Spark's MLLib and Gephi. Finally, during the fifth and final week, we will show you how to bring it all together to create engaging and compelling reports and slide presentations. As a result of our collaboration with Splunk, a software company focus on analyzing machine-generated big data, learners with the top projects will be eligible to present to Splunk and meet Splunk recruiters and engineering leadership.
Coursera
Evaluate Employee Comprehension with Canvas
In this 2-hour long project-based course, you will learn how to customize an interactive course syllabus within a Canvas course, develop an assignment in a Canvas course, and design a rubric in a Canvas course. By the end of this project, you will be able to use the Canvas LMS to evaluate employee comprehension through assessments. Through the Canvas LMS you will be able to support and develop online learning in a variety of ways. By using various assessment tools in the Canvas LMS you are able to effectively evaluate your employees’ comprehension of important trainings and company related content. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
University of Colorado Boulder
Advanced Strategies for Sustainable Business
This course focuses on integrating sustainability into core business strategies, measuring and reporting sustainability performance, and developing human capital for sustainability. Learners will explore sustainable branding, policy advocacy, community partnerships, and compliance reporting. The course also covers innovation in sustainability, ethical perspectives, and continuous improvement processes. By the end, learners will be equipped with the knowledge to implement and lead effective sustainability initiatives within their organizations.
Edureka
Generative AI for Customer Service Automation
This course explores how AI transforms customer service operations, empowering professionals to design, automate, and manage intelligent, ethical, and efficient support systems. You’ll learn to combine strategic AI planning, no-code automation, and responsible AI governance to deliver personalized and proactive customer experiences at scale. You’ll begin by understanding how AI aligns with business goals through strategic planning, customer journey mapping, and ROI measurement using tools like Miro AI and Notion AI. Next, you’ll move into no-code AI automation, learning to build and manage workflows for onboarding, feedback, and complaint resolution with Zapier and Flowise. Finally, you’ll explore ethical and privacy-first AI practices, using Claude and Replit to ensure transparency, fairness, and compliance in AI-driven interactions. By the end of this course, you will be able to: - Explain how AI supports strategic customer service planning and relationship management. - Build automated workflows using no-code AI tools to enhance customer engagement and efficiency. - Apply ethical AI principles to ensure fairness, transparency, and data privacy in automation. - Design a comprehensive AI-driven customer service transformation strategy. This course is designed for customer service managers, automation specialists, and business leaders seeking to integrate AI into customer operations. A basic understanding of service workflows or digital tools will help you get the most from the course. Join us to master the art of AI-powered customer service automation—where intelligent systems, human insight, and ethical design come together to create exceptional customer experiences.
Amazon Web Services
Cloud Support Essentials: A Technical Approach
This course will focus on building the skills that you need to get started with the Command Line Interface (CLI). It will cover basic navigation, program installation, reading logs, and troubleshooting issues. We will also dive into network troubleshooting and common AWS scenarios that you may encounter as a Cloud Support Associate. In the first section of this course, we will focus on how to navigate in a Linux environment using only the CLI. We will explain how to create files, directories, and the commands you need to manage your system. We will also discover how to set permissions in the environment as well as edit text and configuration files. In the second section, we’ll go a bit deeper into the CLI and learn about troubleshooting tools that we have available to us. You’ll see how to test networking connectivity as well as install and configure Linux applications. The third portion of this course will focus on network troubleshooting in AWS. We’ll discover the common ports that can cause problems when setting up security groups and network access control lists (NACLs). We’ll also learn how AWS handles DNS and the steps you can take if something isn’t resolving correctly. Finally, the course concludes by covering common troubleshooting scenarios. What do you do when your Linux application keeps crashing? How about a troublesome SSH connection that isn’t working? Where do you look if your hosts are missing internet connectivity? By the end of this course, you’ll have a strong technical foundation in the skills needed to troubleshoot IT problems as a Cloud Support Associate.