The Global Scholarly Directory.
Discover world-class academic programs curated for the modern intellectual. Search through 19877+ degrees and professional certificates.
University of Colorado Boulder
BiteSize Python: NumPy and Pandas
This course delves into advanced data structures in Python, focusing on the powerful capabilities of the NumPy and Pandas libraries. It introduces the ndarray, a multidimensional array object provided by NumPy, enabling efficient storage and manipulation of large datasets. Additionally, learners will explore the Series and DataFrame structures offered by Pandas, which facilitate data analysis and manipulation in a more user-friendly manner. Throughout the course, students will engage in practical exercises and case studies to reinforce their understanding of how these advanced data structures can be applied in real-world scenarios.
Universitat Autònoma de Barcelona
Sport Sponsorship. Let them Play
Are you interested in sport sponsorship? Would you like to understand which actors participate in sport sponsorship? Would you like to know the latest and innovative proposals that are arising in the sport sponsorship world? Are you interested in learning and connecting with sports enthusiasts/students from all around the world? The Universitat Autònoma de Barcelona and the Johan Cruyff Institute jointly offer this introductory course in sports sponsorship for all those interested in knowing how to create a sponsorship plan for a sports event. There are no special requirements to take the course. Thanks to this course you will be capable of facing a real challenge: the activation of a sponsorship plan for the Johan Cruyff Foundation.
Google Cloud
Online Data Migration to BigQuery using Striim
This is a self-paced lab that takes place in the Google Cloud console. Continuous Data Replication from Cloud SQL for MySQL to BigQuery using Striim
Coursera
Secure Mobile AI Models Against Attacks
AI models are no longer locked in the cloud—they live in your pocket, powering mobile apps for fitness, finance, healthcare, and beyond. But with this power comes new risk: adversarial attacks, model theft, privacy leaks, and silent failures that undermine user trust. Securing Mobile AI Models against Attacks (SMAI) is a hands-on course for mobile app developers, AI engineers, and cybersecurity professionals who want to safeguard AI models on Android and iOS. Through interactive coach dialogues, video lessons, and practical labs, you’ll learn how to embed security from day one, analyze threats like reverse engineering and adversarial inputs, and implement layered defenses using encryption, obfuscation, and OpenTelemetry monitoring. By the end, you will have the skills to design, secure, and continuously monitor mobile AI applications, ensuring resilience, compliance, and user confidence in real-world deployments. Participants should have a basic understanding of AI, machine learning, and mobile development, along with knowledge of security concepts like encryption and data protection. Familiarity with AI model deployment and monitoring tools like OpenTelemetry is also helpful.
Michigan State University
Journalism, the future, and you!
You will learn about the career paths that are available in journalism, and what opportunities the skill sets of a journalist can offer in other fields. You will explore areas such as being an international correspondent, self-publishing in journalism, as well as how to freelance in the field. You will be empowered to develop your own path in journalism, from being an active and informed consumer, to being a journalist. The worlds of business, communications, politics, education and marketing all utilize elements of journalism. This course also examines how to keep the trust of audiences through ethical, and responsible, journalistic practices. Sometimes, journalists need to be aware of their own safety. We will intelligently discuss how journalists around the world handle pressure, threats and other dangers while doing their jobs. Journalism - and journalists - are agents of change. Are you ready to become one too?
Pearson
Securing Generative AI
This course offers a comprehensive exploration into the crucial security measures necessary for the deployment and development of various AI implementations, including large language models (LLMs) and Retrieval-Augmented Generation (RAG). It addresses critical considerations and mitigations to reduce the overall risk in organizational AI system development processes. Experienced author and trainer Omar Santos emphasizes “secure by design” principles, focusing on security outcomes, radical transparency, and building organizational structures that prioritize security. You will be introduced to AI threats, LLM security, prompt injection, insecure output handling, and Red Team AI models. The course concludes by teaching you how to protect RAG implementations. You learn about orchestration libraries such as LangChain, LlamaIndex, and others, as well as securing vector databases, selecting embedding models, and more.
University of Leeds
Data Science: Data Storytelling and Presentation Skills
This course helps you build the core communication skills needed in data science. You’ll learn how to review and improve data visualisations, design clear and concise presentation slides, and present insights in a way that is accurate and engaging. You will explore key principles of scientific writing and practise discussing research findings with clarity and consistency. The course also introduces the skills and competencies expected of data science professionals, helping you understand how to communicate technical work to different audiences. By the end, you’ll be able to create stronger visuals, structure research reports effectively, and share your data stories with confidence.
Google Cloud
Machine Learning Operations (MLOps): Getting Started
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models. This course is primarily intended for the following participants: Data Scientists looking to quickly go from machine learning prototype to production to deliver business impact. Software Engineers looking to develop Machine Learning Engineering skills. ML Engineers who want to adopt Google Cloud for their ML production projects. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service
Starweaver
Manufacturing Systems Analysis: A Comprehensive Guide
Unlock the secrets to manufacturing efficiency with our Manufacturing Systems Analysis course. Designed for both beginners and intermediate learners, this course offers a blend of theory and practice, covering foundational concepts, practical applications, and real-world examples. Structured to guide participants from basics to advanced strategies, the course employs interactive video lectures, case studies, and application exercises. Gain essential skills in critical analysis, strategic tool application, communication, and problem-solving. Ideal for Manufacturing Managers, Engineers, Operations Managers, and Supply Chain Professionals, this course enhances proficiency in process optimization. While familiarity with manufacturing principles is recommended, prior knowledge of data analysis concepts is not mandatory. Join us for an immersive learning experience that unleashes the potential of Manufacturing Systems Analysis, propelling your career forward in the dynamic manufacturing industry.
Packt
Intermediate Data Analysis Techniques with Pandas
This Pandas course focuses on mastering DataFrame functionalities, starting with in-depth comparisons between Series and DataFrame methods. You'll learn essential skills such as selecting columns, adding data, and utilizing methods like value_counts and fillna for effective data cleaning. Advanced topics include filtering data, optimizing memory usage, handling missing values, and managing MultiIndex and text data. By exploring techniques for merging and concatenating DataFrames, you'll gain proficiency in handling complex data analysis tasks. This course is tailored for data analysts, scientists, and professionals seeking to enhance their Pandas skills for practical applications and real-world data challenges.
LearnQuest
Spring - Ecosystem and Core
In this course students will learn the why the Spring Framework is one of the dominant Java development Frameworks. the course covers a variety of techniques for Java Object Dependency Injection using various forms of configuration data i.e. XML, Annotations and Java Configuration Classes with Factory Methods. Configurations will be enhanced with Expression Languages and Conditional Beans that are available based off certain conditions like development environment i.e. test and production. Students will build an extensive application iteratively in a succession of hands on labs.
Google Cloud
Google Cloud Fundamentals: Core Infrastructure in italiano
Google Cloud Fundamentals: Core Infrastructure introduce concetti e terminologia importanti per lavorare con Google Cloud. Attraverso video e lab pratici, questo corso presenta e confronta molti dei servizi di computing e archiviazione di Google Cloud, insieme a importanti strumenti di gestione delle risorse e dei criteri.
The Chinese University of Hong Kong
离散优化建模高阶篇 Advanced Modeling for Discrete Optimization
优化问题是一种常见的决策问题，它在我们的社会中很常见。它的应用可以从数独问题的解决涵盖到婚礼的座次安排。同样的技术可以用于航班与机组成员的安排，钢铁生产的调节，和钢铁从矿区到港口的调度问题。在生产中，人力资源与生产材料的合理决策可以使企业获得成千上万的利润提升。类似的问题也存在于我们的日常生活中，它们包括决定包裹的运输路径，调整学校课程时间，和传输能源到千家万户。尽管这些问题很基础，不过以一般本科教育的知识来解决这些问题都会十分困难。 这个课程是设计给已完成离散优化建模基础篇的同学。你将学习到更多关于如何使用先进的高级建模语言表述清楚具有挑战性的离散优化问题，并让约束求解器完成其余工作。本课程将重点介绍模型调试与改良，如何把一个复杂的约束定义封装到一个谓词里面，及如何着手各种复杂的项目调度和打包问题。当你掌握这种先进的技术，你将能够解决以前难以想象的问题。
Google Cloud
Preparing for the Google Cloud Professional Cloud Architect Exam en Español
Del curso: "La mejor manera de prepararse para el examen es ser competente en las habilidades necesarias para el trabajo". Este curso usa un enfoque descendente para reconocer el conocimiento y las habilidades que ya se adquirieron, así como para resaltar la información y las habilidades necesarias para seguir preparándose. Puede usar este curso a fin de crear su propio plan de preparación personalizado. Lo ayudará a diferenciar lo que ya sabe de lo que no, y a desarrollar y a practicar las habilidades que se requieren de los profesionales que realizan este trabajo. Este curso tiene la misma organización que el esquema de la guía del examen: presenta los conceptos más difíciles (es decir, los conceptos clave) para que usted determine si se siente seguro del conocimiento que tiene acerca de esa área y los conceptos que dependen de ella, o bien si desea seguir estudiando. Además, aprenderá sobre las habilidades clave de trabajo y tendrá la oportunidad de ponerlas en práctica. Entre ellas, se incluyen las habilidades cognitivas, como el análisis de casos, la identificación de puntos de análisis técnicos y el desarrollo de las soluciones propuestas. Estas habilidades de trabajo también son habilidades necesarias para el examen. Por otra parte, pondrá a prueba sus capacidades básicas con los Labs de desafío de seguimiento de actividades, y tendrá muchas preguntas de muestra similares a las del examen, con las soluciones incluidas. Al final del curso, se incluye un cuestionario del examen de práctica sin calificación, seguido de un cuestionario del examen de práctica calificado que simula la experiencia de realizar el examen.
Johns Hopkins University
Managing Data Analysis
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
Google Cloud
Gemini in Google Drive - 繁體中文
Gemini for Google Workspace 是一項外掛程式，可讓使用者存取生成式 AI 功能。本課程使用影片、實作活動和練習範例，深入介紹 Google 雲端硬盤中的 Gemini 的功能。 課程結束後，您將具備 Google 雲端硬盤中的 Gemini 的知識及技能，可自信地運用這項工具提升工作流程的效率。