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This course addresses evaluating Large Language Models (LLMs), starting with foundational evaluation methods, exploring advanced techniques with Vertex AI's tools like Automatic Metrics and AutoSxS, and forecasting the evolution of generative AI evaluation. This course is ideal for AI Product Managers looking to optimize LLM applications, Data Scientists interested in advanced AI model evaluation techniques, AI Ethicists and Policy Makers focused on responsible AI deployment, and Academic Researchers studying the impact of generative AI across various domains. A basic understanding of artificial intelligence, machine learning concepts, and familiarity with natural language processing (NLP) is recommended. Prior experience with Google Cloud Vertex AI is beneficial but not required. It covers practical applications, integrating human judgment with automatic methods, and prepares learners for future trends in AI evaluation across various media, including text, images, and audio. This comprehensive approach ensures you are equipped to assess LLMs effectively, enhancing business strategies and innovation.

This course delves into the captivating world of data storytelling, illuminating how data, when woven into narratives, can paint compelling pictures that drive decision-making and inspire change. It's a journey from the fundamentals of data visualization to the artistry of narrative building, designed for those who seek to make their data speak to audiences in meaningful ways. Through this course, participants will explore the intersection of analytics and creativity, learning to convert complex datasets into clear, impactful stories. Whether the goal is to influence business strategies, share insights in academia, or engage the public, this course equips you with the skills to bring data to life through powerful storytelling.   This course is designed for data analysts, marketers, journalists, educators, and students in data-related fields. Whether you're exploring datasets for insights, crafting marketing narratives, or enhancing journalism with data-driven stories, this course enhances your storytelling through impactful data visualization. Participants should have a basic grasp of data analysis concepts and be familiar with tools like Microsoft Excel for creating visualizations. Enthusiasm for refining storytelling and presentation skills is encouraged. This course is ideal for those eager to enhance their ability to communicate data insights effectively. By the end of the course, participants will adeptly navigate the intersection of data visualization and storytelling, enhancing audience engagement and comprehension. Through advanced design principles and storytelling techniques, they'll synthesize compelling data visualizations that resonate with their intended audience. Cultivating a critical eye for data integrity, relevance, and biases, they'll ensure narrative objectivity. Finally, mastering diverse tools and software, they'll craft engaging, visually appealing data stories.

PhET Interactive Simulations (https://phet.colorado.edu/), a project of the University of Colorado Boulder, provides fun, free, interactive, research-based science and mathematics simulations for use across, primary, secondary, and higher education levels. We extensively test and evaluate each simulation to ensure educational effectiveness. All simulations are open source and free to all students and teachers. In this course, you will (a) acquire teaching and facilitation strategies for how to use PhET simulations in a K-2 classroom, (b) explore how PhET simulations can support multilingual students, and (c) engage with lessons supporting students' learning of counting, number sense, and place value. To finish this course, you need to complete one assignment with peer review: complete one early math teacher guide (using a template) to plan a lesson on numeracy using one of the three PhET simulations introduced in this course.

This course dives into the essentials of stakeholder engagement. It starts with fundamental concepts, introducing learners to the key frameworks and theories that underpin successful stakeholder relationships. Learn not just the "what" but the "why" behind these approaches, setting the stage for deeper understanding. This course is tailored for professionals across industries who seek to enhance their stakeholder engagement skills. Whether you're a project manager, communication specialist, or business analyst, this course will provide valuable insights applicable to your role. Both newcomers and experienced professionals looking to refine their skills will benefit from this comprehensive exploration of stakeholder engagement. While no specific prerequisites are required, a basic understanding of business principles and project management fundamentals will be beneficial. Additionally, familiarity with communication strategies and relationship-building techniques will enhance your learning experience. By the end of this course, learners will have a good understanding of stakeholder engagement and be equipped with the skills to apply best practices in your projects. This isn't just about learning concepts; it's about gaining the competence to make a real impact in professional projects.

Most clergy graduate from seminary or divinity school without coursework, training, or exposure to church administration, church management, or human resource management. Clergy commonly presumes most of their time will be spent preaching, presiding at liturgies, forming their congregation in the Christian faith, ensuring the pastoral care of their congregations, and doing good work for those in need in the community. Church leaders are often surprised to find out how much of their time is spent managing staff and volunteers and handling personnel issues. This third course in the Duke Divinity+ Church Administration series is designed to enable clergy and lay leaders serving the church in congregational settings or in other church-related venues to develop healthy frameworks and practices for human resource management. While as church leaders you rely first and foremost upon God to successfully perform your ministries, this work is accomplished through human resources—staff and volunteers. It is your responsibility, as one entrusted to minister in God's name, to steward those human resources in the best manner possible. In this course, you will connect human resource management to theological principles, and we will provide real-life examples and case studies of what to do and not do as a manager, all while learning the basics of human resource management in a church-related setting. If you are a pastor or ministry professional in need of Continuing Education Unit (CEU) credits for your professional development, CEU credits are available for this course. Please share your Coursera course certificate and a copy of the course description with your church denomination or employer as recognition of your earned 1.5 CEUs. This course qualifies for 1.5 Continuing Education Unit (CEU) credits. Completion of all three courses in the “Church Administration: Human Resources” Specialization qualifies for a total of 4.5 CEU credits.

This course gives you a thorough introduction to the python programming language. You'll start by learning the definition of python, its history, semantics, and how it works. The first week concludes by writing a python script to solve a problem. In Week 2, you'll learn about Serverless Architectures and understand the differences between microservices, service-oriented and monolith architectures. You'll build a microservices architecture by learning how to use the following tools: AWS Lambda, Amazon API Gateway, and Cloud9. It's then time to go deep with AWS Databases. You'll see Amazon RDS and Amazon DynamoDB in action, visualizing the important role they play in developing applications in python. The course's final week will cover application integration services. Amazon's Simple Notification Service (SQS), Simple Queue Service (SQS), and EventBridge take center stage as important “glue” components for scalable, cloud-based applications. You'll close the course with a hands-on project, writing some code to use some hardcoded data to write to dynamodb and then convert that into a lambda function.

This course, titled 'Real Estate Property Management: Manage Profitability and Efficiency', explores the field of real estate property management. We will examine the basic principles of property management and understand techniques and strategies to tackle profitability and operational issues in property management. We will cover some real-life case studies and examples highlighting potential challenges and learning from practical experience to tackle such challenges. The course also covers the use of essential tools & technology to improve efficiency and performance in property management. This course is designed for real estate operation professionals, investors, and small business owners involved in property management. Whether managing residential, commercial, or industrial properties, you will gain valuable insights and strategies to enhance your operations. It's ideal for those seeking to deepen their understanding of property management and explore innovative ways to optimize their investments and operations. Participants should have a basic understanding of property operations before enrolling. This fundamental knowledge is necessary to grasp the advanced concepts and strategies discussed. Familiarity with daily property management challenges will help learners apply the course content to real-world scenarios. Upon completion, learners will analyze property management principles, evaluate best practices, and devise effective strategies. They'll assess factors affecting profitability and propose innovative solutions for enhancement. Additionally, they'll explore modern technology's impact on property management to improve effectiveness.

This course explores back-end infrastructure management, secure API design, and robust data protection techniques. Participants will gain practical skills in server configuration, API security, and strategies for protecting data, which are critical for building resilient and scalable systems. The course combines theoretical foundations, hands-on applications, and real-world examples to ensure comprehensive understanding and skill acquisition. This course empowers professionals like Data Protection Officers, Back-End Developers, API Designers, System Administrators, and Information Security Professionals with essential knowledge and skills in data protection and API security. With prerequisites including basic data protection principles, back-end infrastructure familiarity, API design expertise, and information security experience, participants are well-equipped for effective engagement. Throughout the course, participants will accomplish various objectives, including evaluating data protection's role in securing back-end infrastructure and APIs and analyzing successful case studies. Using HOTS verbs like "evaluate" and "analyze," learners will enhance their critical thinking in data security. They'll also refine practical skills for secure server configuration, ensuring robust API integrations to safeguard sensitive information. Upon completing this course, professionals will be well-prepared to evaluate and implement security measures that enhance the integrity and confidentiality of data. With a focus on real-world case studies and practical application, participants will emerge equipped to navigate complex data protection challenges and implement secure API strategies confidently. This learning journey promises to empower individuals to bolster their organizations' security posture and mitigate potential risks effectively.

This comprehensive course is tailored specifically for data analysts, business professionals, and anyone looking to significantly enhance their proficiency in Microsoft Excel for complex data analysis tasks. By exploring a wide range of advanced Excel features and techniques, participants will gain the ability to analyze and interpret data to make informed business decisions. Learners will be introduced to real-world applications, using Excel to tackle diverse scenarios. The main case study will focus on Marketing, specifically the pricing of diamonds based on their characteristics and analyzing production quality data in manufacturing. By the end of the case study, learners will feel empowered to apply regression analysis to problems in their own field. This practical approach ensures that learners will leave the course equipped to handle complex data analysis challenges, providing them with the skills to conduct hypothesis testing, regression analysis, and more. The course is designed for Business Analysts and Data Analysts seeking to enhance their analytical skills and improve their decision-making capabilities. It is ideal for professionals who regularly handle data, generate reports, and provide insights to support business strategies. Participants are expected to have a keen interest in leveraging data to drive organizational success and be open to learning advanced analytical techniques and tools. Participants should be proficient in Microsoft Excel for data manipulation, analysis, and visualization. They should also understand fundamental statistical concepts, including measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation), and confidence intervals. This foundation will help them grasp more complex analytical methods and apply them effectively in their roles. By the end of this course, learners will have mastered the necessary tools to effectively visualize, analyze, and interpret data using Excel, making them invaluable assets in their professional fields. This course will empower participants to transform raw data into compelling insights, driving business strategies and achieving better outcomes.

Agentic AI is a type of AI that combines Generative AI with tools and actions, allowing us to build "agents" to assist us with everyday tools like Gmail, Office365, Google Sheets, Salesforce, etc. These agents can perform tasks on our behalf, such as updating a spreadsheet or sending an email. There are classes that teach these Agentic AI concepts, but they are geared towards programmers. If you aren't a programmer, they won't teach you how to build agents to help with your daily tasks. This class will teach you how to use ChatGPT and Zapier to build your own agents to help prioritize tasks, keep track of expenses, plan meals and shopping lists, etc. These won't be tools that require you to cut and paste the results back into an email; these will be tools that can directly create a draft email and put it into your email account or allow you to snap a picture of a receipt on the go and have it show up in a spreadsheet. These agents can automate and take action for you. By the end of this class, not only will you understand the state-of-the-art aspects of Agentic AI, but you will have built your own Travel Expense Agent. Your agent will be able to take a picture of a receipt or a description of an expense and catalog it into a Google Sheet. It will check your expense with another agent for completeness, ensure the expense isn't already in the spreadsheet, and even draft an email to your administrator with a simple reimbursement report. This course will transform the way you work.

The course "Applied Machine Learning: Techniques and Applications" focuses on the practical use of machine learning across various domains, particularly in computer vision, data feature analysis, and model evaluation. Learners will gain hands-on experience with key techniques, such as image processing and supervised learning methods while mastering essential skills in data pre-processing and model evaluation. This course stands out for its balance between fundamental concepts and real-world applications, giving learners the opportunity to work with widely-used datasets and tools like scikit-learn. Topics include image classification, object detection, feature extraction, and the selection of evaluation metrics for assessing model performance. By completing this course, learners will be equipped with the practical skills necessary to implement machine learning solutions, enabling them to apply these techniques to solve complex problems in data processing, computer vision, and more.

The course "Advanced Methods in Machine Learning Applications" delves into sophisticated machine learning techniques, offering an in-depth understanding of ensemble learning, regression analysis, unsupervised learning, and reinforcement learning. The course emphasizes practical application, teaching students how to apply advanced techniques to solve complex problems and optimize model performance. Learners will explore methods like bagging, boosting, and stacking, as well as advanced regression approaches and clustering algorithms. What sets this course apart is its focus on real-world challenges, providing hands-on experience with advanced machine learning tools and techniques. From exploring reinforcement learning for decision-making to applying a priori analysis for association rule mining, this course equips learners with the skills to handle increasingly complex datasets and tasks. By the end of the course, learners will be able to implement, optimize, and evaluate sophisticated machine learning models, making them well-prepared to address advanced challenges in both research and industry.