develop your ai skill set
Tackle real-world data challenges like labeling trade-offs, inter-annotator agreement, and label consistency through hands-on exercises and expert-led case studies. Develop the skills to assess and improve AI data quality, enhance model performance, and support high-stakes decision-making across AI initiatives.
Johns Hopkins University Certificate of CompletionCurriculum designed and delivered by JHU FacultyReal-world relevant content and use casesLIVE monthly seminars with JHU Faculty
The course is live. Click Register to start. Investment: $500
4 Weeks • Online • Hands-on Projects • $500
Earn a certificate of completion from JHU, a recognized leader in education and research. Earn 2 Continuing Education Units (CEUs) upon course completion.
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Johns Hopkins Engineering Lifelong Learning’s online and asynchronous AI Data Foundations course offers a distinctive blend of technical knowledge and critical thinking skills to help you master crucial aspects of AI data management. It focuses on real-world challenges like data labeling trade-offs, label consistency, and inter-annotator agreement. Through hands-on exercises, such as calculating Krippendorf’s Alpha and analyzing high-profile case studies, you will develop a deep understanding of how data quality directly impacts AI model performance.
This course empowers you to make informed, high-stakes decisions that ensure AI systems are efficient, accurate, and aligned with business objectives. You will have access to Johns Hopkins faculty and AI experts, gaining valuable knowledge and mentorship to enhance your learning experience. Position yourself for advanced roles in AI project management, data science, and AI strategy and lead AI initiatives across various industries.
AI Project ManagersData ScientistsAI/ML EngineersAI Product OwnersBusiness AnalystsDesigned by JHU Faculty, the curriculum covers key areas of gaining proficiency in Python, utilizing generative AI.
Quality Assessment
Resource Management
Data Labeling
Troubleshooting Expertise
Root-Cause Analysis
Engage in dynamic, interactive learning from anywhere in this online, asynchronous course.
Johns Hopkins University faculty are readily accessible to address your questions in the course, ensuring a personalized learning experience.
Content and use cases ensure it is current and relevant to today’s civil space exploration.
Hands-on activities that ensure you leave with actionable takeaways.
Dr. Ian McCulloh leads AI Continuing and Executive Education at Johns Hopkins University, where he specializes in data science, network analysis, and artificial intelligence. He holds a PhD in Computer Science from Carnegie Mellong University.McCulloh’s professional experience includes leading the U.S. Army’s network science initiative and establishing Accenture’s U.S. Federal AI practice, where he grew a team of over 1,200 professionals. His work spans advanced data analysis, machine learning, and Python programming, with applications in healthcare, defense, and business. He is a published author in peer-reviewed journals and has taught courses in data science and AI, making him uniquely qualified to lead a course in Python programming. McCulloh’s teaching emphasizes practical applications, bridging technical expertise with real-world problem-solving.
Lead with data-driven decisions
4 Weeks • Online • Hands-on Projects
4 Weeks • Online • Hands-on Projects
12 Weeks • Online • Hands-on Projects
4 Weeks • Online • Hands-on Projects • $500
Submit your details below to learn more about the course curriculum, benefits, fee and more.
Submit your details below to learn more about the course curriculum, benefits, fee and more.
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