ICME Summer Workshops 2023 | Fundamentals of Data Science
2023 Summer Workshops - Live Online July 24 - August 11
ICME’s annual Summer Workshop Series offers a variety of virtual data science and AI courses, taught live and online by faculty, researchers, and Stanford alumni working in the industry. The series is open to the general public worldwide. Discounts are offered to students, staff, and faculty from all schools as well as to ICME industry partners.
The series offers:
- Fourteen workshops that cover a spectrum of data science topics (see offerings below)
- Each workshop is spilt into two half-day modules
Every participant who attends a workshop and completed the evaluation at the end will receive a Certificate of Completion for that workshop. In addition, participants looking to gain a broad knowledge base may work towards the ICME Fundamentals of Data Science Certificate. The Fundamentals certificate requires the completion of at least four workshops, one of which needs to be SWS 05 (Privacy & Responsible AI) or SWS 06 (Applications of Data Science). We strongly recommend that aspirants also complete SWS 07 (Python for Data Science). Note that if anyone arriving with no previous Python experience should complete SWS 02 (Introduction to Python) before SWS 07. For more details please see our FAQ.
Below is a glance at our 2023 Summer Workshops:
01. Introduction to Statistics
The Introduction to Statistics workshop covers the fundamentals and key methodologies of statistics, which is also known as the science of learning from data and powers modern day machine learning, deep learning and data science. Learn More
02. Introduction to Python
The Introduction to Python workshop equips participants with the essential skills and knowledge to begin programming in Python, covering basic syntax, data structures, control flow, and introductory applications of Python programming. Learn More
03. Linear Algebra
The Linear Algebra workshop introduces participants to the fundamental concepts and techniques of linear algebra, including matrix operations, vector spaces, linear transformations, and applications in various fields such as data analysis and machine learning. Learn More
04. Big Data
The Big Data workshop provides participants with an overview of the principles, tools, and techniques used to process, analyze, and extract insights from large and complex datasets, exploring topics such as data storage, parallel computing, distributed systems, and scalable data processing frameworks. Learn More
05. Privacy and Responsible AI
The Privacy and Responsible AI workshop educates participants on ethical considerations, regulatory frameworks, and best practices for developing and deploying AI systems with a focus on protecting user privacy and ensuring responsible use of AI technologies. Learn More
06. Applications of Data Science
The Applications of Data Science workshop explores practical examples, techniques, and real-world case studies to demonstrate the diverse applications of data science in various domains, empowering participants to leverage data-driven insights for decision-making and problem-solving. Learn More
07. Python for Data Science
The Python for Data Science workshop teaches participants how to author powerful data science code using modern Python features and techniques such as abstract classes, generic programming and generators, which support programming patterns that can make data science code easier to write, debug and maintain.
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08. Introduction to Machine Learning
The Introduction to Machine Learning workshop provides participants with a foundational understanding of machine learning concepts, algorithms, and techniques, empowering them to build predictive models, uncover patterns in data, and apply machine learning to real-world problems.
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09. Introduction to Mathematical Optimization
The Introduction to Mathematical Optimization workshop equips participants with theoretical knowledge and practical skills to formulate and solve optimization problems, applying various algorithms and techniques to optimize decision-making, resource allocation, and machine-learning models across a broad range of diverse applications.
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10. Data Visualization
The Data Visualization workshop guides participants in effectively visualizing and presenting data using various visualization techniques and tools, enabling them to communicate insights and patterns in data to facilitate understanding and decision-making. Learn More
11. Introduction to Deep Learning
The Introduction to Deep Learning workshop provides participants with a comprehensive understanding of deep learning concepts and methodologies, equipping them with the knowledge to design, train, and deploy neural networks for tasks such as image recognition, natural language processing, and predictive modeling. Learn More
12. Introduction to Natural Language Processing
The Introduction to Natural Language Processing workshop equips participants with a foundational understanding of the building blocks of modern NLP concepts as well as practice to apply these techniques to real-world use cases such as machine translation and sentiment extraction. Learn More
13. Search and Recommendation
The Search and Recommendation workshop explores techniques and algorithms used in search engines and recommendation systems, enabling participants to understand and apply methods for effectively retrieving information, generating personalized recommendations, and enhancing user experiences. Learn More
14. Generative Models
The Generative Models workshop introduces participants to the principles and techniques of generative modeling, focusing on models that can generate new data instances, such as images, text, or audio, with applications in art, content generation, and data augmentation. Learn More