Information Session: Code In Place 2025
Skills:
AI Pair Programming50%
Get more information about Code in Place 2025 and submit your application: https://codeinplace.stanford.edu/
In today’s technology-driven world, coding isn’t just a skill—it’s a superpower. It unlocks your ability to understand and shape technology, fosters critical thinking and problem-solving, and fuels creativity. These skills are essential for innovation and success in any field.
Code in Place is a free introductory coding course from Stanford School of Engineering. Tens of thousands of learners have already transformed their lives through Code in Place. Join our vibrant community of learners!
Register for this online info session and hear directly from the Code in Place team about:
- The Code in Place Experience: Discover our unique, human-centered approach to learning to code and how learning new skills can help you succeed.
- The Application Process: Get tips and guidance on submitting your application to join the course.
More about Code in Place:
Learn content from Stanford's flagship course CS106A, online for free, with the support from thousands of teachers.
Who? Learners, with no programming experience, and time to dedicate to learning.
Where? Anywhere with internet.
What? Learn the first half of Stanford's intro to Python course, CS106A.
When? Class starts 21st April 2025. Student applications due 9th April 2025.
Certification? Yes. Build a sharable portfolio of your code, hosted by Stanford.
How much work? At least 7 hours each week for 6 weeks. Set your own schedule.
CS106A is one of the most popular courses at Stanford University, taken by almost 1,600 students every year. It has been developed over the last 30 years by an amazing team, including Nick Parlante, Eric Roberts and more. The course teaches the fundamentals of computer programming using the widely-used Python programming language. This course is for everyone from humanists, social scientists, to hardcore engineers.
What makes Code in Place special? We recruit and train one vo
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Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate
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Statistical Learning: 13.1 Introduction to Hypothesis Testing II
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Statistical Learning: 12.R.3 Hierarchical Clustering
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Statistical Learning: 12.R.1 Principal Components
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Statistical Learning: 13.R.1 Bonferroni and Holm II
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Statistical Learning: 12.6 Breast Cancer Example
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Statistical Learning: 12.5 Matrix Completion
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Statistical Learning: 12.4 Hierarchical Clustering
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Statistical Learning: 12.3 k means Clustering
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Statistical Learning: 13.1 Introduction to Hypothesis Testing
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Stanford Seminar - Introduction to Web3
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Stanford CS330: Deep Multi-Task & Meta Learning I 2021 I Lecture 1
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Stanford Seminar - Perceiving, Understanding, and Interacting through Touch
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 3
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Stanford CS330: Deep Multi-Task & Meta Learning I 2021 I Lecture 4
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 5
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Stanford Seminar - Evolution of a Web3 Company
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 6
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 7
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 8
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Stanford Seminar - Designing Human-Centered AI Systems for Human-AI Collaboration
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 9
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Women Rise: Sheri Sheppard
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 10
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 11
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 12
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 13
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 14
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Stanford Webinar - Cloud Computing: What’s on the Horizon with Dr. Timothy Chou
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 15
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Stanford Seminar - Multi-Sensory Neural Objects: Modeling, Inference, and Applications in Robotics
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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 16
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Stanford Seminar - Toward Better Human-AI Group Decisions
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Stanford CS330: Deep Multi-Task & Meta Learning I 2021 I Lecture 17
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Stanford CS330: Deep Multi-Task & Meta Learning I 2021 I Lecture 18
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Stanford Webinar - Web3 Considered: Possible Futures for Decentralization and Digital Ownership
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Stanford Seminar - ML Explainability Part 2 I Inherently Interpretable Models
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Stanford Seminar - ML Explainability Part 3 I Post hoc Explanation Methods
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