21 - 30 of 366 results for: CS

The use of data to drive decisions and discoveries has increased dramatically over the past two decades, thanks to prevalent data collection, cheaper storage, faster computers, and sophisticated algorithms. This introductory seminar has three components: (1) Hands-on instruction in tools and techniques for working with data, from spreadsheets to data visualization systems to machine learning packages. This material is designed for students with little or no computer programming or data science experience. (2) A quarter-long "quantified self" project where students identify a set of questions about themselves or their surroundings, collect data to answer the questions, and analyze and visualize the collected data. (3) A set of guest speakers, including some who focus on the "doubts" of collecting and exploiting data, such as questions of ethics, bias, and privacy. In addition to the course project, students will complete short assignments to practice the learned tools and techniques, and will be expected to do some readings in advance of each guest speaker and engage in thoughtful discussion.

The fundamentals of cross-platform mobile application development using the React Native framework (RN). The Primary focus is on enabling students to build apps for both iOS and Android using RN. Students will explore the unique aspects that made RN a primary tool for mobile development within Facebook, Instagram, Walmart, Tesla, and UberEats, SpaceX, Coinbase and many more. Prerequisites: no formal pre-reqs but CS142/CS193x and/or prior programming experience helps. Website: web.stanford.edu/class/cs47/. To enroll in the class, please fill the following application: https://forms.gle/rhcGyigx1hWCrfA48. The application deadline is at the end of week 1.

Sophisticated data collection and analysis are now key to program success across many sports: Nearly all professional and national-level teams employ data scientists, and "datathletics" is becoming prevalent in college sports as well. This immersive seminar combines extensive hands-on data analytics with a first-hand peek into Stanford athletics. Class meetings roughly alternate between: (1) instruction in a variety of tools and techniques for analyzing and visualizing data; and (2) guest lectures by Stanford athletics coaches explaining how data is or could be used in their sport. Through regular problem sets, students bring each week's tools to bear on data related to the week's sport. One goal of the class is empowering students to perform compelling data analytics by mastering tools across a wide spectrum, including spreadsheets, the Tableau system for data preparation and visualization, Jupyter notebooks, relational databases and SQL, Python and many of its data-specific packages including Pandas, and machine learning. On the sports side, while the Stanford coaches may touch on many aspects of data collection and analysis, the main focus of this course is on using data for strategic decision-making rather than optimizing individual human performance. Prerequisites: No background in statistics or data analysis is needed, but basic programming and computing skills at the level of high school computer science or CS106A is expected. On the flip side, students with extensive experience in coding or data science may not be challenged by the technical aspects of the course.

This is a crash course in how to use a stripped-down computer system about the size of a credit card (the rasberry pi computer) to control as many different sensors as we can implement in ten weeks, including LEDs, motion sensors, light controllers, and accelerometers. The ability to fearlessly grab a set of hardware devices, examine the data sheet to see how to use it, and stitch them together using simple code is a secret weapon that software-only people lack, and allows you to build many interesting gadgets. We will start with a "bare metal'' system --- no operating system, no support --- and teach you how to read device data sheets describing sensors and write the minimal code needed to control them (including how to debug when things go wrong, as they always do). This course differs from most in that it is deliberately mostly about what and why rather than how --- our hope is that the things you are able at the end will inspire you to follow the rest of the CS curriculum to understand better how things you've used work. Prerequisites: knowledge of the C programming language. A Linux or Mac laptop that you are comfortable coding on.

Get real-world experience researching and developing your own social impact project! Students in CS51 work in small teams to develop high-impact projects around problem domains provided by partner organizations, under the guidance and support of design/technical coaches from industry and non-profit domain experts. Main class components are workshops, team collaboration, guest speakers and mentorship. Studio provides an outlet for students to create social change through CS while engaging in the full product development cycle on real-world projects. The class culminates in a showcase where students share their project ideas and Minimum Viable Product (MVP) prototypes with stakeholders and the public. Application required; please see cs51.stanford.edu for more information. Designated a Cardinal Course by the Haas Center for Public Service.

CS52 is a required continuation of CS51 (CS + Social Good Studio). Teams enter the quarter having completed and tested a MVP with a well-defined target user, and a community partner. Students will learn to apply scalable technical frameworks, tools for deployment, and user testing techniques. The purpose of the class is to facilitate students to build a sustainable infrastructure around their product idea. Main class components are workshops, team collaboration, and software development. The class culminates in a showcase where students share their projects with stakeholders and the public.

This seminar course will explore the science behind generative AI, the likely future of tools such as DALL-E, ChatGPT, GPT-4, and Bard, and the implications for education, both in and outside of structured school environments. Students in the course will work in teams to each become experts in some aspect of AI and in some way that generative AI could create a new future for education. The background for this course is the public release of ChatGPT, which created new awareness of the potential power of AI to dramatically change our lives. In considering the possible implications for education, ChatGPT has sparked dreams of automated personal tutors, customizable teaching assistance, AI-led collaborative learning, and revolutions in assessment. In addition to optimistic projections, there are clear and significant risks. For example, will AI-assisted learning be culturally appropriate and equally available to all? Can it increase opportunity for underprivileged learners worldwide, or will it accentuate privilege and privileged views? Will it help us learn faster, or distract us from thinking deeply about difficult problems ourselves? As experienced student learners, members of the class will be able to draw on their own educational history and design learning approaches that could change the future of their education and others in college or at other stages of their lives.

This seminar will explore some of both the great discoveries that underlie computer science and the inventions that have produced the remarkable advances in computing technology. Key questions we will explore include: What is computable? How can information be securely communicated? How do computers fundamentally work? What makes computers fast? Our exploration will look both at the principles behind the discoveries and inventions, as well as the history and the people involved in those events. Some exposure to programming is required.

Is it ever reasonable to make a decision randomly? For example, would you ever let an important choice depend on the flip of a coin? Can randomness help us answer difficult questions more accurately or more efficiently? What is randomness anyway? Can an object be random? Are there genuinely random processes in the world, and if so, how can we tell? In this seminar, we will explore these questions through the lenses of philosophy and computation. By the end of the quarter students should have an appreciation of the many roles that randomness plays in both humanities and sciences, as well as a grasp of some of the key analytical tools used to study the concept. The course will be self-contained, and no prior experience with randomness/probability is necessary.