These will be made available to you (UCSD podcast). However, lecture attendance is not required. Our goal is to make lectures and office hours worth your while to attend, e.g. We will get back to you within 48 hours with our final decision.a specific reference to something said in a lecture, the readings, or office hours) Provide evidence for why your answer is correct and merits a regrade (i.e.Initiate the regrade through Gradescope (if it is a group project, confer w/ your team first and submit one regrade after your team comes to a consensus).If you think a grading error has occurred please follow these steps: We want to spend that time on regrades where a serious issue has occurred, or with helping students learn the material outside of class. These discussions require a serious investment of time. This is not to discourage students from requesting legitimate regrades, but to discourage students from arguing about 1 point (which is worth 0.05% of your grade). This means your grade can either stay the same, go up, or go down. When we regrade, we closely go through the entire assignment again and reevaluate it as a whole. In our experience less than 3% of the time a regrade results in a change. A grader may incorrectly take off 1-2 points, but they are as likely to give students 1-2 points. The regrade policy is here to protect students from serious issues in grading, not to provide students with a platform to argue about, or plead for an extra point. Ultimately it is your responsibility to check your final grade and get in touch if you believe there is a problem. Grades are released on Gradescope often a week after the submission date, typically sooner. Grades are not rounded up, that’s why we have included 5 bonus points. Your letter grade will be determined using the standard grading scale.Final exam date: No final exam, only a final group project.Describe potential pitfalls of data analyses, how to identify them, and how to avoid themĥ Reading Quizzes (lowest quiz score dropped).Demonstrate how to think critically about data, and how to approach problems with a “data-first” mindset.Communicate data-related topics and projects.Identify data science questions and the appropriate analytic approach to answering those questions.Discuss data privacy and ethical concerns with real-world examples.Comprehend core data science concepts and examine their applications.Assignments and the final are submitted through Gradescope.Reading quizzes and the exam are taken through Gradescope.All course materials are provided through this website.The content of this project itself is licensed under the Creative Commons Attribution 3.0 Unported license, and the underlying source code used to format and display that content is licensed under the MIT license. Start planning ahead now to avoid late submissions and issues later in the quarter. This will require good time management and planning on your part. This is an assignment-heavy course load to get you as much practice as possible. These assignments are intermingled with your project proposal, checkpoints and final project (due finals week). Some example projects from the Spring 2017, Winter 2018, Spring 2019, Fall 2019, Winter 2020, Spring 2020, Fall 2020, Winter 2021, Spring 2021, Fall 2021, Winter 2022 iterations of the class are available Final ProjectsĪ core component of the class is completing a group project. ReadingsĪ suggested reading list (recommended, but not required). Tutorial Notebooks that run along with the topics of the class and can help as you complete assignments and discussion section tasks.Īssignments and Discussion Labs will be completed on.Discussion Labs are released, completed, and submitted on datahub.All links to class videos, slides, and notebooks used MWF will be included here. Slides and materials will be organized by week.The most recent iteration of this class is Winter Quarter 2022, the syllabus for which is available here. Here is an overview and map of the COGS 108 Organization, which hosts materials and assignments for the class. COGS 108 - Data Science in Practice - is a class offered by the Cognitive Science Department of UC San Diego, taught by Professors Bradley Voytek, Shannon Ellis, and Jason Fleischer.
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