diff --git a/policies.md b/policies.md index 8dcfcd8..c6d944e 100644 --- a/policies.md +++ b/policies.md @@ -13,6 +13,7 @@ description: >- {:.no_toc} ## Table of Contents + {: .no_toc .text-delta } 1. TOC @@ -20,187 +21,155 @@ description: >- --- -## Policies - ## About the Course ### Course Description -Foundations of Data Science combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It also delves into social issues surrounding data analysis, such as privacy and study design. +Foundations of Data Science combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It also delves into social issues surrounding data analysis such as privacy and study design. ### Prerequisites -The curriculum and format are designed specifically for students who have not previously taken statistics or computer science courses. Students with some prior experience in either statistics or computing are welcome to enroll and often find that this course offers a new perspective that blends computational and inferential thinking. Students who have taken several statistics or computer science courses should instead take a more advanced course like [Data 100](https://ds100.org/). +The curriculum and format is designed specifically for students who have not previously taken statistics or computer science courses. Students with some prior experience in either statistics or computing are welcome to enroll and often find that this course offers a new perspective that blends computational and inferential thinking. Students who have taken several statistics or computer science courses should instead take a more advanced course like [Data 100](https://ds100.org/). ### Materials & Resources -Our primary text is an online book called [Computational and Inferential Thinking: The Foundations of Data Science](http://inferentialthinking.com/). This text was written for the course by the course instructors. A complete PDF of the textbook can be found in the [Student Materials](https://drive.google.com/drive/folders/1VWVpfbm1-6zVmyohB_-Jm4o0EWsxzCxr?usp=drive_link) Google Drive. +Our primary text is an online book called [Computational and Inferential Thinking: The Foundations of Data Science](http://inferentialthinking.com/). This text was written for the course by the course instructors. A complete PDF of the textbook can be found in the Student Materials Google Drive. -The computing platform for the course is hosted at data8.datahub.berkeley.edu. Students find it convenient to use their own computer for the course. If you do not have adequate access to a personal computer, we can help you borrow a machine; please contact data8@berkeley.edu. +The computing platform for the course is hosted at [data8.datahub.berkeley.edu](https://data8.datahub.berkeley.edu/). Students find it convenient to use their own computer for the course. If you do not have adequate access to a personal computer, we can help you borrow a machine; please contact [data8@berkeley.edu](mailto:data8@berkeley.edu). - -In past semesters of Data 8, more than 40% of the students received grades in the A+/A/A- range and more than 35% received grades in the B+/B/B- range. +Instructors not TA's will responed to any questions regarding grade bins or letter grades. -Grades for Homeworks, Projects, and Labs will be posted on Gradescope about 1 week after the assignment's due date. Solutions to the assignment and common mistakes will also be posted on Ed. It is up to you to check the solutions and request a regrade request before the regrade deadline (typically 5 days after grade release). Regrade requests can be made on Gradescope. Any regrade request past the deadline will not be looked at; this is to enforce the same deadline across all students, so please do not delay reviewing your work. + -We understand that the submission process is new for many students taking the course. To account for this, we will do our best to accommodate submission-related issues (submitting to the wrong assignment, not saving files correctly, autograder timing out) up until the third week of the course. After the third week, it is **your responsibility** to confirm you have submitted your work correctly. We reserve the right to impose penalties for having to resubmit students’ work beyond this point. +### Grade Bins -### Late Submission - -The deadline for all assignments in this course is **5 PM PST**. **Submissions after this time** will be **accepted for 24 hours and will incur a 20% penalty**. Any submissions later than 24 hours after the deadline will not be accepted. +This semester, we will use **grade bins** to determine the **lowest possible letter grade** based on final composite scores (where each grade component is weighted according to above table). While we will not raise these bins, **we may lower them**. The table below contains the grade bins for this semester. For example, final composite scores between 80% (inclusive) and 90% (exclusive) will receive grades of **_at least_** B+/B/B-. -If you need an extension, instructions on how to request an assignment extension are in the following section. +| **Composite Score (%) Range** | **Grade Range** | +| [90, 100] | A+/A/A- | +| [80, 90) | B+/B/B- | +| [65, 80) | C+/C/C- | -Your two lowest homework scores and two lowest lab scores will be dropped in the calculation of your overall grade. If you have an ongoing situation that prevents you from completing course content, please contact your lab TA. +### Regrades -### Assignment Extensions +Grades for Homeworks, Projects, and Labs will be posted on Gradescope after the assignment’s due date. Solutions to the assignment and common mistakes will also be posted on Ed. It is up to you to check the solutions and request a regrade request before the regrade deadline (typically 5 days after grade release). Regrade requests can be made on Gradescope. Any regrade request past the deadline will not be looked at; this is to enforce the same deadline across all students, so please do not delay in reviewing your work. -We understand that life happens and want to provide you with the support you need. If you need to request an extension, please fill out [this form](https://docs.google.com/forms/d/e/1FAIpQLScIjB9LSxV7UPKdNrAWbPJWJMJqV05P3jyznuAtAqQPmB79EA/viewform?usp=sf_link). Submissions to the form will be visible only to the course instructors and select Lead TAs. Please ensure that extension requests are submitted before the deadline to ensure a timely response on our end. - -Extension requests are most likely to be approved if they are submitted **at least 3 days in advance and requesting at most 2 days’ extension**. Requests outside of these guidelines are subject to more detailed review and may require a meeting with course staff or be denied. Students with DSP extension accommodations must submit extension requests before the assignment deadline. +For the midterm exam, there will be a regrade request submission window. Please review the solutions and common mistakes before submitting a regrade request. Requests where a rubric item was incorrectly selected or not selected will be reviewed, but any regrade requests that ask to change the rubric or for partial credit will be ignored. -Please read the entirety of the form and its instructions before/while submitting a request to reduce confusion. +## Assignments -We hope that this policy encourages you to be proactive in communicating difficulties in advance while also allowing flexibility in the case of unforeseen circumstances. +### Submitting Assignments -### Accommodations +All assignments (homework, labs, and projects) will be submitted on Gradescope. Please refer to [this tutorial](https://drive.google.com/file/d/1j-H2NCyC01SL8P2rkyiz7-AYFXE11HCD/view?usp=sharing) for submitting assignments. -We will provide appropriate accommodations to all students enrolled in Berkeley's [Disabled Students Program (DSP)](https://dsp.berkeley.edu/). To ensure that you receive the appropriate accommodations, have your DSP specialist submit a letter confirming your status and accommodations. +### Late Submission -CONTENT FROM ACCOMODATIONS PAGE STARTS HERE +**The deadline for all assignments in this course is 11 PM PT**. Assignments submitted **until 11:59 PM** on the day of the deadline will not be marked as late. **Homework and lab submissions after this time will not be accepted**. Instructions on if you qualify, and how to request assignment extensions are on the [accommodations page](../accommodations/). -## Assignment Extensions +We understand that life happens. For this reason, **your two lowest homework scores and two lowest lab scores will be dropped in the calculation of your overall grade.** If you have an ongoing situation that prevents you from completing course content, please contact your lab TA. -We understand that life happens and want to provide you with the support you need. If you need to request an extension, please fill out the [DATA 8 SP24 Extension Request](https://docs.google.com/forms/d/e/1FAIpQLScIjB9LSxV7UPKdNrAWbPJWJMJqV05P3jyznuAtAqQPmB79EA/viewform?usp=sf_link) form. Submissions to this form will be visible only to the Course Directors and Grading Leads. +Projects will be accepted up to 2 days (48 hours) late. Projects submitted fewer than 24 hours after the deadline will receive 2/3 credit, and projects submitted between 24 and 48 hours after the deadline will receive 1/3 credit. Projects submitted 48 hours or more after the deadline will receive no credit. -## DSP Accommodations +### Assignment Extensions -We will provide appropriate accommodations to all students enrolled in Berkeley’s [Disabled Students Program (DSP)](https://dsp.berkeley.edu/). To ensure that you receive the appropriate accommodations, please have your DSP specialist submit a letter confirming your status and accommodations. +Due to the fast pace of the summer offering, **we are unable to offer assignment extensions for the vast majority of students.** As mentioned above, you are provided two lab and homework drops for emergency situations that may come up. Only after using all your assignment drops, if you continue to encounter further emergencies beyond your control, please do not hesitate to reach out. Please fill out the [Extenuating Circumstances Form](https://docs.google.com/forms/d/e/1FAIpQLScuJXqPqocHgYd1SLx2GryGVUhcA6_OzDtYZvbhek3La65KxA/viewform), and a course staff member will reach out to you and provide a space for conversation, as well as to arrange accommodations as necessary. Note that you will be asked to provide supporting documentation, and these requests will be approved on a case-by-case basis. -If you’re not enrolled in DSP, or are in the process of being onboarded by DSP, you may still be eligible for accommodations. We also aim to provide fair and appropriate accommodations to any students who, because of extenuating circumstances, may need them. Please reach out to data8@berkeley.edu if this is the case and our DSP Lead will get back to you. +### Learning Cooperatively -## Privacy +We encourage you to discuss course content with your friends and classmates as you are working on your assignments. No matter your academic background, you will learn more if you work alongside others than if you work alone. Ask questions, answer questions, and share ideas liberally. -All DSP and accommodations-related materials for this course are kept in a repository separate from the rest of the course materials that is visible only to the instructors and selected Lead GSIs. +If some emergency takes you away from the course for an extended period, or if you decide to drop the course for any reason, please don’t just disappear silently! You should inform your lab TA and your project partner (if you have one) immediately, so that nobody is expecting you to do something you can’t finish. -For any DSP and accommodations-related communications, please reach out to data8@berkeley.edu. This inbox will be visible to future members of course staff, so if you ever have a communication that you wish to remain private, let us know and we can delete the email exchange once the conversation is resolved. +## Accommodations -CONTENT FROM ACCOMODATIONS PAGE ENDS- HERE +We will provide appropriate accommodations to all students enrolled in Berkeley's [Disabled Students Program (DSP)](https://dsp.berkeley.edu/). To ensure that you receive the appropriate accommodations, have your DSP specialist submit a letter confirming your status and accommodations. -If you're not enrolled in DSP, or are in the process of being onboarded by DSP, you may still be eligible for accommodations. We also aim to provide fair and appropriate accommodations to any students who, because of extenuating circumstances, may need them. Please reach out to [data8@berkeley.edu](mailto:data8@berkeley.edu) in this case. +If you’re not enrolled in DSP, or are in the process of being onboarded by DSP, you may still be eligible for accommodations. We also aim to provide fair and appropriate accommodations to any students who, because of extenuating circumstances, may need them. Please reach out to [data8@berkeley.edu](mailto:data8@berkeley.edu) if this is the case and our DSP Lead will get back to you. -#### Privacy +### Privacy All DSP and accommodations-related materials for this course are kept in a repository separate from the rest of the course materials that is visible only to the instructors and selected Lead GSIs. -For any DSP and accommodations-related communications, please reach out to [data8@berkeley.edu](mailto:data8@berkeley.edu) and the DSP Lead will get back to you. This inbox will be visible to future members of course staff, so if you ever have a communication that you wish to remain private, let us know and we can delete the email exchange once the conversation is resolved. - -### Learning Cooperatively +For any DSP and accommodations-related communications, please reach out to [data8@berkeley.edu](mailto:data8@berkeley.edu). This inbox will be visible to future members of course staff, so if you ever have a communication that you wish to remain private, let us know and we can delete the email exchange once the conversation is resolved. -We encourage you to discuss course content with your friends and classmates while working on your assignments. No matter your academic background, you will learn more if you work alongside others than if you work alone. Ask questions, answer questions, and share ideas liberally. - -If some emergency takes you away from the course for an extended period, or if you decide to drop the course for any reason, please don't just disappear silently! You should inform your lab TA and your project partner (if you have one) immediately so that nobody is expecting you to do something you can't finish. - -### Academic Honesty +## Academic Honesty You must write your answers in your own words, and you must not share your completed work. The exception to this rule is that you can share everything related to a project with your project partner (if you have one) and turn in one project between the two of you, and if you are attending a lab session and have a lab partner you can share everything related to that lab with your lab partner. -Make a serious attempt at every assignment yourself. If you get stuck, read the textbook and go over the lectures and lab discussion. After that, go ahead and discuss any remaining doubts with others, especially the course staff. That way, you will get the most out of the discussion. It is important to keep in mind the limits of collaboration. As noted above, you and your peers are encouraged to discuss course content and approaches to problem solving. But you cannot share your code or answers with other students. Doing so is considered academic misconduct, and it won’t help your peers either. Sharing answers will set them up for trouble on upcoming assignments and exams. +Make a serious attempt at every assignment yourself. If you get stuck, read the textbook and go over the lectures and lab discussion. After that, go ahead and discuss any remaining doubts with others, especially the course staff. That way you will get the most out of the discussion. It is important to keep in mind the limits to collaboration. As noted above, you and your friends are encouraged to discuss course content and approaches to problem solving. But you are not allowed to share your code or answers with other students. Doing so is considered academic misconduct, and it doesn’t help them either. It sets them up for trouble on upcoming assignments and on the exams. In addition, posting course content such as homeworks, projects, and exams on any 3rd party websites or submitting your own answers on outside sites/forums is considered academic misconduct. -You are also not permitted to turn in answers or code that you have obtained from others or online sources. **This includes any generative AI tools, including but not limited to ChatGPT.** Not only does such copying count as academic misconduct, but it also circumvents the pedagogical goals of an assignment. You must solve problems with the resources made available in the course. You should never look at or have solutions in your possession from another student or another semester. +**You are also not permitted to turn in answers or code that you have obtained from others.** Not only does such copying count as academic misconduct, it circumvents the pedagogical goals of an assignment. You must solve problems with the resources made available in the course. You should never look at or have in your possession solutions from another student or another semester. -Please read Berkeley's [Code of Conduct](https://sa.berkeley.edu/code-of-conduct) carefully. Penalties for academic misconduct in Data 8 are severe and include reporting to the [Center for Student Conduct](https://sa.berkeley.edu/conduct). They might also include an F in the course or even dismissal from the university. It's just not worth it! +Please read Berkeley's [Code of Conduct](https://sa.berkeley.edu/code-of-conduct) carefully. Penalties for academic misconduct in Data 8 are severe and include reporting to the [Center for Student Conduct](https://sa.berkeley.edu/conduct). They might also include a F in the course or even dismissal from the university. It's just not worth it! -When you need help, reach out to the course staff using Ed, in office hours, and/or during labs. You are not alone in Data 8! Instructors and staff are here to help you succeed. We expect that you will work with integrity and respect for other members of the class, just as the course staff will work with integrity and respect for you. +When you need help, reach out to the course staff using Ed, in office hours, and/or during labs. **You are not alone in Data 8!** Instructors and staff are here to help you succeed. We expect that you will work with integrity and with respect for other members of the class, just as the course staff will work with integrity and with respect for you. -Finally, know that it's normal to struggle. Berkeley has high standards, which is one of the reasons its degrees are valued. Everyone struggles, even though many try not to show it. Even if you don't fully master everything covered, you can build on what you learn, whereas if you cheat, you'll have nothing to build on. You aren't expected to be perfect; it's ok not to get an A. +Finally, know that it's normal to struggle. Berkeley has high standards, which is one of the reasons its degrees are valued. Everyone struggles even though many try not to show it. Even if you don't learn everything that's being covered, you'll be able to build on what you do learn, whereas if you cheat you'll have nothing to build on. You aren't expected to be perfect; it's ok not to get an A. ## A Parting Thought -The main goal of the course is that you should learn and have a fantastic experience doing so. Please keep that goal in mind throughout the semester. Welcome to Data 8! --> - - - +The main goal of the course is that you should learn, and have a fantastic experience doing so. Please keep that goal in mind throughout the semester. Welcome to Data 8!