Course webpage:
WWW: https://ubc-stat.github.io/dsci-200/
-Canvas: https://canvas.ubc.ca/courses//
Lectures/Labs:
See Canvas for times and locations.
Textbooks:
-[ISLR]
Prerequisite:
DSCI 100
Evaluate and justify the data privacy needs for a given data analysis, and where needed, choose and apply an appropriate data privacy technique. Reflect on the consequences with regards to the conclusions of the chosen method.
Evaluate and justify who owns the data for a given case.
An Introduction to Statistical Learning, James, Witten, Hastie, Tibshirani, 2013, Springer, New York. (denoted [ISLR])
-Available free online: https://www.statlearning.com
-The Elements of Statistical Learning, Hastie, Tibshirani, Friedman, 2009, Second Edition, Springer, New York. (denoted [ESL])
-Also available free online: https://web.stanford.edu/~hastie/ElemStatLearn/
-This second book is a more advanced treatment of a superset of the topics we will cover. If you want to learn more and understand the material more deeply, this is the book for you. All readings from [ESL] are optional.
-Labs: [0, 20]
-Homework assignments: [0, 50]
-Clickers: [0, 10]
Total: min(65, Labs + Homework + Clickers)
These are intended to keep you on track. They are to be submitted via pull requests in your personal labs-<username> repo (see the computing tab for descriptions on how to do this).
Labs typically have a few questions for you to answer or code to implement. These are to be done during lab periods. But you can do them on your own as well. These are worth 2 points each up to a maximum of 20 points. They are due at 2300 on the day of your assigned lab section.
-If you attend lab, you may share a submission with another student (with acknowledgement on the PR). If you do not attend lab, you must work on your own (subject to the collaboration instructions for Assignments below).
-You must submit via PR by the deadline. Your PR must include at least 3 commits. After lab 2, failure to include at least 3 commits will result in a maximum score of 1.
-If you attend your lab section, you may work in pairs, submitting a single document to one of your Repos. Be sure to put both names on the document, and mention the collaboration on your PR. You still have until 11pm to submit.
-The overriding theme here is “if you put in the effort, you’ll get all the points.” Grading scheme:
-You may submit as many labs as you wish up to 20 total points.
-There are no appeals on grades.
-It’s important here to recognize just how important active participation in these activities is. You learn by doing, and this is your opportunity to learn in a low-stakes environment. One thing you’ll learn, for example, is that all animals urinate in 21 seconds.1
-There will be 5 assignments. These are submitted via pull request similar to the labs but to the homework-<username> repo. Each assignment is worth up to 10 points. They are due by 2300 on the deadline. You must make at least 5 commits. Failure to have at least 5 commits will result in a 25% deduction on HW1 and a 50% deduction thereafter. No exceptions.
Assignments are typically lightly marked. The median last year was 8/10. But they are not easy. Nor are they short. They often involve a combination of coding, writing, description, and production of statistical graphics.
-After receiving a mark and feedback, if you score less than 7, you may make corrections to bring your total to 7. This means, if you fix everything that you did wrong, you get 7. Not 10. The revision must be submitted within 1 week of getting your mark. Only 1 revision per assignment. The TA decision is final. Note that the TAs will only regrade parts you missed, but if you somehow make it worse, they can deduct more points.
-The revision allowance applies only if you got 3 or more points of “content” deductions. If you missed 3 points for content and 2 more for “penalties” (like insufficient commits, code that runs off the side of the page, etc), then you are ineligible.
-Discussing assignments with your classmates is allowed and encouraged, but it is important that every student get practice working on these problems. This means that all the work you turn in must be your own. The general policy on homework collaboration is:
-Adherence to the above policy means that identical answers, or nearly identical answers, cannot occur. Thus, such occurrences are violations of the Course’s Academic honesty policy.
-These rules also apply to getting help from other people such as friends not in the course (try the problem first, discuss strategies, not step-by-step solutions, acknowledge those from whom you received help).
-You may not use homework help websites under any circumstances. You are also very strongly discouraged from using ChatGPT, Stack Overflow, and so on. The purpose here is to learn. Good faith efforts toward learning are rewarded.
-You can always, of course, ask me for help on Slack. And public Slack questions are allowed and encouraged.
-You may also use external sources (books, websites, papers, …) to
-But external sources must be used to support your solution, not to obtain your solution. You may not use them to
-If you use code from online or other sources (including generative AI), you must include code comments identifying the source. It must be clear what code you wrote and what code is from other sources. This rule also applies to text, images, and any other material you submit.
-Please talk to me if you have any questions about this policy. Any form of plagiarism or cheating will result in sanctions to be determined by me, including grade penalties (such as negative points for the assignment or reductions in letter grade) or course failure. I am obliged to report violations to the appropriate University authorities. See also the text below.
-These are short multiple choice and True / False questions. They happen in class. For each question, correct answers are worth 4, incorrect answers are worth 2. You get 0 points for not answering.
Suppose there are N total clicker questions, and you have x points. Your final score for this component is
-max(0, min(5 * x / N - 5, 10)).
Note that if your average is less than 1, you get 0 points in this component.
-In addition, your final grade in this course will be reduced by 1 full letter grade.
-This means that if you did everything else and get a perfect score on the final exam, you will get a 79. Two people did this last year. They were sad.
-DON’T DO THIS!!
-This may sound harsh, but think about what is required for such a penalty. You’d have to skip more than 50% of class meetings and get every question wrong when you are in class. This is an in-person course. It is not possible to get an A without attending class on a regular basis.
-To compensate, I will do my best to post recordings of lectures. Past experience has shown 2 things:
-The purpose is to let you occasionally miss class for any reason with minimal consequences. See also below. If for some reason you need to miss longer streches of time, please contact me or discuss your situation with your Academic Advisor as soon as possible. Don’t wait until December.
-The total you can accumulate across these 3 components is 65 points. But you can get there however you want. The total available is 80 points. The rest is up to you. But with choice, comes responsibility.
-Rules:
-We’re not going to police this stuff. You don’t need to let me know. There is no reason that every single person enrolled in this course shouldn’t get > 65 in this class.
-Illustrative scenarios:
-Choose your own adventure. Note that the biggest barrier to getting to 65 is skipping the assignments.
-Late lab/homework submissions will not be accepted.
-More specifically, any submission that we receive after grading has commenced will receive a 0. We likely won’t start grading at 11:01pm on the due date, so don’t worry if you’re a few minutes late. On the other hand, don’t even bother submitting if you’ve missed the deadline by a few days.
-This policy may seem harsh, but remember that there are many paths to a full 65 on the effort based grade. If you miss one assignment, focus on doing well on the other labs/assignments and you might still end up with an A in the course.
-35 points
-The Final is very hard. By definition, it cannot be effort-based.
-It is intended to separate those who really understand the material from those who don’t. Last year, the median was 50%. But if you put in the work (do all the effort points) and get 50%, you get an 83 (an A-). If you put in the work (do all the effort points) and skip the final, you get a 65. You do not have to pass the final to pass the course. You don’t even have to take the final.
-The point of this scheme is for those who work hard to do well. But only those who really understand the material will get 90+.
-If you think you’re sick, stay home no matter what.
-Masks. For our in-person meetings in this class, it is important that all of us feel as comfortable as possible engaging in class activities while sharing an indoor space. Masks are a primary tool to make it harder for Covid-19 to find a new host. Please feel free to wear one or not given your own personal circumstances. Note that there are some people who cannot wear a mask. These individuals are equally welcome in our class.
-Vaccination. If you have not yet had a chance to get vaccinated against Covid-19, vaccines are available to you, free. See http://www.vch.ca/covid-19/covid-19-vaccine for help finding an appointment. Boosters will be available later this term. The higher the rate of vaccination in our community overall, the lower the chance of spreading this virus. You are an important part of the UBC community. Please arrange to get vaccinated if you have not already done so. The same goes for Flu.
-If you are sick, it’s important that you stay home – no matter what you think you may be sick with (e.g., cold, flu, other).
-Please talk with me if you have any concerns or ask me if you are worried about falling behind.
-UBC provides resources to support student learning and to maintain healthy lifestyles but recognizes that sometimes crises arise and so there are additional resources to access including those for survivors of sexual violence. UBC values respect for the person and ideas of all members of the academic community. Harassment and discrimination are not tolerated nor is suppression of academic freedom. UBC provides appropriate accommodation for students with disabilities and for religious, spiritual and cultural observances. UBC values academic honesty and students are expected to acknowledge the ideas generated by others and to uphold the highest academic standards in all of their actions. Details of the policies and how to access support are available here.
-UBC Vancouver Statement
-Academic honesty is essential to the continued functioning of the University of British Columbia as an institution of higher learning and research. All UBC students are expected to behave as honest and responsible members of an academic community. Breach of those expectations or failure to follow the appropriate policies, principles, rules, and guidelines of the University with respect to academic honesty may result in disciplinary action.
-For the full statement, please see the 2022/23 Vancouver Academic Calendar
-Course specific
-Several commercial services have approached students regarding selling class notes/study guides to their classmates. Please be advised that selling a faculty member’s notes/study guides individually or on behalf of one of these services using UBC email or Canvas, violates both UBC information technology and UBC intellectual property policy. Selling the faculty member’s notes/study guides to fellow students in this course is not permitted. Violations of this policy will be considered violations of UBC Academic Honesty and Standards and will be reported to the Dean of Science as a violation of course rules. Sanctions for academic misconduct may include a failing grade on the assignment for which the notes/study guides are being sold, a reduction in your final course grade, a failing grade in the course, among other possibilities. Similarly, contracting with any service that results in an individual other than the enrolled student providing assistance on quizzes or exams or posing as an enrolled student is considered a violation of UBC’s academic honesty standards.
-Some of the problems that are assigned are similar or identical to those assigned in previous years by me or other instructors for this or other courses. Using proofs or code from anywhere other than the textbooks, this year’s course notes, or the course website is not only considered cheating (as described above), it is easily detectable cheating. Such behavior is strictly forbidden.
-In previous years, I have caught students cheating on the exams or assignments. I did not enforce any penalty because the action did not help. Cheating, in my experience, occurs because students don’t understand the material, so the result is usually a failing grade even before I impose any penalty and report the incident to the Dean’s office. I carefully structure exams and assignments to make it so that I can catch these issues. I will catch you, and it does not help. Do your own work, and use the TAs and me as resources. If you are struggling, we are here to help.
-If I suspect cheating, your case will be forwarded to the Dean’s office. No questions asked.
-Generative AI
-Tools to help you code more quickly are rapidly becoming more prevalent. The point of this course is not to “complete assignments” but to learn coding (and other things). With that goal in mind, I recommend you avoid the use of Generative AI. It is unlikely to contribute directly to your understanding of the material. Furthermore, I have experimented with certain tools on the assignments for this course and have found the results underwhelming.
-The material in this course is best learned through trial and error. Avoiding this mechanism (with generative AI or by copying your friend) is a short-term solution at best. I have tried to structure this course to discourage these types of short cuts, and minimize the pressure you may feel to take them.
-These are handled according to UBC policy. Please see
- -Students who miss the final exam must report to their Faculty advising office within 72 hours of the missed exam, and must supply supporting documentation. Only your Faculty Advising office can grant deferred standing in a course. You must also notify your instructor prior to (if possible) or immediately after the exam. Your instructor will let you know when you are expected to write your deferred exam. Deferred exams will ONLY be provided to students who have applied for and received deferred standing from their Faculty.
-Course work at this level can be intense, and I encourage you to take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress. I struggle with these issues too, and I try hard to set aside time for things that make me happy (cooking, playing/listening to music, exercise, going for walks).
-All of us benefit from support during times of struggle. If you are having any problems or concerns, do not hesitate to speak with me. There are also many resources available on campus that can provide help and support. Asking for support sooner rather than later is almost always a good idea.
-If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, I strongly encourage you to seek support. UBC Counseling Services is here to help: call 604 822 3811 or visit their website. Consider also reaching out to a friend, faculty member, or family member you trust to help get you the support you need.
-A careful reading of this paper with the provocative title “Law of Urination: all mammals empty their bladders over the same duration” reveals that the authors actually mean something far less precise. In fact, their claim is more accurately stated as “mammals over 3kg in body weight urinate in 21 seconds with a standard deviation of 13 seconds”. But the accurate characterization is far less publicity-worthy.↩︎