From def8a7ac9ff6518142d1a77c008dbe4f98f7ec53 Mon Sep 17 00:00:00 2001 From: kdkorthauer Date: Fri, 3 Jan 2025 08:41:05 -0800 Subject: [PATCH] add TA pronouns to syllabus --- search.json | 6 +++--- syllabus.Rmd | 2 +- syllabus.html | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/search.json b/search.json index 57d2ca5..723784e 100644 --- a/search.json +++ b/search.json @@ -89,15 +89,15 @@ "title": "Submission Guide", "author": [], "contents": "\nHow to start and submit assignments through GitHub and Gradescope\nYou need to have Git installed on your computer, and have a GitHub account. You can use command line Git or RStudio itself to push, pull, etc. to/from GitHub. See Seminar 1 for an illustrated setup guide and illustration of the full workflow from accepting an assignment to submission.\nStart a new assignment\nAccept: Click on the assignment invitation link for the assignment in Canvas. Each individual assignment/deliverable will have its own link, whereas the group project will only have one link at the start of the project (all deliverables will use the same shared repository). After you accept the assignment, a GitHub repository will be automatically be generated for you.\nClone the repository to your local machine (see Seminar 1 for illustration of this workflow). Now you can begin working on your submission files locally. See below for more detail on repositories and organizing files.\nHow to “turn in” your work: Save, Commit, Push, Submit\nFollow these steps carefully, and be sure they are complete before the deadline of each deliverable. Important Note: the deadline is automated, so you will not be able to perform a late submission (even by one second). Make sure to allow plenty of time for submission!\nPlease include a link to your assignment repository at the top of your main output file.\nSave all the files associated with your solution locally (i.e. for coding assignments R Markdown, GitHub Markdown, and any associated output files - see below for more detail).\nCommit those files to your local Git repository.\nPush the current state of your local repo to your private repo on GitHub. Double check that this was successful, by viewing your repo on GitHub to verify that the most recent commits are visible.\nSubmit the repository on Gradescope via the link in Canvas.\nThat’s it! You can commit/push to update your assignment as often as you’d like before the deadline (frequent commits are actually encouraged). A snapshot of your repo at the deadline will automatically be captured for grading.\n\nIf you’re concerned that something hasn’t gone right with the submission and the deadline is imminent, send the TAs an e-mail with your assignment attached. Note: this is only a last-minute emergency back-up plan, and penalties may apply. We will work with you until you get it submitted via GitHub/Gradescope.\n\nFiles & organization\nIndividual assignments\nUse the repositories that are automatically generated for you when you accept each assignment (e.g., the repo with the format STAT540-UBC-2024/seminar-01-yourGitHubID will contain your Seminar 1 deliverable). Do not use additional branches or any other repositories to submit your course work.\nAll your work for each deliverable should be nicely organized in its repository.\nYour repositories should include a README.md file with a clear description. These are like each assignment’s landing page.\nGroup project\nA shared team repository will be created for your group project; all group-level project deliverables will be submitted in this repo.\nInformation on organizing this repo can be found on the Group Project Rubrics Page\nMain required file types\nIn general for coding assignments, you need to commit and push your R Markdown, GitHub Markdown, and any associated figure/output files with your assignments (note: you should not to commit raw data to your repository - see below). Writing assignments are the exception (e.g. paper critique, project proposal): a Markdown file sufficient.\n\nThis workflow is specific to STAT 540 and may not necessarily reflect your workflow in other contexts.\n\nR Markdown (source file)\nWrite your homework in R Markdown. The file extension should be .Rmd.\nRecommendation: For coding assignments, start with the Markdown or R Markdown file that creates the assignment itself! You can take some things away (unnecessary detail) and add others (R chunks, explanatory text) to morph this into your homework solution.\nGitHub Markdown (output file)\nCompile your homework to GitHub Markdown (file extension should be .md).\nIn the YAML header, specify output: github_document as shown below\nRStudio’s “Knit” button will create the GitHub Markdown file (.md) when the output is specified in this way. The Github Markdown file is a file that can be rendered nicely on GitHub.\n\n---\ntitle: \"Homework assignment\"\nauthor: \"Your Name\"\noutput: github_document\n---\n\nNever ever edit the output Markdown “by hand”. Only edit the R Markdown source and then regenerate the downstream products from that.\nFigure files (output files)\nBy default, any figures created by your .Rmd are placed into a subdirectory titled [filename]_files where [filename] is the basename (without extension) of your .Rmd file.\nThe GitHub Markdown file links to these figure files and, therefore, requires these files to be present to in order to include the figures in your full report.\nFiles not to commit: Data files (input files)\nLocally, you may keep input data files in some logical place within the assignment’s directory. But list the names of such data files in your top-level .gitignore file, so that Git ignores it. Or better yet, include commands that download the data directly (if available and not too large). This is to reduce storage required on GitHub and when the TAs download your repositories for marking.\nGeneral tips for working on the assignments\nStart early. Even if you are already fluent with all the seminar materials, it will still take time to answer all the questions, and prepare files for submission.\nCommit early; commit often (and push). It’s helpful to have an earlier version of your work to return to (e.g. to debug if something suddenly throws an error and you can’t figure out why 🤯, or if your cat walks on your keyboard and deletes your repo 🤷).\nWhen you get stuck or when you run into an error, ask yourself these questions:\nAm I in the right working directory ?\nIs this material covered in one of the lectures or seminars?\nCan I google how to do this?\nIs there an R package that can more efficiently do what I’m attempting now?\nAm I using the right parameters for this function? (hint, type ?function_name() where function_name is the name of the function, to check if you’re using it right)\n\nYour Rmarkdown can’t be knitted? Is it because you didn’t define all the variables inside your Rmarkdown? Your code chunk might run in RStudio if all the variables are all defined in the RStudio environment, but it wouldn’t run as the Rmarkdown is being knitted if the variables aren’t defined in the Rmarkdown document.\nOverall presentation and mechanics refer to the fluency, neatness, easiness to read. For example:\nExplain what you’re doing to show your understanding, i.e. sandwiching your code and result with some explanations and interpretation. We don’t want to see just a graph or some R outputs standing alone in a question.\nUse headings/subheadings to distinguishes different sections/questions\nWell-structured dataframes and reusable functions can often lighten your work load and increase readability of code.\nUse inline R code whenever you refers to the value of a variable in a block of text.\nComment on your code so that everyone, including yourself, can easily follow through your steps. In R, comment follows the number sign. For example # What I did here\nConsider formating R output in a nice table instead of just printing it (e.g. see the kable() function from knitr package)\n\nYou might find cheatsheets from Rstudio useful, in terms of graphing, making an awesome R markdown, etc.\nMake it easy for others to run your code\nIn exactly one, very early R chunk, load any necessary packages, so your dependencies are obvious.\nIn exactly one, very early R chunk, import anything coming from an external file. This will make it easy for someone to see which data files are required, edit to reflect their locals paths if necessary, etc. There are situations where you might not keep data in the repo itself. Ideally, include commands to download if it is available and not too large.\nPretend you are someone else. Clone a fresh copy of your own repo from GitHub, fire up a new RStudio session and try to knit your R markdown file. Does it “just work”? It should!\n\n\n\n\n", - "last_modified": "2025-01-02T14:31:28-08:00" + "last_modified": "2025-01-02T15:16:16-08:00" }, { "path": "syllabus.html", "title": "Syllabus", "description": "STAT 540: Statistical Methods for High Dimensional Biology\n", "author": [], - "contents": "\n\nContents\nLand acknowledgement\nCourse-level learning objectives\nSelected topics\nTeaching Team\nSchedule\nCourse communication\nPrerequisites and Resources\nEvaluation\nAcademic Concession\nAcademic Integrity\nPrivacy\nLearning Environment & Support\nHealth and Safety in the Classroom\nExtreme Environmental Conditions Contingency Plan\n\n2023-2024 Winter Term 2 (January 8, 2024 - April 12, 2024)\nSTAT 540 is a 3 credit course with a mandatory computing seminar\nCross-listed as STAT 540, BIOF 540, GSAT 540\nLand acknowledgement\nWe respectfully recognize that the University of British Columbia Vancouver campus is located on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm (Musqueam) people. Please take a moment to learn about the territory you are occupying by visiting this interactive indigenous land map.\nCourse-level learning objectives\nPerform exploratory data analysis and visualize genomics data\nApply tailored statistical methods to answer questions using high dimensional biological data\nMake your work reproducible, reusable, and shareable\nWork with real data in a collaborative model\nSelected topics\nBasics of molecular genetics/genomic and data collection assays (methods)\nBasic probability and math foundations\nExploratory data analysis and data quality control\nNormalization, batch correction\nCausal inference and confounding effects\nBasic statistical inference (“one gene at a time”) – linear models\nLarge-scale inference (“genome-wide”) – multiple testing\nAnalysis of microarray, RNASeq, and epigenetics data\nPrincipal Component Analysis and clustering (unsupervised machine learning)\nClassification and cross validation (supervised machine learning)\nGene set analysis and gene networks\nGenome-wide association analysis (GWAS)\nSingle-cell genomics\nTeaching Team\nFor more info on the Teaching team, including brief bios, see the ‘People’ pages on this website (linked below). Links for connecting to recurring virtual office hours will be shared via Canvas.\nInstructors\nKeegan Korthauer, PhD (She/Her/Hers)\nEmail: keegan@stat.ubc.ca\nVirtual office hours: By appointment\nYongjin Park, PhD (He/Him/His)\nEmail: ypp@stat.ubc.ca\nVirtual office hours: By appointment\nTeaching Assistants\nAsfar Lathif (He/Him/His)\nEmail: asfar.lathif@ubc.ca\nVirtual office hours: Wed 11a-12p (PST)\nJJ Hum\nEmail: jj.hum@ubc.ca\nVirtual office hours: TBA\nSchedule\nLectures (Sec 201)\nTime: Tues Thurs 9:00 - 10:30am\nLocation: Building and room number listed on Canvas\nSee Lectures for lecture materials and schedule\nSeminars (Sec S2B)\nTime: Tues 12:30pm - 1:30pm\nLocation: Building and room number listed on Canvas\nSee Seminars for schedule and seminar materials\nCourse communication\nAnnouncements\nCourse announcements will be posted on Canvas. You are responsible for checking it regularly.\nGeneral questions\nPiazza (in Canvas) for posting questions (e.g. topics discussed in class, questions about course organization, assignment clarifications, if you are stuck on an assignment and need help). This ensures the message can be seen by the entire teaching team, and that others in the class who might have the same question can learn from responses. You are also welcome to share your input on questions posted by others.\nPrivate matters\nFor private matters (e.g. requesting an extension, scheduling appointment for office hours), please contact the Teaching team by email (listed above). Do not use email to ask general questions described above.\nGroup work\nIn your final project groups, we expect you to (1) arrange regular meetings either in person or virtually and (2) make use of your team’s Piazza group.\nPrerequisites and Resources\nThis interdisciplinary course requires a broad range of skills at the interface of statistics, molecular biology / genomics, and computing. While you may have some background in one or more of the following areas, you are not expected to be an expert in all. That said, to be successful in the course, you may need to spend a bit more time in the areas that you have less experience in. Although there are no official prerequisites for the course, here is a list of useful skills to bring into the course and/or learn along the way.\nStatistics:\nYou should have already taken a university level introductory statistics course.\nThis free online book “Modern Statistics for Modern Biology” by Susan Holmes and Wolfgang Huber is a great reference for introduction or review of many of the statistical concepts that are relevant for this course.\nThis free online book “Data Analysis for the Life Sciences” by Rafael Irizarry and Michael Love is another great resource for introduction or review of many of the statistical concepts relevant in this course, with an emphasis on implementation in R.\nBiology:\nNo requirements, but you are expected to learn things like, e.g. the difference between DNA and RNA, and the difference between a gene and a genome.\nSee this video and this slideset for some basic introductory material.\nGo through the (optional, not for a grade) molecular biology quiz on Canvas\nThis free online book “Concepts of Biology” by Fowler, Roush & Wise is a great resource for biological concepts, in particular chapters 6 and 9\nThis free online book “Biology” by Clark, Douglas & Choi goes more in-depth, see Chapters 14, 15, and 16 for material on genetics that is particularly relevant for this course.\nNo matter your prior experience, when you come across a new biological concept during the course or in your research, you might need to spend a bit of time ‘learning as you go’ - this is expected and something I still do regularly in my day-to-day research!\nR:\nNo experience required but be prepared to do a lot of self-guided learning if you haven’t taken other courses on R or used it in your research.\nStart now by installing R and the HIGHLY RECOMMENDED “integrated development environment” (IDE) RStudio - both are free and open source.\nYou should be able to run R on your own computer during each seminar session.\nIf you are new to R, check out this blog post on getting started with R.\nThis free online book “Introduction to Data Science” by Rafael Irizarry is also a great resource for getting more in-depth with R, programming basics, and the tidyverse. In particular see Chapters 1-5:\nChapter 1: Getting Started with R and R Studio\nChapter 2: R Basics\nChapter 3: Programming Basics\nChapter 4: The tidyverse\nChapter 5: Importing data\n\nGit/GitHub and R Markdown:\nIn this course we’ll be using the version control software Git and its web-based hosting and collaborative platform GitHub.\nThe online resource “Happy Git and GitHub for the useR” from Jenny Bryan is a great reference for these tools as we learn them.\nAnother helpful git resource is Hadley Wickham’s webinar “Collaboration and time travel- version control with git, github and RStudio”\nWe’ll learn about using R markdown to generate readable and reproducible reports with code and text, and you’ll be using that a lot in this course - see Chapter 18 of the ‘Happy Git’ resource: “Test drive R markdown”.\nEvaluation\nYou will have three individual assignments, six seminar submissions (one divided into two parts), and one group project. Deadlines are all by 11:59 pm (Pacific time) on the due date. Any submission or modification after the due date will not be graded unless you have requested an extension. If you anticipate having trouble meeting a deadline and need to request an extension/academic concession please reach out in advance via email to the instructors.\nFor more detail on each of these assignments, see the assignments page (the header of each assignment on this page points to the relevant section of the assignments page). Also refer to this visual overview of the timeline.\nFor detailed instructions on how to work and submit assignments for this course, please see the Submission Guide.\nIntro Assignment (5%)\nAn introductory assignment designed to assess basic knowledge of GitHub, R and Rmarkdown\n\nAssignment\nDue Date\nIntro Assignment\nThu 23 January 2025\n\nSeminar completion (30%)\nYou will submit short “deliverables” for each seminar session\nEach seminar session is weighted equally, but the lowest score will be dropped (so that the 10 seminars with highest score will each count for 3% of the final grade).\nThese deliverables give practical experience applying the knowledge that will be helpful on assignments, the project, and your future research.\nEach deliverable is due on the Friday following the TA-led session for that seminar\n\nAssignment\nDue Date\nSeminar 1\nFri 10 January 2025\nSeminar 2a & 2b\nFri 17 January 2025\nSeminar 3\nFri 24 January 2025\nSeminar 4\nFri 31 January 2025\nSeminar 5\nFri 07 February 2025\nSeminar 6\nFri 14 February 2025\nSeminar 7\nFri 28 February 2025\nSeminar 8\nFri 14 March 2025\nSeminar 9\nFri 21 March 2025\nSeminar 10\nFri 28 March 2025\nSeminar 11\nFri 11 April 2025\n\nPaper critique (5%)\nSee here for detailed instructions and rubric.\n\nAssignment\nDue Date\nPaper Critique\nThu 20 February 2025\n\nAnalysis assignment (20%)\nInvolves detailed analysis of real data using R\nThis assignment will assess your ability to understand and apply the methods learned in class\n\nAssignment\nDue Date\nAnalysis Assignment\nThu 06 March 2025\n\nGroup project (40%)\nA semester-long data analysis group project where you will apply the techniques covered in class to a research question of your choosing\nGroups of target size of 4 students will be formed at the beginning of the course\nImportant checkpoints during the term (with deliverables):\nInitial Proposal\nFinal Proposal\nProgress Report\nOral presentation\nWritten Report & GitHub repository\nIndividual and Group Evaluation\n\nFor more details on the project components and how groups are selected see the assignments page\nParticipation in group projects is assessed via a contribution multiplier - see the multiplier rubric for details.\nFor detailed grading rubrics of the final project components, see the final project rubric page and the final project presentation rubric\n\nAssignment\nDue Date\nInitial Proposal\nMon 27 January 2025\nFinal Proposal\nTue 11 February 2025\nProgress Report\nTue 11 March 2025\nFinal Report\nTue 01 April 2025\nPresentation Day 1\nTue 01 April 2025\nPresentation Day 2\nThu 03 April 2025\nPresentation Day 3\nTue 08 April 2025\nIndividual & Group Evaluation\nFri 11 April 2025\n\nAcademic Concession\nIf you anticipate having trouble meeting a deadline and need an academic concession, please reach out in advance via email to the instructors. Here is a template you can use for a self-declaration.\nIf you miss class, we suggest you to:\nConsult the class resources on the course website\nUse the class Piazza discussion board to discuss missed material\nCome to virtual office hours\nSeek academic concessions, if applicable\nAcademic Integrity\nNot only is academic integrity is essential to the successful functioning of our course, but adopting best practices will benefit you in your research practice. Make sure you understand UBC’s definitions of academic misconduct and its consequences. Policy guidelines can be found here.\nWhat is academic integrity?\nThe academic enterprise is founded on honesty, civility, and integrity. As members of this enterprise, all students are expected to know, understand, and follow the codes of conduct regarding academic integrity. At the most basic level, this means submitting only original work done by you and acknowledging all sources of information or ideas and attributing them to others as required. This also means you should not cheat, copy, or mislead others about what is your work; nor should you help others to do the same. For example, it is prohibited to: share your past assignments and answers with other students; work with other students on an assignment when an instructor has not expressly given permission; or spread information through word of mouth, social media, websites, or other channels that subverts the fair evaluation of a class exercise, or assessment.\nWhat does academic integrity look like in this course?\nDo your own work. All individual work that you submit should be completed by you and submitted by you. Do not receive or share completed coursework with students who take the course in another term.\nAcknowledge others’ ideas. Scholars build on the work of others, and give credit accordingly. You are expected to acknowledge the use of any outside sources (e.g. from the literature, or AI tools) or inside sources (e.g from your peers).\nLearn to avoid unintentional plagiarism. Visit the Learning Commons’ guide to academic integrity to help you organize your writing as well as understand how to prevent unintentional plagiarism.\nAt any time: if you are unsure if a certain type of assistance is authorized, please ask us.\nUse of generative artificial intelligence (AI) tools\nTools that use artificial intelligence algorithms trained large dataset of training data to generate content, such as ChatGPT, have become widely accessible (these are often referred to as “generative AI tools”). If you choose to use generative AI tools to complete coursework, you must disclose your use of them. This disclosure must be included at the top of the submission file for the assignment in which the generative AI tool was used. The disclosure should include the name of the tool and a brief description of how it was used.\nFurther, if you choose to use generative AI tools for coursework, you should be aware of the following:\nPrivacy implications: Information you input into some tools, along with some device data such as your IP address, may be sent to servers outside of Canada, which may put your intellectual property and/or personal information at risk. For more information, please refer to UBC’s Privacy Impact Assessment for generative AI tools.\nFalse or misleading information: Generative AI tools can produce biased, false or misleading content, because of the nature of the training datasets. They are designed to produce the most statistically plausible text result, not necessarily the correct one, and they cannot take responsibility for what they produce. You, on the other hand, must take responsibility for the accuracy and integrity of your written work: therefore, you should not consider the output of a generative AI tool to be reliable unless verified by information in scholarly sources, and the information included in your assignments must be found in and attributed to scholarly sources.\nUltimately, this course is designed to help students practice analytical skills, and investigate a well-reasoned scientific question. Using generative AI tools to produce entire written and coding assignments will stifle independent thinking and undermine development of these valuable skills. If you have questions around the acceptable or unacceptable use of generative AI tools, we encourage you to speak to the instructors.\nWhat happens when academic integrity is breached?\nViolations of academic integrity (i.e., misconduct) lead to the breakdown of the academic enterprise, and therefore serious consequences arise and harsh sanctions are imposed. For example, incidences of plagiarism or cheating may result in a mark of zero on the assignment and more serious consequences may apply if the matter is referred for consideration for academic discipline. Careful records are kept to monitor and prevent recurrences. Any instance of cheating or taking credit for someone else’s work, whether intentionally or unintentionally, can and often will result in at minimum a grade of zero for the assignment, and these cases will be reported to the Head of the Department of Statistics and Associate Dean Students of the Faculty of Science.\nPrivacy\nThis course requires students to work on github.com. Please be advised that the material and information you put on GitHub will be stored on servers outside of Canada. Data used for these tools may not be protected, as they have not gone through a Privacy Impact Assessment (PIA) and been identified as FIPPA compliant. When you access GitHub, you will be required to create an account. While this tool has a privacy policy, UBC cannot guarantee security of your private details on servers outside of Canada. Please exercise caution whenever providing personal information. Please feel free to contact UBC (access.and.privacy@ubc.ca) or the support team for GitHub if you have any questions.\nLearning Environment & Support\nWe strive to provide a learning environment where all students can succeed. Please join me in contributing to a classroom culture where everyone feels valued. If you encounter an issue that presents a barrier to your learning, please reach out to us. You can also contact the Ombudsperson for help: https://ombudsoffice.ubc.ca. If you have a documented disability that affects your learning, you may contact the UBC Centre for Acessibility: https://students.ubc.ca/about-student-services/centre-for-accessibility, and contact us as soon as possible if you think you may require accommodation options for course work. Use of class time\nDiscussions during our scheduled class time are intended to relate to the learning outcomes for the course.\nYour mental health and wellbeing impacts your academic performance. Sometimes it is possible to manage challenges on your own, while other times you may need support. UBC is committed to providing student mental health and wellbeing resources, strategies, and services that help you achieve your goals. Visit https://students.ubc.ca/health for more information.\nHealth and Safety in the Classroom\nPlease follow the current UBC Communicable Disease Prevention Guidelines regarding self-monitoring, and staying home if you are sick. Although masks are no longer required on campus, please respect the choices of your fellow students and the instructional team who may continue to wear masks.\nExtreme Environmental Conditions Contingency Plan\nIn-person, on campus activities may need to be cancelled due to issues such as weather conditions (e.g., snow). The most up-to-date information about cancellations will be posted on ubc.ca. Please check ubc.ca often during times when an extreme weather event could disrupt our course activities. Here is what you can expect in the event an in-person lecture or lab session is cancelled:\nDepending on the nature of the planned in-class activities, class may take place over Zoom (in this case, Zoom link will be posted on Canvas), or an alternate activity may be posted on Canvas for you to complete before the next scheduled class. We will communicate via Canvas to announce the specifics for each case that arises as soon as we can.\n\n\n\n", - "last_modified": "2025-01-02T14:31:29-08:00" + "contents": "\n\nContents\nLand acknowledgement\nCourse-level learning objectives\nSelected topics\nTeaching Team\nSchedule\nCourse communication\nPrerequisites and Resources\nEvaluation\nAcademic Concession\nAcademic Integrity\nPrivacy\nLearning Environment & Support\nHealth and Safety in the Classroom\nExtreme Environmental Conditions Contingency Plan\n\n2023-2024 Winter Term 2 (January 8, 2024 - April 12, 2024)\nSTAT 540 is a 3 credit course with a mandatory computing seminar\nCross-listed as STAT 540, BIOF 540, GSAT 540\nLand acknowledgement\nWe respectfully recognize that the University of British Columbia Vancouver campus is located on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm (Musqueam) people. Please take a moment to learn about the territory you are occupying by visiting this interactive indigenous land map.\nCourse-level learning objectives\nPerform exploratory data analysis and visualize genomics data\nApply tailored statistical methods to answer questions using high dimensional biological data\nMake your work reproducible, reusable, and shareable\nWork with real data in a collaborative model\nSelected topics\nBasics of molecular genetics/genomic and data collection assays (methods)\nBasic probability and math foundations\nExploratory data analysis and data quality control\nNormalization, batch correction\nCausal inference and confounding effects\nBasic statistical inference (“one gene at a time”) – linear models\nLarge-scale inference (“genome-wide”) – multiple testing\nAnalysis of microarray, RNASeq, and epigenetics data\nPrincipal Component Analysis and clustering (unsupervised machine learning)\nClassification and cross validation (supervised machine learning)\nGene set analysis and gene networks\nGenome-wide association analysis (GWAS)\nSingle-cell genomics\nTeaching Team\nFor more info on the Teaching team, including brief bios, see the ‘People’ pages on this website (linked below). Links for connecting to recurring virtual office hours will be shared via Canvas.\nInstructors\nKeegan Korthauer, PhD (She/Her/Hers)\nEmail: keegan@stat.ubc.ca\nVirtual office hours: By appointment\nYongjin Park, PhD (He/Him/His)\nEmail: ypp@stat.ubc.ca\nVirtual office hours: By appointment\nTeaching Assistants\nAsfar Lathif (He/Him/His)\nEmail: asfar.lathif@ubc.ca\nVirtual office hours: Wed 11a-12p (PST)\nJJ Hum (They/Them/Theirs)\nEmail: jj.hum@ubc.ca\nVirtual office hours: TBA\nSchedule\nLectures (Sec 201)\nTime: Tues Thurs 9:00 - 10:30am\nLocation: Building and room number listed on Canvas\nSee Lectures for lecture materials and schedule\nSeminars (Sec S2B)\nTime: Tues 12:30pm - 1:30pm\nLocation: Building and room number listed on Canvas\nSee Seminars for schedule and seminar materials\nCourse communication\nAnnouncements\nCourse announcements will be posted on Canvas. You are responsible for checking it regularly.\nGeneral questions\nPiazza (in Canvas) for posting questions (e.g. topics discussed in class, questions about course organization, assignment clarifications, if you are stuck on an assignment and need help). This ensures the message can be seen by the entire teaching team, and that others in the class who might have the same question can learn from responses. You are also welcome to share your input on questions posted by others.\nPrivate matters\nFor private matters (e.g. requesting an extension, scheduling appointment for office hours), please contact the Teaching team by email (listed above). Do not use email to ask general questions described above.\nGroup work\nIn your final project groups, we expect you to (1) arrange regular meetings either in person or virtually and (2) make use of your team’s Piazza group.\nPrerequisites and Resources\nThis interdisciplinary course requires a broad range of skills at the interface of statistics, molecular biology / genomics, and computing. While you may have some background in one or more of the following areas, you are not expected to be an expert in all. That said, to be successful in the course, you may need to spend a bit more time in the areas that you have less experience in. Although there are no official prerequisites for the course, here is a list of useful skills to bring into the course and/or learn along the way.\nStatistics:\nYou should have already taken a university level introductory statistics course.\nThis free online book “Modern Statistics for Modern Biology” by Susan Holmes and Wolfgang Huber is a great reference for introduction or review of many of the statistical concepts that are relevant for this course.\nThis free online book “Data Analysis for the Life Sciences” by Rafael Irizarry and Michael Love is another great resource for introduction or review of many of the statistical concepts relevant in this course, with an emphasis on implementation in R.\nBiology:\nNo requirements, but you are expected to learn things like, e.g. the difference between DNA and RNA, and the difference between a gene and a genome.\nSee this video and this slideset for some basic introductory material.\nGo through the (optional, not for a grade) molecular biology quiz on Canvas\nThis free online book “Concepts of Biology” by Fowler, Roush & Wise is a great resource for biological concepts, in particular chapters 6 and 9\nThis free online book “Biology” by Clark, Douglas & Choi goes more in-depth, see Chapters 14, 15, and 16 for material on genetics that is particularly relevant for this course.\nNo matter your prior experience, when you come across a new biological concept during the course or in your research, you might need to spend a bit of time ‘learning as you go’ - this is expected and something I still do regularly in my day-to-day research!\nR:\nNo experience required but be prepared to do a lot of self-guided learning if you haven’t taken other courses on R or used it in your research.\nStart now by installing R and the HIGHLY RECOMMENDED “integrated development environment” (IDE) RStudio - both are free and open source.\nYou should be able to run R on your own computer during each seminar session.\nIf you are new to R, check out this blog post on getting started with R.\nThis free online book “Introduction to Data Science” by Rafael Irizarry is also a great resource for getting more in-depth with R, programming basics, and the tidyverse. In particular see Chapters 1-5:\nChapter 1: Getting Started with R and R Studio\nChapter 2: R Basics\nChapter 3: Programming Basics\nChapter 4: The tidyverse\nChapter 5: Importing data\n\nGit/GitHub and R Markdown:\nIn this course we’ll be using the version control software Git and its web-based hosting and collaborative platform GitHub.\nThe online resource “Happy Git and GitHub for the useR” from Jenny Bryan is a great reference for these tools as we learn them.\nAnother helpful git resource is Hadley Wickham’s webinar “Collaboration and time travel- version control with git, github and RStudio”\nWe’ll learn about using R markdown to generate readable and reproducible reports with code and text, and you’ll be using that a lot in this course - see Chapter 18 of the ‘Happy Git’ resource: “Test drive R markdown”.\nEvaluation\nYou will have three individual assignments, six seminar submissions (one divided into two parts), and one group project. Deadlines are all by 11:59 pm (Pacific time) on the due date. Any submission or modification after the due date will not be graded unless you have requested an extension. If you anticipate having trouble meeting a deadline and need to request an extension/academic concession please reach out in advance via email to the instructors.\nFor more detail on each of these assignments, see the assignments page (the header of each assignment on this page points to the relevant section of the assignments page). Also refer to this visual overview of the timeline.\nFor detailed instructions on how to work and submit assignments for this course, please see the Submission Guide.\nIntro Assignment (5%)\nAn introductory assignment designed to assess basic knowledge of GitHub, R and Rmarkdown\n\nAssignment\nDue Date\nIntro Assignment\nThu 23 January 2025\n\nSeminar completion (30%)\nYou will submit short “deliverables” for each seminar session\nEach seminar session is weighted equally, but the lowest score will be dropped (so that the 10 seminars with highest score will each count for 3% of the final grade).\nThese deliverables give practical experience applying the knowledge that will be helpful on assignments, the project, and your future research.\nEach deliverable is due on the Friday following the TA-led session for that seminar\n\nAssignment\nDue Date\nSeminar 1\nFri 10 January 2025\nSeminar 2a & 2b\nFri 17 January 2025\nSeminar 3\nFri 24 January 2025\nSeminar 4\nFri 31 January 2025\nSeminar 5\nFri 07 February 2025\nSeminar 6\nFri 14 February 2025\nSeminar 7\nFri 28 February 2025\nSeminar 8\nFri 14 March 2025\nSeminar 9\nFri 21 March 2025\nSeminar 10\nFri 28 March 2025\nSeminar 11\nFri 11 April 2025\n\nPaper critique (5%)\nSee here for detailed instructions and rubric.\n\nAssignment\nDue Date\nPaper Critique\nThu 20 February 2025\n\nAnalysis assignment (20%)\nInvolves detailed analysis of real data using R\nThis assignment will assess your ability to understand and apply the methods learned in class\n\nAssignment\nDue Date\nAnalysis Assignment\nThu 06 March 2025\n\nGroup project (40%)\nA semester-long data analysis group project where you will apply the techniques covered in class to a research question of your choosing\nGroups of target size of 4 students will be formed at the beginning of the course\nImportant checkpoints during the term (with deliverables):\nInitial Proposal\nFinal Proposal\nProgress Report\nOral presentation\nWritten Report & GitHub repository\nIndividual and Group Evaluation\n\nFor more details on the project components and how groups are selected see the assignments page\nParticipation in group projects is assessed via a contribution multiplier - see the multiplier rubric for details.\nFor detailed grading rubrics of the final project components, see the final project rubric page and the final project presentation rubric\n\nAssignment\nDue Date\nInitial Proposal\nMon 27 January 2025\nFinal Proposal\nTue 11 February 2025\nProgress Report\nTue 11 March 2025\nFinal Report\nTue 01 April 2025\nPresentation Day 1\nTue 01 April 2025\nPresentation Day 2\nThu 03 April 2025\nPresentation Day 3\nTue 08 April 2025\nIndividual & Group Evaluation\nFri 11 April 2025\n\nAcademic Concession\nIf you anticipate having trouble meeting a deadline and need an academic concession, please reach out in advance via email to the instructors. Here is a template you can use for a self-declaration.\nIf you miss class, we suggest you to:\nConsult the class resources on the course website\nUse the class Piazza discussion board to discuss missed material\nCome to virtual office hours\nSeek academic concessions, if applicable\nAcademic Integrity\nNot only is academic integrity is essential to the successful functioning of our course, but adopting best practices will benefit you in your research practice. Make sure you understand UBC’s definitions of academic misconduct and its consequences. Policy guidelines can be found here.\nWhat is academic integrity?\nThe academic enterprise is founded on honesty, civility, and integrity. As members of this enterprise, all students are expected to know, understand, and follow the codes of conduct regarding academic integrity. At the most basic level, this means submitting only original work done by you and acknowledging all sources of information or ideas and attributing them to others as required. This also means you should not cheat, copy, or mislead others about what is your work; nor should you help others to do the same. For example, it is prohibited to: share your past assignments and answers with other students; work with other students on an assignment when an instructor has not expressly given permission; or spread information through word of mouth, social media, websites, or other channels that subverts the fair evaluation of a class exercise, or assessment.\nWhat does academic integrity look like in this course?\nDo your own work. All individual work that you submit should be completed by you and submitted by you. Do not receive or share completed coursework with students who take the course in another term.\nAcknowledge others’ ideas. Scholars build on the work of others, and give credit accordingly. You are expected to acknowledge the use of any outside sources (e.g. from the literature, or AI tools) or inside sources (e.g from your peers).\nLearn to avoid unintentional plagiarism. Visit the Learning Commons’ guide to academic integrity to help you organize your writing as well as understand how to prevent unintentional plagiarism.\nAt any time: if you are unsure if a certain type of assistance is authorized, please ask us.\nUse of generative artificial intelligence (AI) tools\nTools that use artificial intelligence algorithms trained on large datasets to generate content, such as ChatGPT, have become widely accessible (these are often referred to as “generative AI tools”). If you choose to use generative AI tools to complete coursework, you must disclose your use of them. This disclosure must be included at the top of the submission file for the assignment in which the generative AI tool was used. The disclosure should include the name of the tool and a brief description of how it was used.\nFurther, if you choose to use generative AI tools for coursework, you should be aware of the following:\nPrivacy implications: Information you input into some tools, along with some device data such as your IP address, may be sent to servers outside of Canada, which may put your intellectual property and/or personal information at risk. For more information, please refer to UBC’s Privacy Impact Assessment for generative AI tools.\nFalse or misleading information: Generative AI tools can produce biased, false or misleading content, because of the nature of the training datasets. They are designed to produce the most statistically plausible text result, not necessarily the correct one, and they cannot take responsibility for what they produce. You, on the other hand, must take responsibility for the accuracy and integrity of your written work: therefore, you should not consider the output of a generative AI tool to be reliable unless verified by information in scholarly sources, and the information included in your assignments must be found in and attributed to scholarly sources.\nUltimately, this course is designed to help students practice analytical skills, and investigate a well-reasoned scientific question. Using generative AI tools to produce entire written and coding assignments will stifle independent thinking and undermine development of these valuable skills. If you have questions around the acceptable or unacceptable use of generative AI tools, we encourage you to speak to the instructors.\nWhat happens when academic integrity is breached?\nViolations of academic integrity (i.e., misconduct) lead to the breakdown of the academic enterprise, and therefore serious consequences arise and harsh sanctions are imposed. For example, incidences of plagiarism or cheating may result in a mark of zero on the assignment and more serious consequences may apply if the matter is referred for consideration for academic discipline. Careful records are kept to monitor and prevent recurrences. Any instance of cheating or taking credit for someone else’s work, whether intentionally or unintentionally, can and often will result in at minimum a grade of zero for the assignment, and these cases will be reported to the Head of the Department of Statistics and Associate Dean Students of the Faculty of Science.\nPrivacy\nThis course requires students to work on github.com. Please be advised that the material and information you put on GitHub will be stored on servers outside of Canada. Data used for these tools may not be protected, as they have not gone through a Privacy Impact Assessment (PIA) and been identified as FIPPA compliant. When you access GitHub, you will be required to create an account. While this tool has a privacy policy, UBC cannot guarantee security of your private details on servers outside of Canada. Please exercise caution whenever providing personal information. Please feel free to contact UBC (access.and.privacy@ubc.ca) or the support team for GitHub if you have any questions.\nLearning Environment & Support\nWe strive to provide a learning environment where all students can succeed. Please join me in contributing to a classroom culture where everyone feels valued. If you encounter an issue that presents a barrier to your learning, please reach out to us. You can also contact the Ombudsperson for help: https://ombudsoffice.ubc.ca. If you have a documented disability that affects your learning, you may contact the UBC Centre for Acessibility: https://students.ubc.ca/about-student-services/centre-for-accessibility, and contact us as soon as possible if you think you may require accommodation options for course work. Use of class time\nDiscussions during our scheduled class time are intended to relate to the learning outcomes for the course.\nYour mental health and wellbeing impacts your academic performance. Sometimes it is possible to manage challenges on your own, while other times you may need support. UBC is committed to providing student mental health and wellbeing resources, strategies, and services that help you achieve your goals. Visit https://students.ubc.ca/health for more information.\nHealth and Safety in the Classroom\nPlease follow the current UBC Communicable Disease Prevention Guidelines regarding self-monitoring, and staying home if you are sick. Although masks are no longer required on campus, please respect the choices of your fellow students and the instructional team who may continue to wear masks.\nExtreme Environmental Conditions Contingency Plan\nIn-person, on campus activities may need to be cancelled due to issues such as weather conditions (e.g., snow). The most up-to-date information about cancellations will be posted on ubc.ca. Please check ubc.ca often during times when an extreme weather event could disrupt our course activities. Here is what you can expect in the event an in-person lecture or lab session is cancelled:\nDepending on the nature of the planned in-class activities, class may take place over Zoom (in this case, Zoom link will be posted on Canvas), or an alternate activity may be posted on Canvas for you to complete before the next scheduled class. We will communicate via Canvas to announce the specifics for each case that arises as soon as we can.\n\n\n\n", + "last_modified": "2025-01-03T08:38:37-08:00" }, { "path": "timeline.html", diff --git a/syllabus.Rmd b/syllabus.Rmd index 57a2bcc..981c458 100644 --- a/syllabus.Rmd +++ b/syllabus.Rmd @@ -61,7 +61,7 @@ Virtual office hours: By appointment Email: Virtual office hours: Wed 11a-12p (PST) -* [JJ Hum](about_JJ.html) +* [JJ Hum](about_JJ.html) (They/Them/Theirs) Email: Virtual office hours: TBA diff --git a/syllabus.html b/syllabus.html index 5b90931..33f069c 100644 --- a/syllabus.html +++ b/syllabus.html @@ -2603,7 +2603,7 @@

Teaching Assistants

  • Asfar Lathif (He/Him/His)
    Email:
    Virtual office hours: Wed 11a-12p (PST)

    • -

  • JJ Hum
    +

  • JJ Hum (They/Them/Theirs)
    Email:
    Virtual office hours: TBA