Explanatory and predictive data analysis with multiple explanatory +variables. Choosing the right methods to apply based on the statistical +question and data at hand. Trade-offs between model-based and non-model +based approaches. Emphasis placed on case studies and real data sets, as +well as reproducible and transparent workflows when writing computer +scripts for analysis and reports.
+STAT 201 and one of MATH 100, MATH 102, MATH 104, MATH 110, MATH 120, +MATH 180, MATH 184, SCIE 001.
+Dr. Gabriela Cohen Freue (ESB 3146)
+Use CANVAS to send me an email. Please use email only for +personal matters that you would want to discuss privately.
Use office hours and Piazza Discussion Board for questions +regarding assignments, projects and class note examples etc.
The course is structured in weekly lectures and tutorials. The +lectures are mandatory and in +person. Tutorials are in person and a good +space to work collaboratively with peers on worksheets, tutorials and +project. This course will have plenty of synchronous activities that +students must work on during the lectures and tutorials. **Students are +expected to attend lectures and tutorials.
+The lectures will be expositive with the use of in-class activities. +Students will work on activities in Jupyter Notebooks . Students need to +bring a laptop to class to work on activities in Jupyter Notebooks and +iClickers. If a student does not have their own laptop or chromebook, +students may be able to loan +a laptop from the UBC library.
+Instructor: Thursdays, 11am – 12pm (Room TBD)
TAs office hours: Fridays, 1pm - 2pm (Online in Zoom)
Alternative times can be added by request
Midterm Break: February 17-21
If the university is closed due to extreme weather conditions or +other reasons, lectures will run via Zoom or recorded lectures will be +posted on CANVAS. Detailed information will be posted through CANVAS +announcements.
Textbooks are recommended for extra context and reference but are not +strictly required for the course.
+James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). +An introduction to statistical learning: With applications in R.
+Rafael Irizarry. Introduction to Data Science.
+Modern Dive: we used this book in STAT 201. This books also has +some useful material for this course, in particular:
+By the end of the course, students are expected to be able to:
+Describe real-world examples of explanatory modelling (e.g. A/B +testing optimization & regression with variable selection) and +predictive modelling problems.
Explain the trade-offs between model-based and non-model based +approaches, and describe situations where each might be the preferred +approach.
+Choose & apply a suitable method (e.g., regression, GLM’s, +sample size estimation, controlling for multiple testing, peeking, +bandit algorithms, variable selection, model diagnostics) based on the +statistical question and data at hand. Discuss the advantages and +disadvantages of different methods that may be suitable for a given +problem.
Correctly interpret computer output when performing the +statistical analyses presented in this course, in the context of the +statistical question being asked and the audience being reported +to.
Identify the assumptions / conditions required for each method to +produce reliable results. Choose techniques to check (or at least be +able to falsify) those assumptions. Discuss the consequence(s) of +mapping the wrong methods to the question and/or data type.
Most weeks there will be two assignments: (1) a worksheet; and (2) a +tutorial. Deadline TBD.
+The worksheets are fully autograded with visible tests to help you +identify points that need more clarification. Therefore, reach out to +the teaching team if you don’t understand why you are getting an answer +wrong in the worksheet. On the other hand, the tutorials are not fully +autograded.
+You can access the assignments through Canvas (assignments).
+Worksheets: 3%
Tutorials: 5%
Clicker Qs: 2%
Project: 20%
Midterm: 25%
Final Exam: 45%
Bonus Piazza: 1%
Team Work Contract: 1%
Individual Assignment 1 (Planning Stage): +5%
Individual Assignment 2 (Analysis Stage): +5%
Group Interview with TA: 2%
Group Final Report: 5%
Teammate Evaluation: 2%
Note: Please refer the Canvas Course page for +deadlines
+Worksheets are fully auto-graded with visible tests to help you +identify points that need more clarification.
Tutorials have only a few exercises will have visible +tests.
Make sure your work is saved on our server +(i.e., accessed using the link from Canvas) before the deadline. Our +server will automatically snapshot at the due date/time.
Please do not rename the assignments +files.
While you can work locally on your computer, only work saved on +the course’s server will be graded. Please, do not use other courses’ +servers (e.g., Syzygy).
We will be using iClicker Cloud in lectures. iClicker Cloud is a +response system that allows you to use your own computer or mobile +device to respond to questions posed by instructors during class. You +need to set up an iClicker Cloud account and add STAT 301 as a course to +this account. To do so, please follow https://lthub.ubc.ca/guides/iclicker-cloud-student-guide +for details.
+A project based on a case study that you will work on throughout the +term. Details about this assignment will be made available to you on +Canvas. You will work on this project both individually and in group. +You can (and are encouraged to) discuss all parts of the project with +your group members. However, every student will submit their own +individual assignments and will receive an individual grade on the +individual components.
+All the exams will be on Canvas with lockdown browser. You will be +able to have 1 letter-size page (double-sided) cheatsheet (all you can +write or print). You are not allowed to access any +webpage or files in your computer or other electronic devices.
+The types of questions can vary: reasoning, multiple-choice, +multiple-answer, dropdown, true or false. Although most questions will +be about the content, you can expect a few simple coding questions. That +being said, the coding question will not be overly complicated, and we +will only check your familiarity with the main functions and packages we +use in the course. We are not trying to test your +software development skills!!! Please don’t spend energy trying to +memorize everything. If you had done the worksheets and tutorial, this +should not be a problem for you.
+Midterm: administered during the full lecture of +Week 8. The midterm will cover Worksheets/Tutorials 1-5. Content in +readings are to help you get a full understanding but won’t be tested if +not covered in course material.
Final Exam: The final exam will be a two-hour +exam and it will cover the material of the entire +course. However, more emphasis will be given to the second part +of the course (i.e., Worksheets/Tutorials 6-10).
To have access Piazza, go to “Piazza” in the left menu in the Canvas +course page and it will open in a new window. Then you can sign up for +the class page. You can use “Piazza Discussion Board” to post your +questions and also to provide answers/hints to the questions posted +there. This is where you can discuss ideas, strategies, and resources +for solving the problems with your classmates.
+TAs will monitor the questions in the piazza page and post answers. +But do not expect we will answer all your questions posted in Piazza +page with detailed solutions. If you need more clarification, it’s +always better to contact TAs or me during our office hours.
+Do not provide full answer to questions in Piazza. The idea is to use +this platform to generate a fruitful discussion, give hints and tips, +but not full solutions. The same applies to other platforms (e.g., +Discord).
+Students that have answered the most statistics-related questions in +Piazza in a way that explains concepts well but does not reveal the +answer to an assignment, lab, or webwork question will get a bonus 1% +added to their grade. When you answer question, teaching team endorse +your answers as “good answer”. I add this 1% if you have more than 10 +Endorsed Answers.
+Students who miss the final exam must report to their faculty +advising office within 48 hours of the missed exam, and must apply for +deferred standing: https://students.ubc.ca/enrolment/academic-learning-resources/academic-advising. +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.
+If you’re a Science student, you must apply for deferred +standing (an academic concession) through Science Advising no later than +48 hours after the missed final exam/assignment. Learn more and find the +application online:https://science.ubc.ca/students/advising/concession.
+Students who are granted deferred standing write the final +exam/assignment at a later date.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.
+Many of the questions in assignments are graded automatically by +software. The grading computer has exactly the same hardware setup as +the server that students work on. No assignment, when completed, should +take longer than 5 minutes to run on the server. The autograder will +automatically stop (time out) for each student assignment after a +maximum of 5 minutes; any ungraded questions at that point will +receive a score of 0. Students are responsible for making sure +their assignments are reproducible, and run from beginning to +end on the autograding computer.
+Tip: when you’re done the assignment, click “Restart and Run +All”, and check that the autograder returns the results you are +expecting.
+If you have concerns about the way your work was graded, please open
+a request within one week of having the grade returned to you. After
+this one-week window, we may deny your request for re-evaluation. Also,
+please keep in mind that your grade may go up or down as a result of
+re-grading. To open a regrade requests, please follow the steps below:
+1. Go to Piazza and click on New post. 2. In
+Post Type, select Question. 3. Make the post
+private to instructors and TAs only. In Post to select
+Individual Students(s)/Instructor(s). A text box will
+appear, where you must type Instructors. 4. In
+Select Folder(s) select the folder regrading.
+5. In Summary say the Assignment you want to be regraded,
+followed by the question and your name and student number. For example,
+lab 3 -> Q3 -- Rodolfo Lourenzutti (9982313) 6. Provide
+a brief reason for why the regrade is needed. 7. The TAs will see the
+request and will take a look at the assignment. If necessary, they will
+involve the instructors. Finally, once the TA is finished reassessing
+the assignment: - If the grade deserves more marks: the TA will update
+the mark on Canvas and comment on the question so everyone can see that
+the question has been addressed. - If your grade goes down or stays the
+same: the TA will answer the post on Piazza, giving the student a reason
+for their final decision.
Students are responsible for using a device and browser compatible +with all functionality of Canvas. Lockdown browser will be used for +exams so students must address any issues with their laptop prior to the +exam. We will test it with a mock quiz before the exam in the review +session. Chrome or Firefox browsers are recommended; Safari has had +issues with Canvas quizzes in the past.
+Please see UBC’s +concession policy for detailed information on dealing with missed +coursework, quizzes, and exams under circumstances of an acute and +unanticipated nature.
+Discussion of ideas leaned in class is encourage (with other +students, TAs or the instructor). This helps the leaning process. But +individual work turned in by each student should be your own work.
+The 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.
+Violations 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 or +exam and more serious consequences may apply if the matter is referred +to the President’s Advisory Committee on Student Discipline. Careful +records are kept in order to monitor and prevent recurrences.
+A more detailed description of academic integrity, including the +University’s policies and procedures, may be found in the Academic +Calendar.
+Students must correctly cite any code or text that has been authored +by someone else or by the student themselves for other assignments. +Cases of plagiarism may include, but are not limited to:
+An “adequate acknowledgement” requires a detailed identification of +the (parts of the) code or text reused and a full citation of the +original source code that has been reused.
+The above attribution policy applies only to assignments. No +code or text may be copied (with or without attribution) from any source +during a quiz or exam. Answers must always be in your own +words. At a minimum, copying will result in a grade of 0 for the related +question.
+Repeated plagiarism of any form could result in larger penalties, +including failure of the course.
+We acknowledge that the UBC Vancouver campus is situated within the +traditional, ancestral and unceded territory of the Musqueam First +Nation.
+| Deliverable | -Weight | -
|---|---|
| Worksheets | -5% | -
| Tutorials | -10% | -
| In-class clicker Qs | -2% | -
| Project | -28% | -
| Midterm | -25% | -
| Final Exam | -30% | -
| Bonus Piazza | -1% | -
| Deliverable | -Weight | -
|---|---|
| Team Work Contract | -1% | -
| Individual Assignment 1 | -4% | -
| Individual Assignment 2 | -4% | -
| Individual Assignment 3 | -4% | -
| Group Interview with TA | -4% | -
| Individual Assignment 4 | -4% | -
| Group Final Report | -5% | -
| Teammate Evaluation | -2% | -