What is a typical week like?
|Do the readings||Syllabus|
|Help forum||Ed Discussion|
|ARC tutoring||Tutoring schedule|
|Pre-lecture quiz||U 18:00||Tests & Quizzes|
|Lecture meeting||M 08:30-11:00||Zoom (IB 2050)|
|ARC review session||M 19:30-21:30||More info|
|Office hours||T 08:30-09:30||Sign up here|
|Lab||T 10:00-11:15||Zoom (IB 1011/1012)|
|Pre-lecture quiz||T 18:00||Tests & Quizzes|
|Lecture meeting||W 08:30-11:00||Zoom (IB 2050)|
|Office hours||R 10:00-12:00||Sign up here|
|Datacamp assignment||F 18:00||Datacamp|
|End-of-week assignment||U 18:00||Assignments|
Green: Class sessions; Yellow: Assignments due; Grey: Optional support
All times are 24-hour Beijing times. R is Thursday and U is Sunday.
We will have two lectures each week. You are responsible for finishing the assigned readings ahead of lecture, and I expect you to attend and actively participate in each lecture unless you are sick (email me ahead of class).
- To ensure that you’ve done the readings and help me address common misunderstandings, you are expected to complete an online reading quiz on Sakai by 18:00 the day of each lecture. Completing the online reading quiz by the deadline will earn you full marks, regardless of your score. [5%]
- We will also have three closed-book, in-class reading quizzes. These quizzes will be similar in format and difficulty to the online reading quizzes, and only a bit longer. Your two best quizzes will count 5% each. [10%]
- Finally, I will grade your attendance and participation. Showing up on time, attending all classes unless you’re sick (and emailing me ahead of class if you are), and participating actively during class and on Ed Discussion will earn you a high mark. [5%]
We will have one lab each week where you will learn and practice hands-on statistical programming and report-making.
- I will grade your attendance and participation in the labs, as with the lectures. [5%]
- Each week, I will assign an asynchronous DataCamp lab assignment that will help you practice the concepts covered in lab. These are due Fridays at 18:00, and completing them by the deadline will earn you full marks. [10%]
You will practice applying concepts from the labs and lectures in a series of end-of-week projects that are due Sundays at 18:00:
- At the end of week 1, 2, 3, and 5, you will submit homework assignments. I will drop your lowest homework grade. [15% in total]
- At the end of week 4 and during finals week, you will submit midterm and final projects. [15% and 25%]
- At the end of week 6, you will submit a paper analysis. [10%]
How are these course components related?
|Formative assessment||Summative assessment|
|Online reading quizzes [5%]||In-class reading quizzes [10%]|
|Homework assignments [15%]
Datacamp labs [10%]
|Midterm and final projects [40%]|
|Paper analysis practice [P/F]||Paper analysis [10%]|
Attendance and participation [10%] constitutes the final component of your grade. The paper analysis practice exercise is graded pass/fail (timely submission and reasonable effort earns you a pass; failing will affect your participation grade).
Where can I get help with the course?
Please reach out if you think you are struggling with the material. I want to help you succeed in this course; the sooner you come by my office hours, the better. If you receive a grade below B— on any assignment, please schedule an appointment with me to discuss how to improve your work. You can sign up for my office hours here.
See the Resoures page for lots of excellent R and RStudio resources.
The fastest way to get your questions answered is to post them to the Ed Discussion forum on Sakai. The forum has sub-pages corresponding to each course theme and homework assignment. I will drop by and answer questions each work day, and I encourage you to help your fellow students whenever you’re able to as well.
You may not ask questions about the paper analysis or midterm/final projects on Ed Discussion. If you have clarifying questions, email them to me. If I answer them, I will email both the question and my answer to the whole class.
We’re very fortunate to be supported by a great team of peer tutors through the Academic Resource Center. Yitong Su and Colden Johnson will provide peer tutoring and lead a weekly study session. .
Collaborating with classmates
I encourage you to discuss the course material with your classmates. You may also work on your homework assignments in groups of two or three students as long as:
- All students contribute about equally (as opposed to one or two students freeriding off the other/-s);
- Each student writes their own homework assignment, with absolutely no copying and pasting of text or code;
- You clearly indicate in the header which student(s) you have collaborated with on each homework.
If you fail to follow the above policy, you will gain a score of zero on that homework. Most importantly, students who fail to follow the above policy tend to do poorly on the midterm and final projects, and thus get a low overall course grade.
While you are allowed to collaborate on the homework assignments, you are not allowed to discuss or collaborate on any aspect of your paper analysis or midterm/final project with other students under any circumstances. If you do, you will fail the course.
Please do the reading quizzes and Datacamp assignments individually. You will learn far more by doing them on your own, and you are not penalized for any wrong answers.
What other DKU resources are available?
Writing and language studio
The Writing and Language Studio (WLS) can help you improve your academic writing. You can register for an account, make an appointment, and learn more about WLS services, policies, and events on the WLS website. The WLS also provides writing and language learning resources on the Writing & Language Studio Sakai site.
General academic advising
Please stay in touch with your academic advisors about course performance issues (such as poor grades) and academic decisions (such as course changes, incompletes, and withdrawals) to ensure you stay on track with degree and graduation requirements.
In addition to your advisors, staff in the Academic Resource Center can provide recommendations on academic success strategies (such as tutoring, coaching, and student learning preferences). Please visit the Office of Undergraduate Advising website for additional information related to academic advising and student support services.
How will I be graded?
The individual components that determine your grade are listed above, along with their weight in your final grade score. I will multiply your component scores with the stated weights, and then convert your numerical grades to letter grades using the rubric below. I will round the overall weighted average of your grades up to the closest integer (but not the individual assignment scores).
|Exceptional||A+: 97+||A: 96–93||A–: 92–90|
|Superior||B+: 89–87||B: 86–83||B–: 82–80|
|Satisfactory||C+: 79–77||C: 76–73||C–: 72–70|
|Low pass||D+: 69–67||D: 66–63||D–: 62–60|
What are the course policies?
I’m glad you asked! See the course policies page. Failure to abide by the policies will impact your grade.