3 EC
Semester 2, period 4
5132DATE3Y
In this class, we will focus on using statistical models to learn from data. Rather than
memorizing many different types of tests and formulas, you will learn the fundamentals of
building statistical models and using models to understand data. Importantly, we will show
that dozens of different tests you may have heard of in statistics are just special cases of
general linear models. We will teach you to use this flexible framework to learn from
the many types of data you may come across in your research.
Above all else, the course is based on a philosophy to promote:
R and RStudio
Lectures: We will have 7 lectures (2 hs), generally one per week expect for week 1 with two lectures. During lectures, you will be encouraged to actively participate in discussions, ask questions, and participate in live polls. Lectures will be in-person only. Recordings of lectures will be posted at the end of the day and only available on canvas to watch for one week following each lecture.
Lab Practicals: We will have 4 computer practicals where you will work in self-selected groups of two. If you prefer to work alone that is also fine. Each pair will work on a problem set to get practical experience analyzing data in R. The goal of the lab practicals is for you to learn how to apply the theory covered in the lectures to analyze data in R. Course instructors will be present to assist groups with their work and answer questions. Attendance at lab practicals is mandatory (you must attend at least 3 out of 4 practicals to pass the course). The course content builds on itself as we progress, so attendance is required to ensure you understand core concepts for that week before moving onto the next topic.
Assignment: In weeks 10-11, groups of two will analyze a data set and write up a short report based on their analysis. This assignment will put into practice the theory covered in the class. There will be no opportunity for a resit on the assignment.
Self-study: It is expected you spend six hours per week on self-study. This involves reading and watching the assigned materials, reviewing course notes and lab practicals, attending question hours, and taking the practice exam.
|
Activity |
Hours |
|
|
Digital Test |
2 |
|
|
Lecture |
14 |
|
|
Labs |
8 |
|
|
Assignment |
8 |
|
|
Self study |
40 |
|
|
Total |
82 |
(3 EC x 28 hr) |
Additional requirements for this course:
The student may be absent in 1 out of 4 practicals, but the absence needs to be communicated to the group lecturer.
For this course, attendance in the computer labs is mandatory. During the workgroups, the learning objectives 1-7 are addressed. The guidance and exercises during the computer labs are essential for achieving these learning objectives. These learning objectives are assessed in the exam and graded report.
| Item and weight | Details |
|
Final grade | |
|
0.7 (70%) Tentamen digitaal | |
|
0.2 (20%) Statistical report | |
|
0.1 (10%) Lab exercises |
10% of your grade will be based on completing the 4 lab practicals. Your grade will be based on whether you followed instructions and thoughtfully attempted to answer every question on the practical. To get full credit on the lab assignments, they must be completed and submitted before the next week's lecture. Assignments turned in after this will only receive half credit. You must turn in all four lab practicals to pass the class.
20 % of your grade will be based on the quality of the Statistical report assignment. The report will be a more in-depth analysis of a complex real-world data set which you will have one week to work on in pairs. A grading rubric will be provided. The goal of the project is to get real-world experience analyzing and interpreting complex datasets. Thus the use of AI tools (e.g. ChatGPT) is not permitted.
70% of your grade will be determined by your score on the week 8 exam.
Students need a grade > 5.5 on the exam and a cumulative grade (exam, labs, report) > 5.5 to pass the course.
Every course goal will be assessed with an equal number of questions in the digital exam.
Students that have take 'From Analyisis to Evidence' last year can apply their grades for the labs and statistics report from the previous year. However, I highly recommend all students who are retaking the course to do all the labs and report again in order to master the material and pass the course.
Students will have the opportunity to inspect their assessed work at a date set after the work has been graded.
In weeks 10-11, groups of two will analyze a data set and write up a short report based on their analysis. This assignment will put into practice the theory covered in the class. The assignment will be graded.
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
Week 6 (Wednesday Lecture)
Week 6 (Thursday Lecture)
Week 7
Week 8
Week 9
Week 10
Week 11
Week 12
We vinden het belangrijk dat je je op de UvA en bij Future Planet Studies veilig voelt. Krijg je onverhoopt te maken met ongewenst gedrag of voel je je onveilig, dan kun je terecht bij verschillende personen. Je melding wordt altijd vertrouwelijk behandeld. Kijk op onze website voor meer informatie over waar en bij wie je terecht kunt.
It is important that everyone feels safe at the UvA and Future Planet Studies. We are committed to provide social safety and we offer various forms of support for people experiencing inappropriate or unsafe situations. Consult the UvA website or Future Planet Studies Canvas page for more information and contact info.
In order to provide students some insight how we use the feedback of student feedback to enhance the quality of education, we decided to include the table below in all course guides.
| Course Name (#EC) | N | |
| Strengths | Notes for improvement |
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| Response lecturer: |
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