Course Evaluations – Seminars in Applied Economics: Happiness and Discrimination

Some favorite quotes (biased)

I took the course to take advantage of the opportunity to learn the structure of an academic paper as well as to learn R. I had limited previous R knowledge, however the use of Datacamp in the course was brilliant. All in all because of the course I was able to complete my thesis as it provided me with the structure of how to write a thesis as well as how to carry out data analysis in R. I encourage anyone with interest in data analysis to take advantage of this course.Ridaa (2019)

This course exceeded my expectations. I recommend that every undergraduate student attends this seminar. My personal goals were to learn to write empirical papers, work with data in the statistical software R and gain invaluable experience. Data Camp courses provided amazing first coding experience. A lot of work, challenges, but also the support of Marco and Writing Fellows. Inna (2020)

I think I’ll miss the seminar a little.Alessandra (2021)

Imagine a friend of yours is taking this seminar. What would you recommend him/her?

I would recommend doing the seminar to prepare for their thesis. Besides the academic writing practice and feedback, completing all the R courses on Datacamp really gives you solid R skills which can then be used for data analysis in your thesis or as an extra skill

I highly recommend the course to anyone since it will improve writing skills as well as coding.

This seminar requires a lot of patience and feels pretty long, but it's all worth it in the end. Prepare to dedicate a lot of time, especially at the beginning. The R-script assignments might seem hard and demand for great efforts, but it feels really good when you finally solve them. Following all the guidelines and announcements is crucial.

As soon as you start taking DataCamp courses (better before the first assignment), you will realize how easy the assignments start to seem. Do not start with the assignment on the last day, start earlier.

If you want to take this course you need a lot of patience and perseverance, plan a lot of time for this course, don't start too late to avoid stress.

I would recommend to take this course if you are interested in improving your skills in data analysis and in programming with R. This course is very interesting, but also quite intensive, so make sure to have enough time in both semester blocks to keep up with the workload.

The language of numbers

Are good grades easy to get?

Semester	# Participants	% Female	Ø Grade
2019/2020	9 (BA)	66 %	2,0
2020	11 (BA) / 4 (MA)	73 %	2,1
2020/2021	4 (BA) / 10 (MA) / 1 (PhD)	40 %

The average grades are based on students who completed the class and passed the examination. We need more information about all candidates, including those who started the course but failed the examination. We then could compare the so called mean grade of the total population.

Thus these average grades are not a good indicator for difficulty. Perhaps high difficulty and workload operates as a filter and only high performer remain in class.

Do I need previous knowledge in programming?

Most students rated their background knowledge in Economics and Statistics as “okay” (so called mode), academic writing experience as mixed and R experience as limited.

Still, considering the overall good final grades we may conclude that sound statistics background is more important than prior programming. In other words, students can learn R programming within the scope of a semester.

Can this course teach you something at all?

In 2020/2021 eleven students completed the seminar whereas about 23 registered at the beginning of winter term. The dropout was about 50%. Drop-out is a serious problem for academic courses as well as for panel studies (panel attrituion). People or students decide for various reasons to no longer participate in a survey or class. — Do you wonder how these 50% of students that passed the class are different from those 50% of students who dropped out?

I don’t dare to say that my class will be the reason why you master statistics, statistical thinking about causality or programming. Perhaps students who make it to the end are high motivated, have high ability or previous knowledge about statistics and programming. Perhaps as a student you only take statistics seminars if you like the challenge. This is called self-selection and might produce serious bias in social sciences.