Current Course Syllabus

The primary reference of this course is Data Science for Psychologists (ebook freely available at https://bookdown.org/hneth/ds4psy/).

This document only provides some additional details that concern the corresponding course at the University of Konstanz in Summer 2020.

Coordinates

spds.uni.kn

Dates and Contents

ds4psy

Part 0: Prepare

  1. 20.04.2020: Introduction

  2. 27.04.2020: Chapter 1: Basic R concepts and commands

Part 1: Explore

  1. 04.05.2020: Chapter 2: Visualizing data

  2. 11.05.2020: Chapter 3: Transforming data

  3. 18.05.2020: Chapter 4: Exploring data

Part 2: Wrangle

  1. 25.05.2020: Chapter 5: Tibbles

  1. 08.06.2020: Chapter 7: Tidying data

  2. 15.06.2020: Chapter 8: Joining data

  3. 22.06.2020: Chapter 9: Text data

  4. 29.06.2020: Chapter 10: Time data

Part 3: Program

  1. 06.07.2020: Chapter 11: Functions

  2. 13.07.2020: Chapter 12: Iteration

Orientation

The following diagram provides a schematic overview of the parts, topics, and corresponding chapters in our course curriculum. Whereas the colors of the horizontal lines signals which sessions belong to different parts, the height of each bar reflects the relevance of each chapter within this course:

Relevance of chapters and topics, with colored lines indicating different parts.

Relevance of chapters and topics, with colored lines indicating different parts.

As you can see, the first four chapters are particularly important. And as the contents of later chapters build and depend on earlier ones, please make sure that you really make a solid start in your endeavours.

Grading

As the Section Assessment in the Introduction notes, your final grade is a function of two parts:

A. Submitting solutions to weekly exercises on Ilias by Thursday of the same week (by 23:59) on at least 10 out of 12 weeks.

B. Final data science project: See Appendix C for guidelines and scope.

Final grades are based on course participation (including regular submission of exercises) (A: 33%) and the final exam/project (B: 67%).

A. Weekly Exercises

To make this as easy as possible for everyone involved, here are the rules for this semester (summer 2020):

  1. Overall, there are 11 weeks with content chapters and corresponding exercises left in this semester.

  2. To obtain a grade for exercises, you need to pass the exercises in 8 or more of those 11 weeks (i.e., in about 3 of every 4 weeks).

  3. Your solutions to each week’s exercises are scored as either passed (1) or failed (0).

  4. To pass a week’s exercises, you need to complete at least 50% of the exercises (e.g., 4 of the 8 Exercises on Chapter 1) and submit them on the corresponding folder on Ilias by Thursday, 23:59, of the corresponding week.

  5. A student will be notified (by the course tutor) if and only if s/he submitted and failed an exercise (0). This implies two special cases:

    • If you did not upload your solutions by the deadline, they will be scored as failed (0), but you will not be notified.
    • If you submitted your solutions and passed (1), you will not be notified.

Thus, you passed an exercise unless you did not submit anything or received a notification.

Our course tutor, Lisa Fleuchaus (lisa.fleuchaus@uni.kn), kindly offered to try to check the exercises and notify you by Monday after each submission deadline. But as this depends on many factors, there is no guarantee for this. Ideally, you always submit exercises and never hear from her — which means that you always passed.

The number of passed exercises are transferred into your grade for Part A by a complex, multi-factorial rule:

  • If you pass the weekly exercises of fewer than 8 of 11 weeks, you fail this course.

  • If you pass the minimum of 8 weekly exercises, you get a grade of 1.3 in this part.

  • If you pass 9 or more weekly exercises, you get a grade of 1.0 in this part.

In other words: To pass this course, you need to work regularly and submit weekly exercises on most weeks. If you fail to do so, you will not learn the material and not pass this course. But if you do the work, it pays off and you are rewarded by a very good grade.

B. Data Science Project

Your final data science project should not worry you at this point, as it is to be discussed with the tutor and/or instructor no later than a few weeks before the end of the semester. (Final projects can be thesis-related; contents to be discussed with instructor.) See Appendix C for guidelines and scope.

The deadline for completing and submitting final projects is 2020-08-01.

Contact

Please ask all questions that may also be of interest to other course members in the Discussion Forum on Ilias.

For all other questions, contact the course tutor Lisa Fleuchaus (lisa.fleuchaus@uni.kn) or the instructor Hansjörg Neth (h.neth@uni.kn).

References

ds4psy

The textbook of this course is:

  • Neth, H. (2020). ds4psy: Data Science for Psychologists.
    Social Psychology and Decision Sciences, University of Konstanz, Germany.
    Textbook and R package (version 0.2.0, April 20, 2020).
    Retrieved from https://bookdown.org/hneth/ds4psy/.

The URL of the R package ds4psy is https://CRAN.R-project.org/package=ds4psy.

ds4psy

[This file last updated on 2020-05-02 by hn.]

  1. During the Covid-19 pandemic, the organisational details of this course at the University of Konstanz are managed via Ilias. However, all course materials remain available at https://bookdown.org/hneth/ds4psy/ and are free to use for everyone interested.↩