Text Analysis - Linguistics Meets Data Science

Teaching and Learning Methods

Let's start by making a very important note: Since the OER is online and does not actively involve a teacher, you will have to take full responsibility for your learning. This is not a bad thing per se, and many people have found that they thrive in situations where they learn by themselves and at their own pace. However, this also means that you will only get out what you put in. While there are quizzes and calls for reflection in the OER, they do not cover every aspect of what is discussed in the lessons and should not be seen as "controls" for whether you have studied enough or not. The quizzes are training tasks, and only you can assess when you know enough about a topic to proceed to a new one. Remember that returning to a previous lesson is always OK if something seems unclear!

A lot of the examples given are showcased in practice using KNIME. Since the emphasis of the OER is on you acquiring the know-how needed to implement the methods in practice, it is essential that you download KNIME and actively follow along with the examples. While it might initially seem silly to follow along and copy what is on the lesson page at home, this will build the familiarity needed to implement these methods on your own data and according to your variables later on. It is also a good idea to take notes while going through the examples, as this will leave you with a "cookbook" at the end of the OER and help you remember what was done and why.

Finally, we should acknowledge that the things we are going to learn during the OER are sometimes going to be difficult. Remember that it is completely OK if things do not stick the first time and that learning new things will always contain a certain level of trial and error. If something does turn out wrong, try to see it as a sign that you are breaking new ground and are actively acquiring something new!