ENC2036 Frequently Asked Questions
Q: Are there any prerequisites for the course?
A: This course assumes students to have at least some experiences with coding, preferrably with data processing experiences with the language . In other words, a certain level of R literacy is expected. Students are expected to have completed at least one of the following prerequisite courses, or their equivalents:
- ENC2053 Quantitative Methods and R Programming
- ENC2055 Introduction to Programming Languages for Linguistic Analysis
Students who have not completed any of the prerequisite courses may still consider taking this course if they are determined to work on Corpus Linguistics. However, they should understand that they are facing a steeper learning curve because they need to self-learn the basics of R programming quite a bit. Although we will have a quick comprehensive reivew of R basics, we will not cover every detail as in the previously mentioned introductory courses. Generally, we will probably spend about one month reviewing the necessary knowledge of R. Please take these into account when you make the decision.
Q: I have some experiences with other programming languages (e.g., Python, Perl etc.). Will this course still be suitable for me?
A: Yes. The constructs and principles we talk about in this course can be extended to other programming languages. In this course, you can choose to implement the same analyses in your own preferred language.
Q: Is the course open to auditors?
A: No, it is not.
Q: Is the course open to non-NTNU students?
A: Yes, as long as you are entitled to register for this course (including via Inter-University Course Selection).
Q: Do I need to prepare my own laptop?
A: Yes. Please bring your own laptop for the class. The course will be conducted in the format of weekly hands-on tutorials.
Q: Can I have the Internet access in the classroom?
A: Yes. The building provides Univeristy Wifi’s. For non-NTNU students, you can still access the Internet via your own University account via eduroam.
Q: Is a high-end laptop necessary?
A: All I can say is: the newer, the better.
Q: How do I submit my assignment?
A: All the assigments need to be submitted electronically via Moodle.
It is extremely important that students specify very clearly which questions each answer responds to. If you fail to follow this protocol we will not be able to properly grade the assignment.
Q: Do I have to do the coding in this class?
A: As stated in the Course Description, it it not our goal to introduce you to ready-made applications or software packages (e.g., Wordsmith, AntConc, Sketch Engine, LancBox, etc.). We hope to point you to the power of coding in computational text analytics. So the answer is yes. In each week, we will have hands-on tutorial sessesions on how to do basic text processing and analysis with , which has now been widely used by most corpus linguists.
Q: Can I use another programming language for the assignments?
A: Yes. You may use your own preferred language (e.g., Python) as long as your script produces the same outputs as requested by the assignments/exams.
Q: Are there any midterm and final exams?
A: Yes. We will have both a midterm and a final exam, where you are expected to respond to the questions by writing codes/scripts. They are going to be timed sessions. However, you may consult any (inanimate) reference that is available to you during the exam sessions, e.g., handouts, books, on-line forums/discussions. Anything really. But you cannot consult your peers no matter it is in personal communication or through social-network apps.
Q: Python seems more popular now. Why don’t we start with Python instead?
A: It is probably true that Python seems to be more widely used in many different communities. However, in the academic community, especially in corpus linguistics, R is probably more often used, to accomplish several computational text analytic tasks due to its powerful capabilities of statistical analyses and data visualizations. Most importantly, based on my own experiences (I started with Java years ago, considered switching to Python for a while, and ended up with R), R is probably a better entry language for those with little background of computing.
During the semester, I will also touch upon a couple of Python modules, which are more effective than R libraries. I need to stress that taking R as our featuring language does not imply that you cannot develop cross-language literacy. In connection to this, an adequate knowledge of one programming language should give you far more than enough for the future self-learning of other programming languages. Plus, it is a fact that most of the data scientists are proficient in more than one language. If you plan to continue/pursue this data-scientist career, not being able to extend your coding proficiency from R to other languages may be a sign, not in a good way though.
Please also refer to the following articles if you are still interested in the pros and cons of these two languages.
- R和Python學那個更好？從多角度了解為何R更好
- R vs. Python: What’s the difference?
- R vs. Python: Which is a better programming language for data science?
- From ‘R vs Python’ to ‘R and Python’
On a side note, the following short clip probably gives you some ideas on how different programming languages evolved in the past few decades.