ENC2055: Introduction to Programming Languages For Linguistic Analysis
Last Updated on: 2024-10-18
Annoucements
Important course information will be posted on this web page and announced in class. You are responsible for all material that appears here and should check this page for updates frequently.
- 2024-06-25: The next time this course will be offered is in the fall of 2024.
- 2024-06-25: Attendance in the first week is mandatory for those who wish to take the course.
- 2024-06-25: All students must attend the first class meeting, especially those who need a course enrollment code.
- 2024-06-25: This course is open to the undergraduates, as well as non-English majors. Please feel free to contact the instructor directly for course enrollment.
- 2024-06-25: For those who are considering taking this course, please read the FAQ first before you make the decision. Thank you very much.
Course Description
The objective of this course is to provide a comprehensive introduction to programming languages with a special focus on its application in linguistic analyses. This course is especially tailored to those who do not have any background or experiences in coding. We will start from the very basic concepts, such as data types, variable assignments, control structures, to more complex procedures such as routines, functions, and other exploratory project-based tasks. The course consists of a series of theme-based hands-on tutorials, which demonstrate how the flexibility of the programming language can help you become a more efficient and productive data scientist.
Specifically, this course will cover coding skills for basic text analytics, including R, Python and shell scripting basics, with the language R being our featuring programming language. We will introduce you to R, Rstudio, and a collection of R packages designed to work together to make linguistic analyses fast, fluent, and fun. Furthermore, we will show you how you can make use of the integrated RStuido for Python and shell scripting. By the end of the course, students should have a working knowledge of coding and an initial ability to advance a project independently as a data scientist.
Course Schedule
(This schedule is tentative and subject to change. Please pay attention to the announcements made during the class.)
| Week | Date | Topic |
|---|---|---|
| Week 1 | 2024-09-06 | Data Science and R |
| Week 2 | 2024-09-13 | R Fundamentals |
| Week 3 | 2024-09-20 | Code Format Convention, Subsetting |
| Week 4 | 2024-09-27 | Conditions and Loops |
| Week 5 | 2024-10-04 | Functions |
| Week 6 | 2024-10-11 | Data Visualization |
| Week 7 | 2024-10-18 | Data Manipulation |
| Week 8 | 2024-10-25 | String Manipulation |
| Week 9 | 2024-11-01 | Midterm Exam |
| Week 10 | 2024-11-08 | University Sports Day (No Meeting) |
| Week 11 | 2024-11-15 | Regular Expressions |
| Week 12 | 2024-11-22 | Regular Expressions |
| Week 13 | 2024-11-29 | Data IO and Iteration |
| Week 14 | 2024-12-06 | Data IO and Iteration |
| Week 15 | 2024-12-13 | Data Analysis and Text Analytics: A Primer |
| Week 16 | 2024-12-20 | Final Exam |
Course Requirement
Course Materials
All the course materials are available on the course website. Please consult the instructor for the direct link to the course materials. They will be provided as a series of online packets (i.e., handouts, script source codes etc.) on the course website. These teaching materials are based on the following recommended readings.
- R for Data Science
- Text Mining with R
- R Programming for Data Science
- Advanced R
- R Graphics Cookbook, 2nd Edition
Email Address
You can reach me at alvinchen@ntnu.edu.tw.
Office Hours
By appointment.
Questions?
For more information related to this course, please see the FAQ on our course website or write me at any time at: alvinchen@ntnu.edu.tw
Course Website
We have a course website. You may need a password to get access to the course materials. If you are an officially enrolled student, please ask the instructor for the access code.
Disclaimer & Agreement
While I have made every attempt to ensure that the information contained on the Website is correct, I am not responsible for any errors or omissions, or for the results obtained from the use of this information. All information on the Website is provided “as is”, with no guarantee of completeness, accuracy, timeliness or of the results obtained from the use of this information, and without warranty of any kind, express or implied.
You may print a copy of any part of this website for your personal or non-commercial use. Without the author’s prior written consent, you cannot disclose confidential information of the website (e.g., log-in username and password) to any third party.