Syllabus and Course Policy

Syllabus

PDF version

Course Information

Lecturer: Yu-You Liou Contact: d10627008@ntu.edu.tw
Course Code: EIB-301-01-A1 Website: https://yyliou.github.io/da
Meeting Time: Wednesday, Periods 8–9 (15:10–17:00) Location: A207

Course Objectives

  1. To enable students to understand the basic logic, applications, and simple techniques of data analysis using R.

  2. To develop students’ ability to build clear and informative data visualizations grounded in the grammar of graphics.

  3. To communicate data-driven findings clearly to both technical and non-technical audiences.

  4. To integrate generative AI to enhance students’ workflows in programming and compilation tasks.

Textbooks

  1. Wickham, H., Navarro, D., & Pedersen, T. L. ggplot2: Elegant Graphics for Data Analysis (3rd ed.). (Main) https://ggplot2-book.org

  2. Adler, J. R in a Nutshell: A Desktop Quick Reference (2nd ed.). O’Reilly Media.

Course Structure

  1. Lecture (First hour): Foundational knowledge and concepts introduced each session.

  2. Lab (Second hour): Hands-on in-class exercises using R to apply the concepts learned during the lecture.

Assessment Criteria

Component Weight Details
In-Class Exercises 36% 12 exercises × 3% each
Midterm Exam 32%
Final Exam 32%

Regulations

  1. Attendance

Per Shih Chien University regulations, students who are absent for more than one-third of class sessions will receive a final grade of zero. Make-up attendance will not be permitted.

  1. In-class exercise deadline

The deadline for each in-class exercise is 23:59 (UTC+8) on the day before the following week’s class. Late submissions will not be accepted.

  1. Academic Integrity

All submitted work must be the student’s own. Copying from other students is strictly prohibited. Every submission carries a unique identifier generated by the official template, and identical identifiers across submissions are treated as evidence of copying; any work found to be copied will receive a grade of zero for all students involved.

  1. AI Policy

Students may use AI tools (e.g., ChatGPT, Gemini, GitHub Copilot) as learning aids for in-class exercises, and doing so is not itself a violation. What is required is that you can explain and defend any code or analysis you submit. Submitting AI-generated work you do not understand constitutes academic dishonesty. AI tools are not permitted during examinations.

Exam Regulations

Both the mid-term and the final examination are answered online, on your own laptop, in the classroom. The following regulations apply to both.

  1. Mandatory Attendance

Students must take the exam in person. Roll call will be taken five minutes before the exam begins. Late arrival or absence will result in a score of zero.

  1. Network Restriction

The exam system tracks IP addresses. You must use the campus Wi-Fi. Access from a non-campus IP address will result in a score of zero.

  1. One Device Only

The exam must be taken on a single device. Keeping any additional device, such as a phone or a tablet, available during the exam will result in a score of zero.

  1. Authorized Resources

This is an open-book exam. Printed reference materials are permitted without limit. Electronic reference materials and AI tools, such as ChatGPT or Gemini, are prohibited. Any use will result in a score of zero.

  1. Media Prohibition

Photography, video or audio recording, and screenshotting are strictly prohibited. Violators will receive a score of zero.

  1. No Communication or Disturbance

Interpersonal communication, causing noise or disruptive movements are strictly prohibited. Violators will receive a score of zero.

  1. Copy-Paste Disabled

The exam system prohibits copying and pasting. Do not attempt to bypass this feature.

Required Software

All software is free and open-source: R at https://cran.r-project.org and RStudio Desktop at https://posit.co/download/rstudio-desktop. The R packages needed for the course, including the ones every submission depends on, install with a single command: install.packages(c("tidyverse", "patchwork", "uuid", "rmarkdown", "knitr", "tinytex")), followed by tinytex::install_tinytex() for PDF output. Step-by-step instructions are on the course R page.

Course Schedule

  1. Weeks 1–2: Part I — Introduction to R and first steps with ggplot2.

  2. Weeks 3–7: Part II — Layers (individual geoms, collective geoms, statistical summaries, maps, networks, annotations, arranging plots).

  3. Weeks 8–9: Mid-Term Examination (online; the exact date will be announced on the course website).

  4. Weeks 10–11: Part III — Scales (position scales, colour scales, other aesthetics).

  5. Weeks 12–14: Part IV — The grammar; advanced topics.

  6. Weeks 15–16: Final Examination (online; the exact date will be announced on the course website).

  7. Weeks 17–18: Flexible learning weeks.

Note: Syllabus is subject to change. Any updates will be announced on the course website.