Course Syllabus

Published

November 5, 2024

Modified

November 14, 2025

1. Course Description

Data structures and data types. Data wrangling. Data import and export. Types of data visualization. Programming for data visualization in business. Popular packages. Applications and case studies.

2. Course Learning Outcomes (CLO): Students are able to

  1. write programs and fix program errors.

  2. create reports for data visualization with software.

  3. analyze and visualize data in business.

3. Course Contents

Week Day Content Hour
1 Tue, Nov 18, 2025 Course introduction and software for study 1.5
1 Fri, Nov 21, 2025 Markdown language: quarto (1) (use computer) 1.5
2 Tue, Nov 25, 2025 Markdown language: quarto (2) (use computer) 1.5
2 Fri, Nov 28, 2025 Data structures and types: vector (1) 1.5
3 Tue, Dec 2, 2025 Data structures and types: vector (2) 1.5
3 Fri, Dec 5, 2025 Holiday (Father’s Day)
4 Tue, Dec 9, 2025 Data structures and types : Matrix (1) 1.5
4 Fri, Dec 12, 2025 Data structures and types : Matrix (2) 1.5
5 Tue, Dec 16, 2025 Data structures and types : Data Frame (1) 1.5
5 Fri, Dec 19, 2025 Data structures and types : Data Frame (2) 1.5
6 Tue, Dec 23, 2025 Data structures and types : XTS (1) 1.5
6 Fri, Dec 26, 2025 Data structures and types : XTS (2) 1.5
7 Tue, Dec 30, 2025 Holiday (New Year Festival)
7 Fri, Jan 2, 2026 Holiday (New Year) 1.5
8 Tue, Jan 6, 2026 Data wrangling (1) 1.5
8 Fri, Jan 9, 2026 Data wrangling (2) 1.5
9 Tue, Jan 13 2026 (no class) I am attending a conference in Vietnam this day.
9 Fri, Jan 16 2026 Based Plot (histogram and density)
10 Tue, Jan 20 , 2026 Reading week (no class)
10-11 Jan 23-29 2026 Midterm exam Topics: Data frame, XTS, Data wrangling 2
11 Fri, Jan 30, 2026 Based Plot (Bar plot) 1.5
12 Tue, Feb 3, 2026 Based Plot (plot mathematics function) 1.5
12 Fri, Feb 6, 2026 Based Plot (Scatter plot) 1.5
13 Tue, Feb 10, 2026 Based Plot (Modifies plot) 1.5
13 Fri, Feb 13, 2026 Dashboard (1) (use computer) 1.5
14 Tue, Feb 17, 2026 Dashboard (2) (use computer) 1.5
14 Fri, Feb 20, 2026 ggplot2 (histogram and density) 1.5
15 Tue, Feb 24, 2026 ggplot2 (bar plot) 1.5
15 Fri, Feb 27, 2026 ggplot2 (scatter plot) 1.5
16 Tue, Mar 3, 2026 Holiday
16 Fri, Mar 6, 2026 ggplot2 (Modified plot) 1.5
17 Tue, Mar 10, 2026 Popular packages (patchwork and plotly) 1.5
17 Fri, Mar 13, 2026 Financial Plot (1) 1.5
18 Tue, Mar 17, 2026 Financial Plot (2) 1.5
19 Fri, Mar 27, 2026 (8.00–11.00) The Final Exam Part 2/2 2
ImportantHow to Calculate Class Attendance Score

Let \(K\) be the total number of classes you are absent from.

\[ \text{Class Attendance Score (\%)} = \begin{cases} 10\%, & \text{if } K = 0, 1, \text{or } 2 \\ 12 - K\%, & \text{if } K = 3, 4, 5, \ldots, 12 \\ 0\%, & \text{if } K > 12 \quad \text{(Grade = F according to CMU regulations)} \end{cases} \]

Exceptions:
Absences will not be counted in the following cases:

  • Medical reasons with an official medical certificate

  • Participation in university-related activities with proper documentation

  • Other cases as deemed appropriate by the instructor

ImportantExam Details

This course includes 4 exams, and each exam is worth 15% of the total grade:
5% for multiple-choice questions and 10% for coding.

Exam 1 Topics:

  • Chapter 2: Quarto
  • Chapter 3: Vectors
  • Chapter 4: Matrices

Exam 2 Topics:

  • Chapter 5: Data Frames
  • Chapter 6: XTS
  • Chapter 7: Data Wrangling

Exam 3 Topics:

  • Base R plots:
    • Histogram
    • Mathematical plots
    • Bar plots
    • Scatter plots
    • Modified plots

Exam 4 Topics:

  • ggplot2:
    • Histogram
    • Bar plots
    • Scatter plots
    • Modified plots
    • Patchwork
    • Plotly

4. Textbooks/Supplies/Materials/Equipment/ Technology or Technical Requirements:

  • Laptop or desktop computer with R, Quarto and RStudio software.

  • Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media.

  • Peng, R. D. (2019). R programming for data science. Leanpub.

  • Matloff, N. (2011). The art of R programming: A tour of statistical software design. No Starch Press.

  • Kabacoff, R. I. (2015). R in action: Data analysis and graphics with R (2nd ed.). Manning.

  • Grolemund, G. (2014). Hands-on programming with R: Write your own functions and simulations. O’Reilly Media.

  • Gillespie, C., & Lovelace, R. (2016). Efficient R programming: A practical guide to smarter programming. O’Reilly Media.

  • Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686

  • Wickham, H. (2016). ggplot2: Elegant graphics for data analysis (2nd ed.). Springer. https://ggplot2.tidyverse.org

  • Ryan, J. A., & Ulrich, J. M. (2023). quantmod: Quantitative financial modelling framework. R package version 0.4.25. https://CRAN.R-project.org/package=quantmod

  • Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman & Hall/CRC. https://plotly-r.com

  • Pedersen, T. L. (2022). patchwork: The composer of plots. R package version 1.1.2. https://CRAN.R-project.org/package=patchworkhttps://CRAN.R-project.org/package=patchwork

  • Kunst, J. (2023). highcharter: A wrapper for the ‘Highcharts’ library. R package version 0.9.4.1. https://CRAN.R-project.org/package=highcharter

  • Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R markdown: The definitive guide. Chapman & Hall/CRC. https://rmarkdown.rstudio.com

  • Allaire, J. J., Xie, Y., Dervieux, C., McPherson, J., & Iannone, R. (2022). Quarto: Scientific and technical publishing system. https://quarto.org

5. Class Activities

Activity Evaluation Week Scores
Attendance 1-17 10
Project 1 before 7 10
Project 2 before 17 10
Project 3 before 18 10

Midterm exam 1/2

Topics: Quarto, vector, matrices

 5 15 (multiple choice 5, coding 10)

Midterm exam 2/2

Topics: Data frame, XTS, Data wrangling

 9 15 (multiple choice 5, coding 10)

Final exam 1/2

Topics: Based plot(histogram, barplot, curve, scatter plot, modified plot)

 13 15 (multiple choice 5, coding 10)

Final exam 2/2

Topics: ggplot2(histogram bar plot, scatter plot, modified plot, patchwork and plotly)

 19 15 (multiple choice 5, coding 10)

6. Grade Evaluation

Grade Score range (PTS) GPA Value Comment
A 80-100 4 Excellent
B+ 74-79 3.5 Very good
B 68-73 3 Good
C+ 61-67 2.5 Above average
C 55-60 2 Average
D+ 49-54 1.5 Below average
D 43-48 1 Poor
F 0-42 0 Fail

7. Remarks

  1. The students must be in class for at least 80 percent of the course to be counted as present. (If you are absent from my class more than 10 times, your grade is F)

  2. Cheating involves actual, intended, or attempted deception and/or dishonest action in relation to any academic work of the University. The consequence will be the award of a mark of zero for the module affected.

  3. The students must read and follow the Chiang Mai University Regulations to ensure that you do not cheat in an exam.