Course Syllabus

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 Nov 12, 2024 Course introduction and software for study 1.5
1 Nov 15, 2024 Markdown language: quarto (1) 1.5
2 Nov 19, 2024 Markdown language: quarto (2) 1.5
2 Nov 22, 2024 Data structures and types: vector (1) 1.5
3 Nov 25, 2024 Data structures and types: vector (2) 1.5
3 Nov 29, 2024 Data structures and types : Matrix (1) 1.5
4 Dec 3, 2024 Data structures and types : Matrix (2) 1.5
4 Dec 6, 2024 Data structures and types : Data Frame (1) 1.5
5 Dec 10, 2024 Holiday -
5 Dec 13, 2024 Data structures and types : Data Frame (2) 1.5
6 Dec 17, 2024 Data structures and types : XTS (1) 1.5
6 Dec 20, 2024 Data wrangling (1) 1.5
7 Dec 24, 2024 Data wrangling (1) 1.5
7 Dec 27, 2024 NO class -
8 Reading week -
9 Midterm Exam -
10 Jan 14, 2025 No class 1.5
10 Jan 17, 2025 Based Plot (histogram and density) 1.5
11 Jan 21, 2025 Based Plot (Bar plot) 1.5
11 Jan 24, 2025 Based Plot (plot mathematics function) 1.5
12 Jan 28, 2025 Based Plot (Scatter plot) 1.5
12 Jan 31, 2025 Based Plot (Modifies plot) 1.5
13 Feb 4, 2025 Dashboard 1.5
13 Feb 7, 2025 ggplot2 (histogram and density) 1.5
14 Feb 11, 2025 ggplot2 (bar plot) 1.5
14 Feb 14, 2025 ggplot2 (scatter plot) 1.5
15 Feb 18, 2025 Popular packages (patchwork and plotly) 1.5
15 Feb 21, 2025 Financial Plot (1) 1.5
16 Feb 25, 2025 Financial Plot (2) 1.5
16 Feb 28, 2025 Programming (1) (extra) 1.5
17 Mar 4, 2025 Programming (2) (extra) 1.5
18 Mar 7, 2025 RevealJS (extra) 1.5
19-20 Mar 10-23, 2025 Final Exam -

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

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

  2. Kabacoff, Robert I. R in action: data analysis and graphics with R. Simon and Schuster, 2015.

  3. Wickham, Hadley, and Garrett Grolemund. R for data science: import, tidy, transform, visualize, and model data. ” O’Reilly Media, Inc.”, 2016.

  4. Xie, Yihui, Joseph J. Allaire, and Garrett Grolemund. R markdown: The definitive guide. Chapman and Hall/CRC, 2018.

5. Class Activities

Activity Evaluation Week Scores
Attendance 1-17 10
Project 1 before 7 10
Project 2 before 16 10
Project 3 before 16 10
Midterm exam 1/2 5 15
Midterm exam 2/2 9 15
Final exam 1/2 12 15
Final exam 2/2 19-20 15

6. Grade Evaluation

Grade Score range (PTS) GPA Value Comment
A 86-100 4 Excellent
B+ 80-85 3.5 Very good
B 74-79 3 Good
C+ 68-73 2.5 Above average
C 62-67 2 Average
D+ 56-61 1.5 Below average
D 50-55 1 Poor
F 0-49 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.