Course Syllabus
Course Description:
Introduction to big data. Business problems and data science solutions. Basic tools for data mining. Predictive modelling. Clustering data. Decision analytic thinking. Visualizing model performance. Evidence and probabilities. Text mining.
Course Learning Objectives: By the end of the course a successful student should
Have a solid conceptual grasp on the big data for business
Be able to apply an appropriate method to get knowledge from big data of the business
Demonstrate the ability to clearly communicate the results of applying selected statistical learning methods to the data.
Have a working knowledge of software in order to apply data analytics
Course Schedule
| Week | Date | Topic |
|---|---|---|
| 1 | Tuesday, 24 June 2025 | Chapter 01: Overview of Big Data for Business |
| 1 | Friday 27 June 2025 | Chapter 02: Data |
| 2 | Tuesday, 1 July 2025 | Chapter 02: Data |
| 2 | Friday, 4 July 2025 | Chapter 03: Introduction to big data |
| 3 | Tuesday, 8 July 2025 | Chapter 04: Business problems and data science solutions |
| 3 | Friday, 11 July 2025 | Holliday: Buddhist Lent Day |
| 4 | Tuesday, 15 July 2025 | Chapter 05: Basic Tools for Data Mining |
| 4 | Friday, 18 July 2025 | Chapter 06: Data Preparation |
| 5 | Tuesday, 22 July 2025 | Chapter 06: Data Preparation |
| 5 | Friday, 25 uly 2025 | Chapter 06: Data Preparation |
| 6 | Tuesday, 29 July 2025 | Chapter 07: Data Visualization |
| 6 | Friday, 1 August 2025 | Chapter 07: Data Visualization |
| 7 | Tuesday, 5 August 2025 | Chapter 07: Data Visualization |
| 7 | Friday, 8 August 2025 | Chapter 09: Statistic and Probability |
| 8 | Tuesday, 12 August 2025 | Holliday: The Mother’s Day |
| 8 | Friday, 15 August 2025 | Chapter 09: Statistic and Probability |
| 9 | 18-24 August 2025 | Reading week |
| 10 | 25-31 August 2025 | Midterm examination |
| 11 | Tuesday, 2 September 2025 | Chapter 09: Intro to Machine Learning |
| 11 | Friday, 5 September 2025 | Chapter 10: Supervised Learning: Regression |
| 12 | Tuesday, 9 September 2025 | Chapter 10: Supervised Learning: Regression |
| 12 | Friday, 12 September 2025 | Chapter 10: Supervised Learning: Regression |
| 13 | Tuesday, 16 September 2025 | Chapter 10: Supervised Learning: Classification |
| 13 | Friday, 19 September 2025 | Chapter 10: Supervised Learning: Classification |
| 14 | Tuesday, 23 September 2025 | Chapter 10: Supervised Learning: Classification |
| 14 | Friday, 26 September 2025 | Chapter 11: Unsupervised Learning: Clustering |
| 15 | Tuesday, 30 September 2025 | Chapter 11: Unsupervised Learning: Clustering (K-means) |
| 15 | Friday, 3 October 2025 | Chapter 11: Unsupervised Learning: Clustering (Hierarchical Clustering) |
| 16 | Tuesday, 7 October 2025 | Chapter 11: Unsupervised Learning: Association Rule |
| 6 | Friday, 10 October 2025 | Chapter 12: Text mining |
| 17 | Tuesday, 14 October 2025 | Chapter 12: Text mining |
| 17 | Friday, 17 October 2025 | Summary information for final exam |
| 18 | 20 October - 2 November 2025 | Final examination |
Textbooks/Supplies/Materials/Equipment/ Technology or Technical Requirements:
Laptop or desktop computer
Introduction To Data Mining Using Orange | PDF | Cross Validation (Statistics) | Statistical Classification. (2022). From https://file.biolab.si/notes/2018-05-intro-to-datamining-notes.pdf
Agresti, A., & Franklin, C. (2007). The art and science of learning from data. Upper Saddle River, New Jersey, 88. From https://www.libs.uga.edu/reserves/docs/main-spring2017/lutz-stat6220/agresti%20&%20franklin%203e.pdf
Grolemund, H. (2022). Welcome | R for Data Science. From https://r4ds.had.co.nz/
Downey, A. B. (2011). Think stats. ” O’Reilly Media, Inc.”. https://greenteapress.com/wp/think-stats-2e/
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer. https://hastie.su.domains/ISLR2/ISLRv2_website.pdf
Grade Evaluation
| Grade | Score range (PTS) | GPA Value | Comment |
|---|---|---|---|
| A | 80-100 | 4 | Excellent |
| B+ | 75-79 | 3.5 | Very good |
| B | 70-74 | 3 | Good |
| C+ | 65-69 | 2.5 | Above average |
| C | 60-64 | 2 | Average |
| D+ | 55-59 | 1.5 | Below average |
| D | 50-54 | 1 | Poor |
| F | 0-49 | 0 | Fail |
Remarks
The students must be in class for at least 80 percent of the course to be counted as present.
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.
The students must read and follow the Chiang Mai University Regulations to ensure that you do not cheat in an exam.