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 Outcomes (CLOs)

CLO Description Teaching Methods
1 Have a solid conceptual grasp on the big data for business Lecture , Case studies , Exercises
2 Be able to apply an appropriate method to get knowledge from big data of the business Lecture, Case Studies, Exercises
3 Demonstrate the ability to clearly communicate the results of applying selected statistical learning methods to the data Lecture, Case Studies, Exercises, Hands-on lab exercises
4 Have a working knowledge of software in order to apply data analytics Lecture, Case Studies, Exercises

Program Learning Outcomes (PLOs)

PLO Description CL01 CLO2 CLO3 CLO4
1 Able to identify opportunities and generate business ideas at national and international levels by applying digital technology or various knowledge domains. ✅ ✅
2 Able to test business ideas, create innovative prototypes, and possess knowledge and understanding in starting a business.
3 Capable of analyzing theories and principles of business-related laws and able to analyze and plan organizational management and business operations using digital technology. ✅ ✅ ✅
4 Able to analyze data and utilize the results for business benefits. ✅ ✅ ✅
5 Possesses the qualities of a good entrepreneur, capable of lifelong learning in the context of technological, economic, and social changes. Possesses communication and coordination skills both within and outside the organization, in diverse cultural contexts, effectively.

Course Schedule

Class Date Day Content / Activity
1 23 Jun 2026 Tuesday Chapter 01: Overview of Big Data for Business + Class orientation
2 26 Jun 2026 Friday Chapter 02: Data
3 30 Jun 2026 Tuesday Chapter 03: Introduction to Big Data
4 3 Jul 2026 Friday Chapter 04: Business Problems and Data Science Solutions
5 7 Jul 2026 Tuesday Chapter 05: Big Data Technology
6 10 Jul 2026 Friday Chapter 06: Data Preparation (Required actions: Taught Content Clarification)
7 14 Jul 2026 Tuesday Chapter 06: Data Preparation — Practice / Activity
8 17 Jul 2026 Friday Chapter 07: Data Visualization
9 21 Jul 2026 Tuesday Chapter 07: Data Visualization — Practice
10 24 Jul 2026 Friday Chapter 07: Data Visualization — Business Dashboard Activity / Review
— 28 Jul 2026 Tuesday Holiday: HM King’s Birthday — No Class
11 31 Jul 2026 Friday No Class
12 4 Aug 2026 Tuesday Chapter 07: Data Visualization — Business Dashboard Activity / Review
13 7 Aug 2026 Friday Chapter 08: Basic Statistics and Probability
14 11 Aug 2026 Tuesday Chapter 08: Basic Statistics and Probability
15 14 Aug 2026 Friday Chapter 08: Basic Statistics and Probability — Practice
— 18 Aug 2026 Tuesday Reading Week / Midterm Preparation — No Teaching and No Exam
— 21 Aug 2026 Friday Reading Week / Midterm Preparation — No Teaching and No Exam
16 25 Aug 2026 Tuesday Midterm Examination
17 28 Aug 2026 Friday Midterm Examination / Midterm Assessment Week
18 1 Sep 2026 Tuesday Chapter 09: Introduction to Machine Learning
19 4 Sep 2026 Friday Chapter 10: Supervised Learning — Overview
20 8 Sep 2026 Tuesday Chapter 10: Regression — Simple Linear Regression (Brief Mention of Multiple Regression)
21 11 Sep 2026 Friday Chapter 10: Simple Linear Regression — Practice Session
22 15 Sep 2026 Tuesday Chapter 10: Classification — Decision Tree (Brief Mention of Logistic Regression)
23 18 Sep 2026 Friday Chapter 10: Decision Tree — Practice Session
24 22 Sep 2026 Tuesday Chapter 11: Unsupervised Learning — Overview
25 25 Sep 2026 Friday Chapter 11: Clustering — K-Means
26 29 Sep 2026 Tuesday Chapter 11: Clustering — Hierarchical Clustering
27 2 Oct 2026 Friday Chapter 11: Association — Association Rule
28 6 Oct 2026 Tuesday Chapter 11: Association — Association Rule Practice
29 9 Oct 2026 Friday Chapter 12: Text Mining
— 13 Oct 2026 Tuesday Holiday: HM King Bhumibol Adulyadej Memorial Day — No Class
30 16 Oct 2026 Friday Chapter 12: Text Mining / Final Review
— 22 Oct 2026 Thursday Final Examination, 15:30–18:30

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

  1. Laptop or desktop computer

  2. 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

  3. 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

  4. Grolemund, H. (2022). Welcome | R for Data Science. From https://r4ds.had.co.nz/

  5. Downey, A. B. (2011). Think stats. ” O’Reilly Media, Inc.”. https://greenteapress.com/wp/think-stats-2e/

  6. 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

Assessment Methods

Assessment Methods Percentages
Overview of Big Data for Business + Data + Big Data 5.00
Business Problems and Data Science Solutions + Basic Tools for Data Mining + Data Preparation 5.00
Data Visualization 5.00
Statistics and Probabilities 5.00
Introduction to Machine Learning 4.00
Supervised Learning 6.00
Unsupervised Learning 6.00
Text Mining 4.00
Attendance ถ.00
Participation(homework) 15.00
Midterm Exam 20.00
Final Exam 20.00
Total 100.00
ImportantHow to Calculate Class Attendance Score

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

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

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

Grade Calculation: criterion-Referenced

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

  1. The students must be in class for at least 80 percent of the course to be counted as present.

  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.