Curriculum · Previous curriculum For students admitted 2019–2023

Bachelor of Science Program in Data Science (2562)

Bachelor of Science (Data Science) · B.S. (Data Science)

  • 131credits in total
  • 4years8 semesters
  • 3elective tracks
  • 75courses in the curriculum

Study plan

Other programs: Bachelor of Science Program in Data Science (2567)

Overview

The curriculum for students admitted from 2019 to 2023. Students admitted from 2024 follow the 2567 (2024) curriculum.

Elective tracks

  • Data analytics using mathematical modeling
  • Statisitical data analytics
  • Data analytics using computational modeling

Curriculum structure

At least 131 credits in total

  • General Education 30
  • Core Courses 21
  • Major 59
  • Minor (if any) 15
  • Free Electives 6

General Education

30 credits

  • Required Courses 24
  • GE Electives 6

Core Courses

21 credits

Major

59 credits

  • Compulsory Courses 32
  • Major Electives 27

Minor (if any)

15 credits

Free Electives

6 credits

Courses

Open a category to see its courses · credits (lecture-practice-self-study hours)

General Education30 credits

Required Courses 24 credits

Learner Person 15 credits

  • 001101Fundamental English 13(3-0-6)
  • 001102Fundamental English 23(3-0-6)
  • 001201Critical Reading and Effective Writing3(3-0-6)
  • 001225English in Science and Technology Context3(3-0-6)
  • 204100Information Technology and Modern Life3(3-0-6)

Innovative Co-creator 3 credits

  • 201190Critical Thinking, Problem Solving and Science Communication3(3-0-6)

Active Citizen 6 credits

  • 140104Citizenship3(3-0-6)
  • 201111The World of Science3(3-0-6)

GE Electives 6 credits

A student also chooses at least 6 credits from these 3 groups of GE courses.

Learner Person

  • 057137Sports for Health3(3-0-6)
  • 701181Accounting for Non Accountants3(3-0-6)
  • 702101Finance for Daily Life3(3-0-6)
  • 851100Introduction to Communication3(3-0-6)

Innovative Co-creator

  • 176100Law and Modern World3(3-0-6)
  • 201114Environmental Science in Today’s World3(3-0-6)
  • 602201Agro-Industrial Vision1(0-3-0)
  • 703103Introduction to Entrepreneurship and Business3(3-0-6)
  • 851103Life and Society through Media3(3-0-6)

Active Citizen

  • 951100Modern Life and Animation3(3-0-6)
Core Courses21 credits
  • 204251Data Structures3(3-0-6)
  • 206111Calculus 13(3-0-6)
  • 206112Calculus 23(3-0-6)
  • 206281Discrete Mathematics3(3-0-6)
  • 208150Probability and Statistics3(3-0-6)
  • 229123Fundamental Concept of Data Science3(3-0-6)
  • 229223Programming for Data Science3(2-2-5)
Major59 credits

Among the credits earned from major courses taken, a minimum of 36 credits must be from the advanced level courses (300-400), and of which at least 18 credits must be from the 400 level courses.

Compulsory Courses 32 credits

  • 204306Ethics for Computer Professionals1(1-0-2)
  • 204426Data Engineering3(2-2-5)
  • 204456Machine Learning3(3-0-6)
  • 206324Applied Linear Algebra3(3-0-6)
  • 208250Statistical Methods for Data Science3(2-2-5)
  • 229323Data Manipulation for Data Science3(2-2-5)
  • 229351Statistical Learning for Data Science 13(2-2-5)
  • 229352Statistical Learning for Data Science 23(2-2-5)
  • 229390Seminar in Data Science1(1-0-2)
  • 229452Research in Data Science3(3-0-6)
  • 229496Cooperative Education6

Major Electives 27 credits

Register courses from one of the following groups with 18 credits:

Group : Data analytics using mathematical modeling

  • 204381Numerical Computation and Software3(3-0-6)
  • 204471Artificial Intelligence3(3-0-6)
  • 206300Mathematics of Finance and Insurance3(3-0-6)
  • 206341Ordinary Differential Equations3(3-0-6)
  • 206465Principles of Mathematical Modeling3(3-0-6)
  • 206370Probability 13(3-0-6)

Group : Statisitical data analytics

  • 204423Data Mining3(3-0-6)
  • 204453Pattern Recognition3(2-2-5)
  • 208251Regression Analysis and Non-Parametric Statistics3(2-2-5)
  • 208350Applied Multivariate Statistics for Data Science3(3-0-6)
  • 208354Time Series Analysis for Data Science3(2-2-5)
  • 208451Analytics for Experimental and Simulated Data3(2-2-5)

Group : Data analytics using computational modeling

  • 204335Microcontroller and Internet of Things3(2-2-5)
  • or204371Feature Engineering3(2-2-5)
  • 204383Image Processing3(3-0-6)
  • 204422Data Warehousing3(2-2-5)
  • 204423Data Mining3(3-0-6)
  • 204453Pattern Recognition3(2-2-5)
  • 204471Artificial Intelligence3(3-0-6)

Select courses which are not duplicate courses from selected group from the following with at least 9 credits:

  • 204320Database Management3(2-2-5)
  • 204335Microcontroller and Internet of Things3(2-2-5)
  • 204371Feature Engineering3(2-2-5)
  • 204381Numerical Computation and Software3(3-0-6)
  • 204383Image Processing3(3-0-6)
  • 204422Data Warehousing3(2-2-5)
  • 204423Data Mining3(3-0-6)
  • 204453Pattern Recognition3(2-2-5)
  • 204471Artificial Intelligence3(3-0-6)
  • 204481Introduction to Mathematical Programming Techniques3(3-0-6)
  • 204482Simulation and Modelling3(3-0-6)
  • 204483Computer Vision3(3-0-6)
  • 206300Mathematics of Finance and Insurance3(3-0-6)
  • 206341Ordinary Differential Equations3(3-0-6)
  • 206442Partial Differential Equations3(3-0-6)
  • 206355Numerical Method3(3-0-6)
  • 206358Mathematics for Data Science3(3-0-6)
  • 206465Principles of Mathematical Modeling3(3-0-6)
  • 206370Probability 13(3-0-6)
  • 206441Nonlinear Differential Equations3(3-0-6)
  • 206457Mathematics of Financial Derivatives3(3-0-6)
  • 206467Neural Networks3(3-0-6)
  • 206470Probability 23(3-0-6)
  • 206471Introduction to Actuarial Mathematics3(3-0-6)
  • 208251Regression Analysis and Non-Parametric Statistics3(2-2-5)
  • 208350Applied Multivariate Statistics for Data Science3(3-0-6)
  • 208353Financial Statistics and Actuarial Science3(3-0-6)
  • 208354Time Series Analysis for Data Science3(2-2-5)
  • 208424Optimization for Statistical Learning3(3-0-6)
  • 208450Analytics for Observational Data3(3-0-6)
  • 208451Analytics for Experimental and Simulated Data3(2-2-5)
  • 208453Statistics for Finance and Investment3(3-0-6)
  • 208454Actuarial Modeling3(2-2-5)
  • 229471Data Analytics in Medicine3(2-2-5)
  • 261441Internet of Things and Big Data3(3-0-6)
  • 261461Visual and Interactive Programming3(3-0-6)
  • 261475Modeling and Optimization for Big Data Analytics3(3-0-6)
  • 954471Business Data Mining3(2-2-5)
  • 954472Business Data Visualization3(2-2-5)
Minor (if any)15 credits

Student who wishes to have minor is required to register at least 15 credits and may take courses choosing from minor in general Business Administration, Economics, any minor being offered by Science faculty except minor in Data Science, Mathematics, Computer Science, and Statistics. Furthermore, student may choose any minor courses corresponding to minor listed in the Chiang Mai University announcement about minor offered for CMU students with approval of an academic advisor. For those who do not wish to have a minor, it is required that a minimum of 15 credits from major elective courses at 300 or 400 level must be choosen.

Free Electives6 credits

At least 6 credits of elective courses, taken outside major and minor fields, are required.

Study plan

The recommended courses for each semester. Items in italics are courses the student chooses.

First Year

First Semester

  • 001101Fundamental English 13
  • 140104Citizenship3
  • 201111The World of Science3
  • 206111Calculus 13
  • 208150Probability and Statistics3
  • 229123Fundamental Concept of Data Science3

Total18 credits

Second Semester

  • 001102Fundamental English 23
  • 201190Critical Thinking, Problem Solving and Science Communication3
  • 204100Information Technology and Modern Life3
  • 206112Calculus 23
  • 229223Programming for Data Science3
  • General Education Electives3

Total18 credits

Second Year

First Semester

  • 001201Critical Reading and Effective Writing3
  • 206281Discrete Mathematics3
  • 208250Statistical Methods for Data Science3
  • 229323Data Manipulation for Data Science3
  • Minor (if any)3
  • Free Electives3

Total18 credits

Second Semester

  • 001225English in Science and Technology Context3
  • 204251Data Structures3
  • 206324Applied Linear Algebra3
  • 229351Statistical Learning for Data Science 13
  • Major Electives3
  • Minor (if any)3

Total18 credits

Third Year

First Semester

  • 204426Data Engineering3
  • 229352Statistical Learning for Data Science 23
  • Major Electives9
  • Minor (if any)3

Total18 credits

Second Semester

  • 204306Ethics for Computer Professionals1
  • 204456Machine Learning3
  • 229390Seminar in Data Science1
  • General Education Electives3
  • Major Electives9
  • Minor (if any)3

Total20 credits

Fourth Year

First Semester

  • 229496Cooperative Education6

Total6 credits

Second Semester

  • 229452Research in Data Science3
  • Major Electives6
  • Minor (if any)3
  • Free Electives3

Total15 credits

Curriculum structure, courses and study plan from Chiang Mai University's TQF2 system (retrieved 11 October 2026)

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