Data Analytics and Informatics
Applicable to students admitted in 2026-27.
Description
Students are required to complete a minimum of 18 units of courses, with at least 6 units at 3000 or above level, as follows:
1. Required Courses[a]: 6 units
Show 6 course(s) →
| Code | Title | T1 | T2 | Prerequisites |
|---|---|---|---|---|
| ENGG 2760 [2] | Probability for Engineers | Not for students who have taken ENGG2430 or 2450 or 2470 or ESTR2002 or 2005 or 2012 or 2018 or 2308 or 2362 or IERG2470 or MIEG2440. | ||
| ESTR 2018 [2] | Probability for Engineers | Not for students who have taken ENGG2430 or ENGG2450 or ENGG2470 or ENGG2760 or ESTR2002 or ESTR2005 or ESTR2012 or ESTR2308 or ESTR2362 or IERG2470 or MIEG2440. | ||
| ENGG 2780 [2] | Statistics for Engineers | Not for students who have taken ENGG2430 or ENGG2450 or ESTR2002 or ESTR2005 or 2020.Co-requisite(s): ENGG2760 or ESTR2018 or 2308 or IERG2470. | ||
| ESTR 2020 [2] | Statistics for Engineers | Not for students who have taken ENGG2430 or ENGG2450 or ENGG2780 or ESTR2002 or ESTR2005.Co-requisite(s): ENGG2760 or ESTR2018 or ESTR2308 or IERG2470. | ||
| IERG 2470 | Probability Models and Applications | Not for students who have taken ENGG2430, ENGG2450, ENGG2460, ENGG2470, ESTR2002, ESTR2005, ESTR2010, ESTR2012, ESTR2308, ESTR2362 or MIEG2440. | ||
| ESTR 2308 | Probability Models and Applications | Not for students who have taken ENGG2430 or ENGG2450 or ENGG2460 or ENGG2470 or ESTR2002 or ESTR2005 or ESTR2010 or ESTR2012 or ESTR2362 or IERG2470 or MIEG2440. |
(b) SEEM2460/ESTR2540[b]
Show 2 course(s) →
| Code | Title | T1 | T2 | Prerequisites |
|---|---|---|---|---|
| SEEM 2460 | Introduction to Data Science | Not for students who have taken ESTR2540. | ||
| ESTR 2540 | Introduction to Data Science | Not for students who have taken SEEM2460. |
2. Elective Courses: 12 units
Choose 12 units of courses of at least 3 subject areas from the following:
AIST4030, 4050, BMEG3102, 3103, BMEG3105/ESTR3605[b], BMEG3130, CSCI3170, 3220, 3320, CSCI4180/ESTR4106[b], CSCI4190, DSPS3190, 3202, 3790, 3791, EESC4510, FTEC4002, IERG3280/ESTR3302[b], IERG3320/ESTR3306[b], IERG4080/ESTR4312[b], IERG4160, 4230, IERG4300/ESTR4300[b], IERG4320/ESTR4324[b], IERG4330/ESTR4316[b], MAEG4010/ESTR4408[b], MATH3320, SEEM3680/ESTR3512[b], SEEM4630, SEEM4720/ESTR4506[b], SEEM4730/ESTR4508[b], SEEM4760/ESTR4512[b], STAT3008, 3009, 3010, 3210, 4001, 4006, 4010, 4012
Show 53 course(s) →
| Code | Title | T1 | T2 | Prerequisites |
|---|---|---|---|---|
| AIST 4030 | Structured Data Modeling and Graph Neural Network | Prerequisite: AIST3120 or CSCI3230 or ESTR3108 or CSCI3320. | ||
| AIST 4050 | Embodied Intelligence: Algorithms and Applications | Prerequisite: AIST1000. | ||
| BMEG 3102 | Bioinformatics | Not for students who have taken ELEG4120 or CSCI3220 or ESTR3110. | ||
| BMEG 3103 | Big Data in HealthCare | — | ||
| BMEG 3105 | Data Analytics for Personalized Genomics and Precision Medicine | Not for students who have taken ESTR3605. | ||
| ESTR 3605 | Data Analytics for Personalized Genomics and Precision Medicine | Not for students who have taken BMEG3105. | ||
| BMEG 3130 | Tele-medicine and Mobile Healthcare | — | ||
| CSCI 3170 | Introduction to Database Systems | Prerequisite: CSCI2100 or 2520 or ESTR2102.For 2nd-year entrants, the prerequisite will be waived. | ||
| CSCI 3220 | Algorithms for Bioinformatics | Not for students who have taken BMEG3102 or ESTR3110. | ||
| CSCI 3320 | Fundamentals of Machine Learning | Prerequisite: ENGG2430 or 2450 or 2760 or 2780 or ESTR2002 or 2005 or 2018 or 2020 or 2308 or 2362 or IERG2470 or MIEG2440 or STAT2001. | ||
| CSCI 4180 | Introduction to Cloud Computing and Storage | Co-requisite: CSCI3150 or ESTR3102.Not for students who have taken ESTR4106. | ||
| ESTR 4106 | Introduction to Cloud Computing and Storage | Co-requisite: CSCI3150 or ESTR3102.Not for students who have taken CSCI4180. | ||
| CSCI 4190 | Introduction to Social Networks | Pre-requisite: CSCI2100 or 2520 or ESTR2102. | ||
| DSPS 3190 | Big Data Analytics for Public Policy | Pre-requisite(s): DSPS2202. | ||
| DSPS 3202 | Machine Learning for Public Policy | Pre-requisite(s): DSPS2201. | ||
| DSPS 3790 | Social Network Analysis for Public Policy | Pre-requisite(s): DSPS2201. | ||
| DSPS 3791 | Natural Language Processing for Public Policy | Pre-requisite(s): DSPS3202. | ||
| EESC 4510 | Statistical Methods and Data Analysis for Earth and Environmental Sciences | Not for students who have taken ESSC4510.Pre-requisite: (STAT1011 or 1012), and ((EESC2030 or ESSC2030) or PHYS2061). | ||
| FTEC 4002 | Behavioral Analytics | — | ||
| IERG 3280 | Networks: Technology, Economics, and Social Interactions | Not for students who have taken ESTR3302. | ||
| ESTR 3302 | Networks: Technology, Economics, and Social Interactions | Not for students who have taken IERG3280. | ||
| IERG 3320 | Social Media and Human Information Interaction | Prerequisite: IERG2051 or MIEG2051 or ENGG2030 or ESTR2302 or ESTR2360 or ESTR2206.Not for students who have taken ESTR3306. | ||
| ESTR 3306 | Social Media and Human Information Interaction | Prerequisite: IERG2051 or MIEG2051 or ENGG2030 or ESTR2302 or ESTR2360 or ESTR2206.Not for students who have taken IERG3320. | ||
| IERG 4080 | Building Scalable Internet-based Services | Pre-requisite: IERG3080.Not for students who have taken ESTR4312. | ||
| ESTR 4312 | Building Scalable Internet-based Services | Not for students who have taken IERG4080.Pre-requisite: IERG3080. | ||
| IERG 4160 | Image Processing and Visual Understanding | Not for students who have taken ELEG4502 or ELEG4512. | ||
| IERG 4230 | Introduction to Internet of Things | — | ||
| IERG 4300 | Web-scale Information Analytics | Not for students who have taken CSCI5510 or ENGG4030 or ESTR4300. | ||
| ESTR 4300 | Web-scale Information Analytics | Not for students who have taken CSCI5510 or ENGG4030 or IERG4300. | ||
| IERG 4320 | Data Science in Practice | Not for students who have taken ESTR4324. | ||
| ESTR 4324 | Data Science in Practice | Not for students who have taken IERG4320. | ||
| IERG 4330 | Programming Big Data Systems | Pre/Co-requisite: ENGG4030 or ESTR4300 or IERG4300.Not for students who have taken ESTR4316 or IEMS5730. | ||
| ESTR 4316 | Programming Big Data Systems | Not for students who have taken IERG4330 or IEMS5730.Co-/Pre-requisite(s): ENGG4030 or ESTR4300 or IERG4300. | ||
| MAEG 4010 | Computer-integrated Manufacturing | Not for students who have taken ESTR4408. | ||
| ESTR 4408 | Computer-integrated Manufacturing | Not for students who have taken MAEG4010. | ||
| MATH 3320 | Foundation of Data Analytics | — | ||
| SEEM 3680 | Technology, Consulting and Analytics in Practice | Not for students who have taken ESTR3512, ESTR4504 or SEEM4680. | ||
| ESTR 3512 | Technology, Consulting and Analytics in Practice | Not for students who have taken ESTR4504, SEEM3680 or SEEM4680. | ||
| SEEM 4630 | E-Commerce Data Mining | — | ||
| SEEM 4720 | Computational Finance | Not for students who have taken ESTR4506. | ||
| ESTR 4506 | Computational Finance | Not for students who have taken SEEM4720.Pre-requisite: SEEM2520 or SEEM3570 or SEEM3590 or ESTR3508 or ESTR3509. | ||
| SEEM 4730 | Data Analytics Models and Methods for Financial Engineering and Fintech | Not for students who have taken ESTR4508. | ||
| ESTR 4508 | Data Analytics Models and Methods for Financial Engineering and Fintech | Not for students who have taken SEEM4730. | ||
| SEEM 4760 | Stochastic Models for Decision Analytics | 1. Pre-requisites: (a) ENGG1110/ESTR1002, ENGG1120/ESTR1005, ENGG1130/ESTR1006, ENGG2440/ESTR2004, ENGG2760/ESTR2018, ENGG2780/ESTR2020, MATH1510 and SEEM2420; or (b) with course instructor's approval.2. Not for students who have taken ESTR4512. | ||
| ESTR 4512 | Stochastic Models for Decision Analytics | 1. Pre-requisites: (a) ENGG1110/ESTR1002, ENGG1120/ESTR1005, ENGG1130/ESTR1006, ENGG2440/ESTR2004, ENGG2760/ESTR2018, ENGG2780/ESTR2020, MATH1510 and SEEM2420; or (b) with course instructor's approval.2. Not for students who have taken SEEM4760. | ||
| STAT 3008 | Applied Regression Analysis | — | ||
| STAT 3009 | Recommender Systems | — | ||
| STAT 3010 | Optimization for Statistics and Data Science | — | ||
| STAT 3210 | Statistical Techniques in Life Sciences | Not for students who have taken STAT3004. | ||
| STAT 4001 | Data Mining and Statistical Learning | — | ||
| STAT 4006 | Categorical Data Analysis | — | ||
| STAT 4010 | Bayesian Learning | — | ||
| STAT 4012 | Statistical Principles of Deep Learning with Business Applications | — |
Explanatory Notes
1.This Minor Programme is not applicable to students who major in Artificial Intelligence: Systems and Technologies, Computer Science, Information Engineering, Mathematics and Information Engineering, and Systems Engineering and Engineering Management.
2.No more than 9 units of courses taken to fulfill the requirements of the students’ Major Programme(s) and other Minor Programme(s) respectively can be used to fulfill the requirement of this Minor Programme.
[a]Non-engineering students who wish to use other courses to substitute the required courses will have to seek approval from the Programme Director.
[b]ESTR courses are developed based on ordinary engineering courses with additional course materials and challenging assessments. The courses are only applicable to students of the ELITE stream, who can choose a combination of the ESTR courses or the equivalent courses listed in the curriculum to fulfill the minor programme requirements.