Enquiry for Major/Minor/Programme Requirements

Major Title Major in Decision Analytics
Offered to students admitted to Year 1 in 2017-2018
Objectives:

Amidst an upsurge of digital data produced worldwide nowadays, the Major in Decision Analytics aims to equip students with the skills and expertise in leveraging and managing big data in real time, and provide them with solid training in making digitized information a strategic part of critical decision-making and resource allocation with greater clarity and accuracy. Core courses in the curriculum emphasize the fundamental concepts and methodologies of decision analytics which include but not limited to statistical analysis, data mining and data visualization, programming, data structuring, mathematical and statistical modelling and implementation of database systems. Elective courses focus on diverse and applied techniques of decision analytics in multidisciplinary fields.

Learning Outcomes:
By the end of this programme, students should be able to:
PLO 1 :

apprehend the concepts of decision analytics and its underlying theory in relation to a broad range of related disciplinary academic or professional areas (by means of coursework, tutorial classes and/or project-based learning in the curriculum)

PLO 2 :

identify and adopt appropriate analytical techniques and tools to extract and classify critical information from structured or unstructured data (by means of coursework, tutorial classes and/or project-based learning in the curriculum)

PLO 3 :

be proficient with the design and implementation of advanced modelling techniques and database management, and offer effective recommendations for analytic initiatives and solutions (by means of coursework, tutorial classes and/or project-based learning in the curriculum)

PLO 4 :

evaluate the quality of information from different sources in support of critical decision making, process streamlining and the optimization of resources, and appraise the related ethical issues (by means of coursework, tutorial classes and/or project-based learning in the curriculum)

PLO 5 :

communicate to people effectively and efficiently with professionalism and accuracy using interactive and dynamic tools to translate technical information and present collaborative and strategic ideas (by means of coursework, tutorial classes, project-based and/or capstone learning in the curriculum)

PLO 6 :

gain insights into current advances in decision analytics and confidence to solve real-life problems through either project or industrial training (by means of coursework, tutorial classes, project-based and/or capstone learning in the curriculum)

Impermissible Combination:

BEng in Computer Science
Major in Computing and Data Analytics
Major in Computer Science
Minor in Computer Science
Major in Risk Management
Major in Statistics
Minor in Statistics

Required courses (96 credits)
1. Introductory level courses (48 credits)
Disciplinary Core Courses: Science Foundation Courses (12 credits)
SCNC1111 Scientific method and reasoning (6)
SCNC1112 Fundamentals of modern science (6)
Disciplinary Core Courses (36 credits)
COMP1117 Computer programming (6)
COMP2119 Introduction to data structures and algorithms (6)
MATH1013 University mathematics II (6)
MATH2014 Multivariable calculus and linear algebra (6)
STAT2601 Probability and statistics I (6)
STAT2602 Probability and statistics II (6)
2. Advanced level courses (42 credits)
Disciplinary Core Courses (30 credits)
COMP3278 Introduction to database management systems (6)
MATH3904 Introduction to optimization (6)
STAT3600 Linear statistical analysis (6)
STAT3612 Data mining (6)
STAT4609 Big data analytics (6)
Disciplinary Electives (12 credits)
At least 12 credits selected from the following courses:
COMP3250 Design and analysis of algorithms (6)
COMP3270 Artificial intelligence (6)
COMP3323 Advanced database systems (6)
COMP3407 Scientific computing (6)
MATH3408 Computational methods and differential equations with applications (6)
MATH3600 Discrete mathematics (6)
MATH3601 Numerical analysis (6)
MATH3901 Operations research I (6)
STAT3616 Advanced SAS programming (6)
STAT3620 Modern nonparametric statistics (6)
STAT3621 Statistical data analysis (6)
STAT3622 Data visualization (6)
STAT4601 Time-series analysis (6)
STAT4602 Multivariate data analysis (6)
3. Capstone requirement (6 credits)
At least 6 credits selected from the following courses:
STAT3799 Directed studies in statistics (6)
STAT4710 Capstone experience for statistics undergraduates (6)
STAT4766 Statistics internship (6)
STAT4799 Statistics project (12)
 
Notes:

1. Students may consider taking the following courses if they wish to pursue a more focused study in the following areas:

a. Biomedical Analytics
BIOL4417 'Omics' and systems biology
STAT3607 Statistics in clinical medicine and bio-medical research
STAT3608 Statistical genetics
STAT3620 Modern nonparametric statistics
STAT3621 Statistical data analysis
STAT4602 Multivariate data analysis

b. Financial and Risk Analytics
STAT3616 Advanced SAS programming
STAT3621 Statistical data analysis
STAT4601 Time series analysis
Plus advanced level courses listed for the Major in Risk Management

c. Operational Analytics
COMP3250 Design and analysis of algorithms
MATH3600 Discrete mathematics
MATH3901 Operations research I
MATH3943 Network models in operations research
MATH4902 Operations research II
STAT3606 Business logistics

2. Double-counting of courses up to a maximum of 24 credits is permissible when a student with a science major opts to undertake a second major in science. The double-counted courses must include SCNC1111 Scientific method and reasoning (6 credits) and SCNC1112 Fundamentals of modern science (6 credits).  Additional credits to be double-counted must be for courses required ('disciplinary core') by both majors. For cases with 24 or less double-counted credits, the student must make up an equivalent number of credits by taking other courses offered by any Faculty.

3. If more than 24 credits (including SCNC1111 & SCNC1112) are listed as required courses ("disciplinary core") in both the first and second majors undertaken by a student, the student must make up the number of credits above the 24 permissible by taking replacement course(s) (disciplinary electives) in the second major.  Double counting of credits is not permissible for major-minor or double-minors combinations.  For details, please refer to "Students taking double Majors, Major-Minor or double Minors with overlapping course requirements" in the BSc syllabuses.

4. Students are not required to take Capstone if this Science major is taken as a second major on the condition that the capstone experience in the first major requires the integration or application of knowledge from both major disciplines.  If this is approved, a 6-credit advanced level course (disciplinary electives) in the second major must be taken to fulfill the credit requirement of the capstone experience.

5. Students must have level 2 or above in HKDSE Extended Module 1 or 2 of Mathematics or equivalent to take this major.  Students who do not fulfill this requirement are advised to take MATH1011 University mathematics I.

6. Students taking the Mathematics related major/minor should check the exemption and replacement arrangement for the introductory level Disciplinary Core Mathematics courses at http://www.scifac.hku.hk/ug/current/bsc/curriculum/overlapping-course-req.

Remarks:

Important! Ultimate responsibility rests with students to ensure that the required pre-requisites and co-requisite of selected courses are fulfilled. Students must take and pass all required courses in the selected primary science major in order to satisfy the degree graduation requirements.



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