Offered to students admitted to Year 1 in ALL
Major/Minor ALL
Course Type
Offer in 2023 - 2024 Y N
Course Code PHYS4151
Date2023/09/24 21:02
Enquiry for Course Details
PHYS4151 Data analysis and modeling in physics (6 credits) Academic Year 2023
Offering Department Physics Quota ---
Course Co-ordinator Prof H F Chau, Physics < hfchau@hku.hk >
Teachers Involved (Prof H F Chau,Physics)
Course Objectives This course covers commonly used data analysis and computational modeling techniques in physics and related subjects with special emphasis on their uses in complex systems, nonlinear systems and adaptive systems.  The focus is on the basic principles rather than blind usage of computer packages and apps although we do use packages in the course.  This is an elective course for the computational physics and experimental physics themes.  This is also an essential course for who plan to pursue postgraduate studies in computational physics and complex systems and work in related areas.
Course Contents & Topics Basic data analysis techniques such as linear and non-linear fittings, determination of the goodness of the fit, commonly used hypothesis testing techniques in physics; modeling physics and related systems via continuous, discrete and agent-based approaches; introduction to complex systems, complex adaptive systems and nonlinear dynamics; basic numerical integration and ordinary differential equation techniques including adaptive techniques; basic numerical eigenvalue and eigenvector techniques; the use of computer packages such as Matlab and Mathematica in modeling and data analysis although the emphasis is on the basic principles and concepts behind rather than features and usage of those packages; depending on mutual interests of the course coordinator and students, illustrative examples will be drawn from conventional fields such as classical mechanics, electromagnetism and quantum mechanics as well as more recent fields like biophysics, econophysics and sociophysics.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 describe and explain state-of-the-art modeling methods used in physics
CLO 2 apply basic modeling techniques, together with logical and mathematical reasoning, to study problems in the physical world
CLO 3 analyze and solve problems with the aid of computer packages such as Matlab and Mathematica
CLO 4 critically interpret experimental data from physics experiments
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in (MATH3301 or MATH3401 or MATH3403 or MATH3405 or PHYS2160 or PHYS3150) and (PHYS3350 or PHYS3351 or PHYS3450 or PHYS3550)
Course Status with Related Major/Minor /Professional Core 2023 Major in Physics ( Disciplinary Elective )
2023 Major in Physics (Intensive) ( Disciplinary Elective )
2023 Minor in Physics ( Disciplinary Elective )
2022 Major in Physics ( Disciplinary Elective )
2022 Major in Physics (Intensive) ( Disciplinary Elective )
2022 Minor in Physics ( Disciplinary Elective )
2021 Major in Physics ( Disciplinary Elective )
2021 Major in Physics (Intensive) ( Disciplinary Elective )
2021 Minor in Physics ( Disciplinary Elective )
2020 Major in Physics ( Disciplinary Elective )
2020 Major in Physics (Intensive) ( Disciplinary Elective )
2020 Minor in Physics ( Disciplinary Elective )
2019 Major in Physics ( Disciplinary Elective )
2019 Major in Physics (Intensive) ( Disciplinary Elective )
2019 Minor in Physics ( Disciplinary Elective )
Course to PLO Mapping 2023 Major in Physics < PLO 1,2,3,4 >
2023 Major in Physics (Intensive) < PLO 1,2,3,4 >
2022 Major in Physics < PLO 1,2,3,4 >
2022 Major in Physics (Intensive) < PLO 1,2,3,4 >
2021 Major in Physics < PLO 1,2,3,4 >
2021 Major in Physics (Intensive) < PLO 1,2,3,4 >
2020 Major in Physics < PLO 1,2,3,4 >
2020 Major in Physics (Intensive) < PLO 1,2,3,4 >
2019 Major in Physics < PLO 1,2,3,4 >
2019 Major in Physics (Intensive) < PLO 1,2,3,4 >
Offer in 2023 - 2024 Y        2nd sem    Examination May     
Offer in 2024 - 2025 N
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills. Apply highly effective computer modeling skills and techniques. Critical use of data and results to draw appropriate and insightful conclusions.
B Demonstrate substantial command of the knowledge and skills required for attaining most of the course learning outcomes. Show evidence of analytical and critical abilities, reasoned logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations using effective organizational and presentation skills. Apply effective computer modeling skills and techniques. Correct use of data of results to draw appropriate conclusions.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills. Apply moderately effective computer modeling skills and techniques. Mostly correct but some erroneous use of data and results to draw appropriate conclusions.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills. Apply partially effective computer modeling skills and techniques. Limited ability to use data and results to draw appropriate conclusions.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective. Apply minimally effective or ineffective computer modeling skills and techniques. Misuse of data and results and/or unable to draw appropriate conclusions.
Communication-intensive Course N
Course Type Lecture with laboratory component course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Laboratory 12
Lectures 36
Tutorials 8
Reading / Self study 80
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments 10 CLO 1,2,3,4
Examination 2-hour written exam 50 CLO 1,2,4
Presentation 20 CLO 1,4
Project report 20 CLO 1,2,3,4
Required/recommended reading
and online materials		 Lecture notes provided by Course Coordinator
J. R. Taylor: An Introduction to Error Analysis (Univ. Sci. Books, 2rd ed., 1996)
B. Hahn and D. Valentine: Essential Matlab for Engineers and Scientists (Academic Press, 5th ed., 2013)
L. Lam: Nonlinear Physics for Beginners (World Sci., 1998)
N. Boccara: Modeling Complex Systems (Springer, 2nd ed., 2012)
A.-L. Barabasi and H. E. Stanley: Fractal Concepts in Surface Growth (CUP, 1995)
R. H. Landau, M. Jose Paez and C. C. Bordeianu: A Survey Of  Computational Physics (Princeton Univ. Press, 2008)
R. L. Burden, J. D. Faires and A. M. Burden: Numerical Analysis (Brooks/Cole Cengage Learning, 10th ed., 2015)
Course Website http://moodle.hku.hk
Additional Course Information NIL
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