Offered to students admitted to Year 1 in ALL
Major/Minor ALL
Course Type
Offer in 2018 - 2019 Y N
Course Code PHYS4151
Date2019/07/23 19:15
Enquiry for Course Details
   <<< This course is not offered in 2018 - 2019. Course details are subject to change. >>>
PHYS4151 Data analysis and modeling in physics (6 credits) Academic Year 2018
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 general modeling and data analysis techniques used 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 and concepts rather than the use of computer packages.  This course provides a solid foundation for students who intended to do computational physics and complex systems research.  It also prepares students to work in related industries.
Course Contents & Topics Topics include basic data analysis techniques, linear and non-linear fittings, determining the goodness of the fit, basic hypothesis testing techniques, modeling physical and related systems via differential (ordinary and/or partial), difference equations as well as discrete models such as cellular automata, introduction to complex systems, complex adaptive systems and nonlinear dynamics, the use of computer package such as Matlab in modeling and data analysis.  The emphasis is on the basic principles and concepts rather than a particular software package or physical model.  Depending on the mutual interests of the coordinators and the 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 situations of the physical world
CLO 3 analyse and solve problems with the aid of computer packages such as Matlab
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 PHYS3150); and
Pass in any one of the following courses: PHYS3350, PHYS3351, PHYS3450, PHYS3550
Course Status with Related Major/Minor /Professional Core 2018 Major in Physics ( Disciplinary Elective )
2018 Minor in Physics ( Disciplinary Elective )
2017 Major in Astronomy ( Disciplinary Elective )
2017 Major in Mathematics/Physics ( Disciplinary Elective )
2017 Major in Physics ( Disciplinary Elective )
2017 Minor in Physics ( Disciplinary Elective )
2016 Major in Astronomy ( Disciplinary Elective )
2016 Major in Mathematics/Physics ( Disciplinary Elective )
2016 Major in Physics ( Disciplinary Elective )
2016 Minor in Physics ( Disciplinary Elective )
2015 Major in Astronomy ( Disciplinary Elective )
2015 Major in Mathematics/Physics ( Disciplinary Elective )
2015 Major in Physics ( Disciplinary Elective )
2015 Minor in Physics ( Disciplinary Elective )
2014 Major in Astronomy ( Disciplinary Elective )
2014 Major in Mathematics/Physics ( Disciplinary Elective )
2014 Major in Physics ( Disciplinary Elective )
2014 Minor in Physics ( Disciplinary Elective )
Course to PLO Mapping 2018 Major in Physics < PLO 1,2,3,4 >
2017 Major in Astronomy < PLO 1,2,3,4 >
2017 Major in Mathematics/Physics < PLO 1,2,3,4 >
2017 Major in Physics < PLO 1,2,3,4 >
2016 Major in Astronomy < PLO 1,2,3,4 >
2016 Major in Mathematics/Physics < PLO 1,2,3,4 >
2016 Major in Physics < PLO 1,2,3,4 >
2015 Major in Astronomy < PLO 1,2,3,4 >
2015 Major in Mathematics/Physics < PLO 1,2,3,4 >
2015 Major in Physics < PLO 1,2,3,4 >
2014 Major in Astronomy < PLO 1,2,3,4 >
2014 Major in Mathematics/Physics < PLO 1,2,3,4 >
2014 Major in Physics < PLO 1,2,3,4 >
Offer in 2018 - 2019 N        To be confirmed Examination To be confirmed
Offer in 2019 - 2020 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.
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)
Course Website NIL
Additional Course Information NIL
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