SDST2601 Probability and statistics I (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota ---
Course Co-ordinator Dr K P Wat, SCDS (Department of Statistics and Actuarial Science) < watkp@hku.hk >
Teachers Involved (Dr K P Wat,Statistics & Actuarial Science)
Course Objectives The discipline of statistics is concerned with situations in which uncertainty and variability play an essential role and forms an important descriptive and analytical tool in many practical problems. Against a background of motivating problems this course develops relevant probability models for the description of such uncertainty and variability.
Course Contents & Topics Sample spaces; Operations of events; Probability and probability laws; Conditional probability; Independence; Discrete random variables; Cumulative distribution function (cdf); Probability mass function (pmf); Bernoulli, binomial, geometric, and Poisson distributions; Continuous random variables; Cumulative distribution function (cdf); Probability density function (pdf); Exponential, gamma, and normal distributions; Functions of a random variable; Joint distributions; Marginal distributions; Conditional distributions; Independent random variables; Functions of jointly distributed random variables; Expected value; Variance and standard deviation; Covariance and correlation.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 understand the basic concepts in probability theory
CLO 2 gain some insights to statistics and inference
CLO 3 solve real-world problems by using probability calculations
CLO 4 pursue their further studies in statistics and quantitative analysis
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass or already enrolled  in MATH2014 or (MATH2101 and MATH2211); and
Not for students who have passed in ELEC2844, MATH3603, SDST1603, SDST2901 or already enrolled in these courses; and
Not for BSc(ActuarSc) students.
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor/Professional Core 2025 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Core/Compulsory )
2025 Bachelor of Engineering in Computer Science ( Core/Compulsory )
2025 Bachelor of Engineering in Artificial Intelligence and Data Science ( Core/Compulsory )
2025 Bachelor of Statistics ( Core/Compulsory )
2025 Major in Decision Analytics ( Core/Compulsory )
2025 Major in Risk Management ( Core/Compulsory )
2025 Major in Statistics ( Core/Compulsory )
2025 Minor in Risk Management ( Disciplinary Elective )
2025 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping 2025 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 1,2,3 >
2025 Professional Core in Decision Analytics < PLO 1,2,3,4,5 >
2025 Professional Core in Risk Management < PLO 2,3,4 >
2025 Professional Core in Statistics < PLO 1,4,5,6 >
Offer in 2025 - 2026 Y        1st sem    2nd sem    Examination Dec    May     
Offer in 2026 - 2027 Y
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.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
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.
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.
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.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (participation, assignments, tutorials, and class test(s)) 40.0 1,2,3
Examination One 2-hour written examination 60.0 1,2,3
Required/recommended reading
and online materials		 Blitzstein, J. K. and Hwang, J. (2019). Introduction to Probability (2nd Edition). CRC Press.
Ghahramani, S. (2019). Fundamentals of Probability with Stochastic Processes (4th Edition). CRC Press.
Pitman, J. (1993). Probability. Springer.
DeGroot, M. H. and Schervish, M. J. (2014). Probability and Statistics (4th Edition). Pearson.
Ross, S. M. (2019). A First Course in Probability (10th Edition). Prentice Hall.
Ross, S. M. (2019). Introduction to Probability Models (12th Edition). Academic Press.
Miller, I. and Miller, M. (2014). John E. Freund's Mathematical Statistics with Applications (8th Edition). Prentice Hall.
Hogg, R. V., McKean, J. W., and Craig, A. T. (2019). Introduction to Mathematical Statistics (8th Edition). Prentice Hall.
Hogg, R. V., Tanis, E. A., and Zimmerman, D. L. (2020). Probability and Statistical Inference (10th Edition). Pearson.
Casella, G. and Berger, R. L. (2002). Statistical Inference (2nd Edition). Duxbury Press.
Miller, M. B. (2014). Mathematics and Statistics for Financial Risk Management (2nd Edition). Wiley.
Chung, K. L. (2001). A Course in Probability Theory (3rd Edition). Academic Press.
Course Website http://moodle.hku.hk
Additional Course Information NIL