SDSC3005 - Computational Statistics

Offering Academic Unit
School of Data Science
Credit Units
Course Duration
One Semester
Course Offering Term*:
Not offering in current academic year

* The offering term is subject to change without prior notice
Course Aims

This course introduces students to algorithms and techniques for statistical computing and their implementations through R software. Students will learn important computational statistics methods such as the EM algorithm, Fisher’s scoring, Monte Carlo simulation, Markov chain Monte Carlo, and bootstrap. Additionally, students will learn statistical applications of these methods, the key advantages of using each method, and how they can be coded in R. Efficient programming methods for R will be taught. Therefore, students gain knowledge of many different tools that can be combined to solve statistical computing problems. Assignments will involve the use R.

Assessment (Indicative only, please check the detailed course information)

Continuous Assessment: 60%
Examination: 40%
Examination Duration: 2 hours
Detailed Course Information


Useful Links

School of Data Science