Prasiswazah FST



Statistics Programme

The statistics programme will expose students to theoretical and applied knowledge involving various problem solving techniques and data analysis. The era of scientific data today greatly requires the skills of statistical analysis. The courses are designed to model students’ creativity in defining and understanding problems, identifying data collection methods, interpreting data analysis. The courses are designed so that graduates can adapt in the world of work related to data analytics.

Major courses

1st year

2nd year

3rd year

4th year


Career Prospects

Core lecturers

Assoc. Prof. Dr. Wan Zawiah Wan Zin

Statistical Modelling for Extreme Data, Industrial Statistics/Quality (

Assoc. Prof. Dr. Nur Riza Mohd Suradi

Quality and Performance Measurement, Structural Equation Modelling (

Assoc. Prof. Dr. Nora Muda

Bioinformatics, Statistical Modeling and Robust Statistics (

Assoc. Prof. Dr. Zainol Mustafa

Quality Engineering (

Assoc. Prof. Dr. Nurulkamal Masseran

Applied Statistical Modelling, Data Mining

Dr. Wan Rosmanira Ismail

Timetabling, Route Scheduling, Analysis of Efficiency and Productivity (

Dr. Mohd Aftar Abu Bakar

Time Series Analysis, Computational Statistic, Statistical Modelling, Signal Processing, Data Science, Machine Learning (

Assoc. Prof. Dr. Noratiqah Mohd Arif

Applied Statistics, Data Analysis, Computational Statistics (

Assoc. Prof. Dr. Zamira Hasanah Zamzuri

Multivariate statistics and Bayesian modeling(

Dr. Zalina Mohd Ali

Statistics, Multivariate Analysis, Bayesian Modeling Analysis, Water Quality Index Development, Customer Satisfaction (

Dr. Bernard Lee Kok Bang

Data Science, Machine Learning, Data Analytics (

Dr. Nor Hamizah Miswan

Applied Statistics, Time Series Analysis (

Dr. Muhammad Hilmi Abdul Majid

Statistical Modeling; Statistics & Probability (

Mrs. Faridatulazna Ahmad Shahabudin

Forecasting; Survey and Sampling

Mrs. Rofizah Mohammad

Bayesian statistics (

Ms. Nurul Afiqah Burhanuddin

Bayesian Modeling; Statistical Machine Learning (

Entry requirements

General requirement:

  1. Complete 12 years of Primary and High School education; and
  2. Obtain High School (e.g. Higher Secondary Certificate, Senior Secondary School Graduation Certificate, Tawjihiyah, Ijazah Sekolah Menengah Atas, High School Diploma); and
  3. Meet minimum English requirement set by faculty; and
  4. Pass UKM interview (if required) ; and
  5. Meet faculty specific requirement;

Specific programme requirement:

Forms and Circulars
Academic Calendar