Master of Statistics in Marine Science (by Coursework)

Statistics in Marine Science

Master (by Coursework)

Overview

Statistics plays a major role in most of real life situation. From understanding the characteristics of the systems to the forecasting of the complex phonemena, the integration of statistics into the modelling part of the system is really demanded. The aim of this programme is to foster an advanced statistics knowledge not only in marine science and oceanography, but also in other fields. You will discover the decision making of the complex problems using advanced statistics with the aid of computer programming. 

Entry requirements

  • Bachelor’s Degree in Science (Statistics) recognized by UMT’S Senate with a minimum CGPA of 2.75; OR minimum CGPA 2.50 with minimum five (5) years’ work experiences.
  • Bachelor’s Degree in Science (Computational Mathematics) or Bachelor’s Degree in Science (Financial Mathematics) with a minimum CGPA of 2.75 from UMT; OR minimum CGPA 2.50 with minimum five (5) years’ work experiences.
  • Bachelor’s Degree in Science which equivalent to Parts (i) and (ii) with minimum CGPA of 2.75 from UMT or any other higher institutions recognized by the Senate; OR minimum CGPA 2.50
    with minimum five (5) years’ work experiences.

English Language Requirements

  • English requirement for international candidates: TOEFL with minimum score 550; or IELTS with minimum band 6.0.

Programme Structure

Duration: Full-time: 1 year (minimum) / Part-time: 2 years (minimum)
Credit hours: 44 credits

Program Core modules include 

This course was chosen to expose students to investigation-based interactions on how to handle and interpret oceanographic data which includes physical oceanography, biological oceanography and chemical oceanography. This mastery of knowledge is very important in order to present the results of a comprehensive and efficient analysis

This course discusses the introductory of time series, forecasting using Box-Jenkins, forecasting using exponential smoothing and forecasting using ARCH dan GARCH models. Forecasting using neural networks end this course.

This course allows students to gain knowledge of linear and nonlinear models for marine data with appropriate approaches.

This course discusses the exploratory data analysis (EDA) used in the field of marine. Topics such as assumptions of EDA and techniques of EDA are discussed. Methods and conditions that are appropriate are also discussed. This course also discusses the categorical data analysis used in the field of marine. Topics such as contingency table, generalized linear model and model for multinomial responses are discussed. Methods and conditions that are appropriate are also discussed.

This course discusses the general methods used in conducting research. The style and method of writing a research proposal paper is also discussed. In addition, issues related to the attitude and value of professionalism as researchers and ethics in writing and publishing are also being highlighted in this course.

This course aims to provide a space for students to demonstrate social skills, teamwork and responsibility in organizing postgraduate colloqium ethically, morally and professionally. In addition, effective oral communication is also emphasized through the individual presentation of the organized colloquium.

Programme Elective modules may include

Machine learning is concerned with the question on how to make computers learn from experience. The ability to learn is not only central to most aspects of intelligent behaviour, but machine learning techniques have become key components of many software systems. This course covers both well-established and advanced machine learning techniques such as Neural Network, Support Vector Machines etc.

The course describes the stochastic method for ocean wave analysis which provides a route to predicting the characteristics of random ocean waves which give vital information for the design and safe operation of ships and sea structures. Begin with a discussion on fundamental knowledge on probability theory, stochastic process and transformation, the course is then describes the essential elements of wind-generated random seas from the stochastic point of view. Next, spectral analysis technique for ocean waves is introduced, probabilistic prediction of wave amplitude and height under various condition is done. Consideration on the wave height, period and travel direction of wind-generated random wave completes the course.

This course begins with an introduction to the R software. Next, this course describes the way of analyzing data in the context of statistical inference and regression using the R software. This course ends with computer-assisted data analysis for various advanced topics in statistics.

This course discusses algorithm concepts such as mapping and comparison between algorithms. It focuses on a few Quasi-Newton methods and constrained optimization. Optimization method in maritime is also covered.

Project module include

This course enables students to expand their Mathematical knowledge, understading and skills that are required to solve a problem in related field using scientific methodology. These include planning, implementation and presenting significant research project outcomes.

Fees and funding

Fees

The 2021/22 annual tuition fees for this programme are:

Home                                 RM    11, 140.00
International full-time      MYR 17, 810.00

General additional costs

Find out more about accommodation and living costs, plus general additional costs that you may pay when studying at UMT. 

Funding

Government funding

You may be eligible for government finance to help pay for the costs of studying. See the Government’s student finance website.

Scholarships

Scholarships are available for excellence in academic performance, sport and music and are awarded on merit. For further information on the range of awards available and to make an application see our scholarships website.

Semester II Session 2020/2021

Contact

Dr. Che Mohd Imran Che Taib
Email: imran@umt.edu.my 
Phone: +609-668 3759 (office)
             +6013-9307556 (mobile)