Courses offered by the Board of Studies in Physical Sciences

Physical Science Board offers following programmes for the graduate students,

Physical Sciences Post Graduate Certificate Postgraduate Certificate in Applied Statistics
Post Graduate Diploma Postgraduate Diploma in Applied Statistics
PG Diploma in Industrial Mathematics
PG Diploma in   Industrial Analytical Chemistry
Computer Science
Masters Programmes Master in Applied Statistics – Plan A, By research
Master in Applied Statistics – Plan B, By coursework
Master in Industrial Mathematics
Master in Industrial Analytical Chemistry
Computer Science
Polymer Science and Technology
MPhil and PhD Chemistry
Mathematics
Physics
Statistics and Computer Science
Information System

Industrial Mathematics

Industrial Mathematics deals with developing mathematical models, finding solutions and interpreting the results of problems that come up in industry. The main objectives of this programme are to provide graduates with an adequate knowledge in Mathematics, Statistics, Operations Research and Scientific Computing, and to provide opportunities for research in applications of Mathematics to existing problems in industry. Although programmes of this nature are well established in developed countries, it is not so in developing countries like Sri Lanka. Such programs are essential to narrow the gap that exists between scientists in industry and mathematicians.

Objectives/ Graduate Profiles

  • Develop knowledge of modeling and data analysis and be able to critically evaluate relevant industrial mathematics and statistics from a variety of sources.
  • Develop independent research skills through completion of supervised and mentored project work.
  • Develop the analytic, modeling, and computational skills needed to spur advances in a range of industrial areas.
  • Develop knowledge in numerical methods, modeling, and discrete math, and select from elective coordinated modules in engineering/science topics such as electrical engineering, bioengineering, physics, and computer science. Y
  • Sound in communication and business skills that prepare you to work in multidisciplinary teams and take leadership roles in the corporate world.

Admission requirements:

  • Bachelor’s Degree with Mathematics as a component preferably B.Sc.(Special) Mathematics, Statistics, Computer Science, B.Sc.(Physical Science) or  Sc.(Engineering) graduates.
  • Be proficient in English, as English will be the medium of instructions for the program.
  • A written examination and/or an interview will be held when the number of applicants exceeds the maximum number that can be accommodated.

Course Duration – one year

Course Description:

Course Code Course Title Lecture hrs. Practical hrs. No. of Credits
Semester I
IM511 2.0 Foundation for Industrial Mathematics 30 2
IM512 2.0 Numerical Analysis 30 2
IM513 3.0 Ordinary and Partial Differential Equations 45 3
IM514 2.0 Computational Mathematics

 

 

30 2
IM515 3.0 Foundation for Industrial Statistics 45 3
Semester II
IM521 2.0 Operational Research 30 2
IM522 2.0 Optimisation 30 2
IM523 2.0 Mathematical Modeling I 45 3
IM524 2.0 Graph Theoretic Applications 30 2
IM525 2.0 Applied Regression Analysis 30 2
IM526 2.0 Design of Industrial Experiments 30 2

Evaluation:

Total requirement for the Diploma is 375 hours of instruction leading to 25 credits or 375 hours of instruction and a research project leading to 30 credits.

A) Postgraduate Diploma in Industrial Mathematics by coursework

Those who wish to obtain the PG Diploma in Industrial Mathematics by coursework should possess a minimum GPA of 3.0 for a total of at least 30.0 credits worth courses at the end of Semester II (Total credits completed by the end of the Semester II is 25.0). In addition, each course included in calculating this minimum GPA of 3.0 should have a minimum of grade C.

B) Postgraduate Diploma in Industrial Mathematics by course work and research project

To be able to obtain the PG Diploma by course work and research project, the student should carry out a research project worth 5.0 credits and submit a project report before the due date and earn a minimum of grade C+ for the PG Diploma Dissertation in addition to satisfying the requirements given above,

C) MSC Industrial Mathematics

Note: The admission requirements are the same as for the PG Diploma which is given under the Diploma Programs in this Handbook.

Duration: 2 years

Course Description:

Course Code Course Title Lecture hrs. Practical hrs. No. of Credits
Semester I
IM511 2.0 Foundation for  Industrial Mathematics 30 2
IM512 2.0 Numerical Analysis 30 2
IM513 3.0 Ordinary and Partial Differential Equations 45 3
IM514 2.0 Computational Mathematics

 

 

30 2
IM515 3.0 Foundation for Industrial Statistics 45 3
Semester II
IM521 2.0 Operational Research 30 2
IM522 2.0 Optimisation 30 2
IM523 2.0 Mathematical Modeling I 45 3
IM524 2.0 Graph Theoretic Applications 30 2
IM525 2.0 Applied Regression Analysis 30 2
IM526 2.0 Design of Industrial Experiments 30 2
Semester III
IM531 2.0 Numerical Methods for Ordinary and Partial Differential Equations 30 2
IM532 3.0 Applied Time Series Analysis, Stochastic Processes and Statistical Quality Control 45 3
IM533 2.0 Mathematical Modeling II 30 2
IM534 2.0 Special Topics in Industrial Mathematics 30 2
IM535 2.0 Special Topics in Industrial Statistics 30 2
Semester IV
IM541 3.0 Seminar* 45 3
IM542 1.0 Technical Writing Skills 15 1
IM551 20.0

IM552 5.0

M.Sc. Thesis                                      PG Dip. Dissertation (Research Project) 20

5

  Total requirement for M.Sc.

 

600 60

* Compulsory to obtain a minimum of a grade C for the M. Sc. degree.

Scholarships: There is a scholarships available for this programme, Weerakoon Watugala Scholarship which will be given to the best merit holder. The Weerakoon Watugala Gold Medal will be given for the student with highest GPA for course work .

Evaluation & Minimum requirement for award of M.Sc.

  • Possess a minimum GPA of 3.0 at the end of Semester IV
  • Minimum of Grade C for Seminar Course (IM 541 3.0)
  • Minimum of Grade D for a total of 32 credits worth courses at the end of Semester IV
  • Carry out research project worth 20 credits and submit a project report before the due date and earn a minimum of Grade B for the thesis.

In addition to the above, if the student had earned a minimum GPA of 3.3 for the courses while earning a Grade A for the M.Sc. thesis he/she will be eligible for a Degree with Merit.

 

Industrial Analytical Chemistry

Chemical analysis plays a vital role in all aspects of life. There is an increasing demand for qualified analytical chemists throughout the world. The M.Sc./Diploma program in Industrial Analytical Chemistry introduces analytical chemistry skill-set that is required in different industrial disciplines including chemical, pharmaceutical, bio-analytical, forensic, food and environmental monitoring applications.

The practice of analytical chemistry has now become an integral and essential component in many diverse spheres such as Food & Beverage industry, Pharmaceutical industry, Health care & medical technology, Environmental control, Electrochemical technology, Agriculture, etc. Many of the underlying principles of Analytical Chemistry can be seen routinely in analysis in many areas such as quality control, research and development work, manufacturing processes, and in industry, university, and other research laboratories.

The objective of the M.Sc./Diploma program in Industrial Analytical Chemistry is to deliver the analytical chemistry skill-set involved in different industrial disciplines for young graduates as well as for working professionals.

The programme comprises a broad range of modules covering all the major analytical techniques, complemented by studies in transferable and professional skills, with the option to study aspects of medicinal and pharmaceutical chemistry if desired.

Study areas include research methods, separation techniques, mass spectrometry and associated techniques, spectroscopy and structural analysis, sensors, pharmacokinetics and drug metabolism, drug targets, drug design and drug synthesis and innovations in analytical science.

Objective/ Graduate Profile

Bu following this programme  student will have,

  • Ability to understand clearly the nature of a given problem, use a variety of analytical methodologies and techniques – both classical and instrumental – to obtain accurate and precise measurements of the system,
  • Operate and maintain analytical instruments,
  • Calculate the final result together with the associated error, a
  • Interpret the results to arrive at appropriate conclusions.
  • A strong background in chemistry with a basic training in analytical aspects, a sound hands-on experience together with advanced principles of analytical chemistry
  • Experience in nanopore technologies, separation science and the latest techniques in mass spectrometry.

Admission Requirements:

Applicants should possess one of the following degrees from any recognized university.

  • Sc. in Sciences including Chemistry as a subject
  • Sc. in Pharmacy
  • Pharm
  • Five years industrial experience with relevant qualifications

Proficiency in English language is mandatory.

A)     Post Graduate diploma in Industrial Analytical chemistry

Duration of the Course:

Post Graduate Diploma – one year

Description of the Course

Course Code Course Title Lecture/lab hours Credit

value

First Year
Taught Components 435 29
IAC 501 3.0 Analytical principles and instrumentation I 3
  Acid/base, precipitation, redox, complexometric equilibria & titrations 15
  Sampling techniques and data analysis/statistics 15
  Elementary electronics and optics 15
 
IAC 502 3.0 Analytical principles and instrumentation II 3
  Spectroscopic techniques 45
 
IAC 503 3.0 Analytical principles and instrumentation III 3
  Chromatographic  techniques 15
  Surface analytical  techniques 15
  Electro analytical  techniques 15
IAC 504 3.0 Analytical techniques in biomedical and pharmaceutical applications  I 3
Bio-molecules analysis 15
Pharmaceutical analysis 15
Analysis and standardization of pharmaceutical and herbal products 15
IAC 505 2.0 Analytical techniques in biomedical and pharmaceutical applications  II 2
Computational drug design & discovery 15
Laboratory diagnosis of disease 15
IAC 506 2.0 Forensic analytical chemistry and applications 2
Analytical techniques in forensic toxicology 15
Serology/DNA and trace analysis 15
IAC 507 2.0 Analytical techniques in agriculture and food science 2
  Soil and agrochemical analysis 15
  Analytical tools to assure food quality 15
 
IAC 508 3.0 Material characterization and analysis I 3
Analytical chemistry of polymers and colloids 15
Analytical techniques in ceramics 15
Analytical tools in fabric technology 15
IAC 509 2.0 Material characterization and analysis II 2
Analytical tools for nano-science 15
Petrochemical analysis 15
IAC 510 2.0 Environmental analysis I 2
  Analytical tools in water and air quality 15
  Waste management 15
 
IAC 511 2.0 Environmental analysis II 2
  Green chemistry 15
  Cleaner production 15
 
IAC 512 2.0 Taught Component – auxiliary 2
  Industrial  and Quality Management 15
  ISO 9001.2008 and ISO 14000 standards and applications 15
Non taught component
IAC 514 4.0 Laboratory Practicals 1804 4

Evaluation:

Method of Assessment

It is necessary to maintain 80% or higher attendance for theory and practical sessions to become eligible to sit for the examination.

Each lecture course unit is assessed and graded by a theory examination. Laboratory practical sessions are graded through continuous assessments. Seminars will be assessed based on the content of the presentation and the way candidates handle the questions from the audience. Final research project report will be evaluated by two faculty members separately and the viva will be graded by a board of lecturers. Each case study report will be graded by two academic staff members.

Candidates should maintain a minimum GPA of  3.00 (Grade B) and must maintain a minimal C+ grade for both taught and non-taught components of the first academic year to qualify for the second year of the M.Sc. program.

Repeat Examination

Candidates in the M.Sc./Diploma program have the opportunity to request a repeat examination for a theory paper (only for the courses under the taught component). The Maximum grade that could be obtained for a repeat course unit is “B”. If a student obtains a lower grade at a repeat attempt than the grade received earlier, the better grade will be used to calculate the GPA. Repeat examinations will be conducted at the same time with the next M.Sc./Diploma batch. Once the program is started, it has to be finished within 5 years.

Postponement of Examinations

Examinations can be postponed only for a valid medical reason. In such situations, a candidate has to wait for one year to sit for the examination with the next M.Sc./Diploma batch.

B)     MSc

Note: The admission requirements are the same as for the PG Diploma which is given under the Diploma Programs in this Handbook.

Duration: 2 years

Course Description:

Course Code Course Title Lecture/lab hours Credit

value

First Year
Taught Components 435 29
IAC 501 3.0 Analytical principles and instrumentation I 3
  Acid/base, precipitation, redox, complexometric equilibria & titrations 15
  Sampling techniques and data analysis/statistics 15
  Elementary electronics and optics 15
 
IAC 502 3.0 Analytical principles and instrumentation II 3
  Spectroscopic techniques 45
 
IAC 503 3.0 Analytical principles and instrumentation III 3
  Chromatographic  techniques 15
  Surface analytical  techniques 15
  Electro analytical  techniques 15
IAC 504 3.0 Analytical techniques in biomedical and pharmaceutical applications  I 3
Bio-molecules analysis 15
Pharmaceutical analysis 15
Analysis and standardization of pharmaceutical and herbal products 15
IAC 505 2.0 Analytical techniques in biomedical and pharmaceutical applications  II 2
Computational drug design & discovery 15
Laboratory diagnosis of disease 15
IAC 506 2.0 Forensic analytical chemistry and applications 2
Analytical techniques in forensic toxicology 15
Serology/DNA and trace analysis 15
IAC 507 2.0 Analytical techniques in agriculture and food science 2
  Soil and agrochemical analysis 15
  Analytical tools to assure food quality 15
 
IAC 508 3.0 Material characterization and analysis I 3
Analytical chemistry of polymers and colloids 15
Analytical techniques in ceramics 15
Analytical tools in fabric technology 15
IAC 509 2.0 Material characterization and analysis II 2
Analytical tools for nano-science 15
Petrochemical analysis 15
IAC 510 2.0 Environmental analysis I 2
  Analytical tools in water and air quality 15
  Waste management 15
 
IAC 511 2.0 Environmental analysis II 2
  Green chemistry 15
  Cleaner production 15
 
IAC 512 2.0 Taught Component – auxiliary 2
  Industrial  and Quality Management 15
  ISO 9001.2008 and ISO 14000 standards and applications 15
Non taught component
IAC 514 4.0 Laboratory Practicals 180 4
Total (1st Year) 615 33
Second Year
IAC 514 2.0 Seminars 180 4
IAC 515 20.0 Research project based on industrial problem 1 year 20
IAC 516 7.0 Case Studies 7
IAC 517 1.0 Academic skills- Scientific writing and academic presentation 15 (2 day workshop) 1
Total (Second Year) 30

 

Evaluation and minimum requirement for the award of M.Sc. Degree

Criteria for Awarding the Degree

Qualifying for the M.Sc.

Students should maintain following minimum grade point (GP) requirements with overall GPA of 3.00 to obtain the M.Sc. degree in Industrial Analytical Chemistry.

Section Minimum GP
Taught components (for individual component) 2.30
Laboratory Continuous Assessments 2.30
Final year research project 3.00
Seminar 2.30
Case study 2.30

 

Special merit pass will be awarded for candidates with above minimal GPA requirements and GPA of 4.00 or higher.

Qualifying for the Diploma

Candidates who meet the minimal GP requirements (as shown in above table) and did not meet the minimal overall GPA requirement (GPA =3.00) will be eligible to obtain a Diploma in Industrial Analytical Chemistry.

Candidates who maintain the minimal GP and GPA requirements for M.Sc. are also eligible to terminate the program by applying for the diploma at the end of the first year.

Applied Statistics

Statistics is an extremely diverse discipline, which has applications in almost every scientific field. It offers a wide range of career opportunities in many fields such as Agriculture, Education, Medicine, Biological Sciences, Economics, and Marketing etc. Professionals in such fields require sound knowledge in applied statistics in order to solve their statistical problems in practical situations. By studying statistics, they gain knowledge in understanding, designing and carrying out statistical research in abroad range of fields.

A)Postgraduate Certificate in Applied Statistics

The Graduate Certificate in Applied Statistics is designed to train students interested in modern statistical techniques associated with their workplace requirements. This course is practically based and provides strong statistical knowledge able to be applied to the individual circumstances and needs of each student. The knowledge gained is suited to application in a range of occupations from health and science to senior secondary teachers, and researchers in fields as broad as finance and the social sciences.

Students are introduced to a statistical package in the first subject and this is used extensively in all subsequent subjects to solve more advanced problems.
Objective / Graduate Profile

  • A Basic knowledge of the principles of statistical inference, probability theory, random processes and design;
  • An understanding the application of statistical modeling to systems,
  • The design of experiments and surveys
  • Analysis and interpretation of experimental and observational data, including multivariate analysis and the analysis of time series;
  • The ability to incorporate the results of a technical analysis into a clearly written report form that may be understood by a non-specialist.

Admission Requirements:

Bachelors Degree in statistics and/or mathematics from a recognized university or equivalent institution

or

Bachelors Degree from a recognized university in any field of study with work experience in applied statistics if they have adequate mathematical knowledge.

Course Duration: One Year

Course Description: Total number of credits is 20 (all the course units in Part 1).

Part 1

Course Code Course Title Lecture/lab hours Credit value
STA 501 1.0

STA 502 1.0

STA 503 1.0

STA 504 1.5

STA 505 1.0

STA 506 2.0

STA 507 1.0

STA 508 1.5

STA 509 2.0

STA 510 2.0

STA 511 2.0

STA 512 1.5

STA 513 1.5

STA 514 1.0

Elements of Probability

Descriptive Statistics

Distribution Theory

Sample Surveys 1

Statistical Inference 1

Data Analysis 1

Nonparametric Statistics

Medical Statistics

Linear Regression Analysis

Design and Analysis of Experiments

Categorical Data Analysis

Multivariate Analysis 1

Survival Analysis

Fundamentals of Actuarial Statistics

15.0

15.0

15.0

22.5

15.0

30.0

15.0

22.5

30.0

30.0

30.0

22.5

22.5

15.0

1.0

1.0

1.0

1.5

1.0

2.0

1.0

1.5

2.0

2.0

2.0

1.5

1.5

1.0

Evaluation:

A candidate is qualified for the Postgraduate Certificate in Applied Statistics if he/she has obtained

  • GP 1 or above (minimum D grade) for all the course units in Part 1 (worth 20 credits in total)

and

  • The cumulative GPA of 2.0 or above for all the course units in Part 1 (worth 20 credits in total)

 B)Postgraduate Diploma in Applied Statistics

The Postgraduate Diploma in Applied Statistics will aim to train you to solve real-world statistical problems. When completing the course you should be able to choose an appropriate statistical method to solve a given problem of data analysis and communicate your results clearly and succinctly. The department will aim to equip you with the computational skills to carry through the analysis and answer the problem as presented.

Objective/ Graduate Profile

  • A deeper knowledge of the principles of statistical inference, probability theory, random, Processes and design;
  • An understanding of how these principles are applied to the statistical modelling of systems, the design of experiments and surveys and the analysis and interpretation of experimental and observational data, including multivariate analysis and the analysis of time series;
  • Experience in the use of a high level computing package with programming capability for statistical data analysis.
  • The ability to incorporate the results of a technical analysis into a clearly written report form that may be understood by a non-specialist.

Admission Requirements:

Bachelors Degree in statistics and/or mathematics from a recognized university or equivalent institution

or

Bachelors Degree from a recognized university in any field of study with work experience in applied statistics if they have adequate mathematical knowledge.

Course Duration: One Year

Course Description: Total number of credits is 25 (all the course units in Part 1 and 2).

Part 1

Course Code Course Title Lecture/lab hours Credit value
STA 501 1.0

STA 502 1.0

STA 503 1.0

STA 504 1.5

STA 505 1.0

STA 506 2.0

STA 507 1.0

STA 508 1.5

STA 509 2.0

STA 510 2.0

STA 511 2.0

STA 512 1.5

STA 513 1.5

STA 514 1.0

Elements of Probability

Descriptive Statistics

Distribution Theory

Sample Surveys 1

Statistical Inference 1

Data Analysis 1

Nonparametric Statistics

Medical Statistics

Linear Regression Analysis

Design and Analysis of Experiments

Categorical Data Analysis

Multivariate Analysis 1

Survival Analysis

Fundamentals of Actuarial Statistics

15.0

15.0

15.0

22.5

15.0

30.0

15.0

22.5

30.0

30.0

30.0

22.5

22.5

15.0

1.0

1.0

1.0

1.5

1.0

2.0

1.0

1.5

2.0

2.0

2.0

1.5

1.5

1.0

 Part 2

Course Code Course Title Lecture/lab hours Credit value
STA 515 1.5

STA 516 2.0

STA 517 1.5

Time Series Analysis

Data Analysis 2

Industrial Statistics

22.5

30.0

22.5

1.5

2.0

1.5

 Evaluation:

  1. A candidate is qualified for the Postgraduate Diploma in Applied Statistics if he/she has obtained
  • GP 1 or above (minimum D grade) for all the course units in Part 1 and 2 (worth 25 credits in total)

and

  • The cumulative GPA of 2.0 or above for all the course units in Part 1 and 2 (worth 25 credits in total)

MSc in Applied Statistics

The MSc in Applied Statistics will aim to train you to solve real-world statistical problems. When completing the course you should be able to choose an appropriate statistical method to solve a given problem of data analysis and communicate your results clearly and succinctly. The course aims to equip you with the computational skills to carry through the analysis and answer the problem as presented.

The MSc is designed to provide a broad but high-level training in applied statistics, computational statistics and statistical methods. These topics are taught through mathematically demanding lectures and problems classes. There is extensive hands-on experience of analysis of real data through practical classes.  Assessment is through submitted practical reports and two examinations. There is also a dissertation on an applied project..

Objectives/ Graduate Profiles

  • Ability to understand clearly the nature of a given problem, use a variety of analytical methodologies and techniques – both classical and instrumental – to obtain accurate and precise measurements of the system,
  • Operate and maintain analytical instruments,
  • Calculate the final result together with the associated error, a
  • Interpret the results to arrive at appropriate conclusions.
  • A strong background in chemistry with a basic training in analytical aspects, a sound hands-on experience together with advanced principles of analytical chemistry
  • Experience in nanopore technologies, separation science and the latest techniques in mass spectrometry.

 C )M. Sc. in Applied Statistics (Plan A: coursework and research)

Admission Requirements:

Bachelors Degree in statistics and/or mathematics from a recognized university or equivalent institution

or

Bachelors Degree from a recognized university in any field of study with work experience in applied statistics if they have adequate mathematical knowledge.

Course Duration: Two Years

Course Description: Total number of credits is 60: 40 credits for lectures (all the course units in Part 1, 2 and 3) and 20 credits for the research project (Part 4).

Part 1

Course Code Course Title Lecture/lab hours Credit value
STA 501 1.0

STA 502 1.0

STA 503 1.0

STA 504 1.5

STA 505 1.0

STA 506 2.0

STA 507 1.0

STA 508 1.5

STA 509 2.0

STA 510 2.0

STA 511 2.0

STA 512 1.5

STA 513 1.5

STA 514 1.0

Elements of Probability

Descriptive Statistics

Distribution Theory

Sample Surveys 1

Statistical Inference 1

Data Analysis 1

Nonparametric Statistics

Medical Statistics

Linear Regression Analysis

Design and Analysis of Experiments

Categorical Data Analysis

Multivariate Analysis 1

Survival Analysis

Fundamentals of Actuarial Statistics

15.0

15.0

15.0

22.5

15.0

30.0

15.0

22.5

30.0

30.0

30.0

22.5

22.5

15.0

1.0

1.0

1.0

1.5

1.0

2.0

1.0

1.5

2.0

2.0

2.0

1.5

1.5

1.0

 Part 2

Course Code Course Title Lecture/lab hours Credit value
STA 515 1.5

STA 516 2.0

STA 517 1.5

Time Series Analysis

Data Analysis 2

Industrial Statistics

22.5

30.0

22.5

1.5

2.0

1.5

Part 3

Course Code Course Title Lecture/lab hours Credit value
STA 518 2.0

STA 519 2.0

STA 520 2.0

STA 521 1.5

 

STA 522 2.0

STA 523 1.5

STA 524 2.0

STA 525 2.0

Generalized Linear Models

Sampling Theory

Data Mining

Advanced Probability and Distribution Theory

Advanced Statistical Inference

Multivariate Statistics 2

Statistical Computing

Design and Analysis of Experiments 2

30.0

30.0

30.0

22.5

 

30.0

22.5

30.0

30.0

2.0

2.0

2.0

1.5

 

2.0

1.5

2.0

2.0

Part 4

Course Code Course Title Lecture/lab hours Credit value
STA 526 20.0 Research Project 300.0 20.0

Evaluation:

A candidate is qualified for the M.Sc. degree (Plan A: by coursework and research) in Applied Statistics if he/she has obtained

  • GP 1 or above (minimum D grade) for all the course units in Part 1, 2 and 3 (worth 40 credits in total)

and

  • The cumulative GPA of 3.0 or above for all the course units in Part 1, 2 and 3 (worth 40 credits in total)

and

  • GP 3 or above for the research project (worth 20 credits).

D)M. Sc. in Applied Statistics (Plan B: by coursework)

Admission Requirements:

Bachelors Degree in statistics and/or mathematics from a recognized university or equivalent institution

or

Bachelors Degree from a recognized university in any field of study with work experience in applied statistics if they have adequate mathematical knowledge.

Course Duration: 1.5 Years

Course Description: Total number of credits is 30 (all the course units in Part 1 and 2 and course units worth 5 credits in total chosen from Part 3).

Part 1

Course Code Course Title Lecture/lab hours Credit value
STA 501 1.0

STA 502 1.0

STA 503 1.0

STA 504 1.5

STA 505 1.0

STA 506 2.0

STA 507 1.0

STA 508 1.5

STA 509 2.0

STA 510 2.0

STA 511 2.0

STA 512 1.5

STA 513 1.5

STA 514 1.0

Elements of Probability

Descriptive Statistics

Distribution Theory

Sample Surveys 1

Statistical Inference 1

Data Analysis 1

Nonparametric Statistics

Medical Statistics

Linear Regression Analysis

Design and Analysis of Experiments

Categorical Data Analysis

Multivariate Analysis 1

Survival Analysis

Fundamentals of Actuarial Statistics

15.0

15.0

15.0

22.5

15.0

30.0

15.0

22.5

30.0

30.0

30.0

22.5

22.5

15.0

1.0

1.0

1.0

1.5

1.0

2.0

1.0

1.5

2.0

2.0

2.0

1.5

1.5

1.0

 

Part 2

Course Code Course Title Lecture/lab hours Credit value
STA 515 1.5

STA 516 2.0

STA 517 1.5

Time Series Analysis

Data Analysis 2

Industrial Statistics

22.5

30.0

22.5

1.5

2.0

1.5

 

Part 3

Course Code Course Title Lecture/lab hours Credit value
STA 518 2.0

STA 519 2.0

STA 520 2.0

STA 521 1.5

 

STA 522 2.0

STA 523 1.5

STA 524 2.0

STA 525 2.0

Generalized Linear Models

Sampling Theory

Data Mining

Advanced Probability and Distribution Theory

Advanced Statistical Inference

Multivariate Statistics 2

Statistical Computing

Design and Analysis of Experiments 2

30.0

30.0

30.0

22.5

 

30.0

22.5

30.0

30.0

2.0

2.0

2.0

1.5

 

2.0

1.5

2.0

2.0

 

Evaluation:

A candidate is qualified for the M.Sc. degree (Plan B: by coursework) in Applied Statistics if he/she has obtained

  • GP 1 or above (minimum D grade) for each of the all the course units in Part 1 and 2 (worth 25 credits in total), and also for course units worth 5 credits in total chosen from Part 3

and

  • The cumulative GPA of 3.0 or above for all the course units in Part 1 and 2 (worth 25 credits in total), and also for course units worth 5 credits in total chosen from Part 3

 

Computer Science

Admission Requirements

Bachelor’s Degree from a recognized university or any other equivalent qualification in the field of Computer Science or ICT that would be acceptable to Faculties of Applied Sciences, Graduate Faculty and the University Senate.

A good working knowledge of English is a must

Course Duration: 2 years

 Course Description:

Course Code Course Title Lecture/lab hours Credit

value

Foundation Courses
CSC 501 0.0 Mathematics for Computing 0.0 23
CSC 502 0.0 Statistics for Computing 0.0 22
CSC 503 0.0 Computer Application Laboratory 0.0 15
CSC 601 0.0 Academic Writing 0.0 15
Total 0.0 75
First Year
CSC 504 1.5 Computer Architecture 1.5 23
CSC 505 3.0 Computer Programming 3.0 45
CSC 506 1.0 Computer Programming Laboratory 1.0 15
CSC 507 1.0 Object Oriented Analysis and Design 1.0 15
CSC 508 1.5 Operating Systems 1.5 23
CSC 509 1.5 Software Engineering 1.5 22
CSC 510  2.0 Database Management Systems                2.0 30
CSC 511 1.5 Computer Networks 1.5 22
CSC 512 2.0 Data Structures and Algorithms 2.0 30
CSC 513 2.0 Web Programming 2.0 30
CSC 514 1.5 Computer Graphics and Animations 1.5 22
CSC 515 1.5 Mobile Computing                1.5 23
Total 20.0 300
Second Year
CSC 602 2.0 Artificial Intelligence 2.0 30
CSC 603 2.0 Nature Inspired Algorithms 2.0 30
CSC 604 2.0 E- commerce 2.0 30
CSC 605 2.0 Digital Image Processing 2.0 30
CSC 606 2.0 Multimedia Technology 2.0 30
CSC 607 2.0 Computer Security 2.0 30
CSC 608 2.0 Bioinformatics 2.0 30
Total 10.0 150
  M.Sc Thesis 30.0 450

 

Evaluation:

A) Completion of the MSc program

To award the MSc degree, a candidate should obtain

  1. A GPA of not less than 3.0 for all credit course units at the end of the third semester of the program and
  2. A ‘B’ or higher grade for the MSc thesis and
  3. A ‘C’ or higher grade for each foundation course unit.

 

B) Completion of the PG Dip program

To award the postgraduate diploma, a candidate should obtain

  1. A GPA of not less than 2.0 for all credit course units at the end of the third semester of the program and
  2. A ‘C’ or higher grade for each foundation course unit except CSC 601 0.0.

Any student who fulfils the requirements given in B) has the option of exiting at the postgraduate diploma level and obtaining the Postgraduate Diploma in Computer Science. However, such student is not allowed to proceed for the Masters Program, i.e. No student can receive both Postgraduate Diploma in Computer Science and the Masters Degree in Computer Science from the University of Sri Jayewardenepura.

Polymer Science and Technology

Admission Requirements

B.Sc. Degree with Chemistry or B. Sc. Degree with Chemical Engineering  or an equivalent qualification from a recognized higher education institution or an equivalent professional qualification.

Special Enrollment for a limited number of candidates with NDT Certificate and 5 years of working experience in executive capacity in rubber and plastics sector.

Duration: 2 years

Course Description –

Course Code Course Title lecture/lab hours Credit value
First Year
Module 1: Polymer Science
MPST 501 2.0 Introduction to polymers 30 2
MPST 502 1.0 Degradation and Stabilization of polymers 15 1
MPST 513 1.0 Identification and Analysis of Polymers 15 1
MPST 512 1.0 Industrially Important Polymers 15 1
MPST 532 1.0 Rubber based Industries in Sri Lanka 15 1
Module 2: Polymer Technology
MPST 508 1.0 Rubber Technology 30 2
MPST 509 1.0 Latex Technology 15 1
MPST 510 2.0 Plastic Technology 30 2
MPST 511 1.0 Paints, Colloids and Surface coatings 15 1
Module 3: Polymer Physics
MPST 503 1.0 Polymer physics 15 1
MPST 504 2.0 Polymer rheology 30 2
MPST 505 2.0 Polymer Kinetics & Thermodynamics 30 2
MPST 506 1.0 Properties and Characterization of Polymers 15 1
Module 4: Polymer Engineering
MPST 531 4.0 CAD designing 60 4
MPST 515 1.0 Process Engineering 15 1
MPST 526 1.0 Advanced materials & technology 15 1
Module 5: Modern advances in Polymer Science and technology
MPST 507 1.0 Polymer Composites and Blends 30 2
MPST 518 1.0 Environment and polymer industry 15 1
MPST 517 1.0 Cleaner production & sustainability  management 15 1
MPST 527 1.0 Modeling and simulations 15 1
MPST 514 1.0 Quality Assurance & ISO systems 15 1
Module 6: Management
MPST 519 1.0 Intellectual properties 15 1
MPST 520 1.0 Statistics 15 1
MPST 521 1.0 Industrial and operational management 15 1
MPST 522 1.0 Financial management and accounting 15 1
MPST 523 1.0 Marketing 15 1
Module 7
MPST 528 2.0 Practical  90 2
Second year
MPST 529 1.0 Seminars 2
MPST 530 20.0 Research project  

 

30

 

  (literature presentation, research ability, thesis, final presentation, and viva examination)

 

Evaluation:

Taught Component

Lectures

Lectures are categorized under six modules. A candidate’s performance at each module is assessed and graded by the theory/practical examination. Examinations of MPST 527 1.0 and MPST 531 2.0 are computer based examinations. A candidate who scores a grade C or better is considered to have passed the relevant module.

Non-taught component

    Practical (Laboratory work)

Laboratory work will be assessed on the laboratory report by the relevant lecturer.

   Seminar

Seminar will be assessed based on three categories by the relevant lecturer.

  • Content (40 marks)
  • Presentation skills (30 marks)
  • Answering questions (30 marks)

Field visits

For each field visit a scientific report needs to be submitted which will be assessed by the lecturer in charge for that field visit.`

Criteria for Awarding the Degree

Requirement for the MSc

MSc degree will be awarded when all of the following minimum conditions are fulfilled

  • GPA of 2.0 or above for all the subjects (all the taught components)
  • GPA of 2.7 or above for the for the laboratory work
  • GPA of 2.7 or above for the for the seminar and field visits
  • GPA of 3.0 or above for the combined viva-voce examination and research project.
  • Overall GPA of 3.0 or above

 

Repeat Examination

  • If the candidate obtains C- or lower (GPA ≤ 1.7) in a particular module, he/she has to sit for the same module examination, in two further occasions only as repeat candidate. If the program does not conduct the same course units again, he/she can sit for a different course unit examination instead, upon the approval of the course coordinator. However, such candidates will be considered as repeating candidates and the maximum grade given will be B (GPA of 3.0).
  • Non-taught components and research projects do not have repeat examinations and therefore the initial marks obtained for such components are taken as the final marks.

(Note: Maximum of three attempts are allowed for a candidate inclusive of the first attempt. The maximum GP value that can be obtained from a repeat attempt is 3.0.)

* Repeat candidate should pay a repeat examination fee

 

M.Phil./ Ph.D.

Medium : English

Subject areas: Chemistry, Mathematics, Physics, Statistics and Computer Science, Information System

 Ph.D :-

Entry Qualifications:

Any one of the following qualifications:

  • Sc. Special Degree in the relevant discipline with first or second class honours.
  • Phil. / M.Sc. or equivalent degree in the relevant discipline.

 M.Phil. –

Entry Qualifications:

Any one  of the following qualifications:

(a) B.Sc. Special degree in the relevant discipline.

(b) M.Sc. Degree in the relevant discipline.

(c) B.Sc. Genaral Degree or equivalent degree and a pass in the qualifying examination

(d) Professional qualification accepatable to the Board of Study and a pass in the qualifying     examination