Introduction
This programme is designed for graduates desiring to enhance their competencies in data analytics concepts, tools, and technologies for delivering data-driven solutions to diverse business problems in the industry and society. The curriculum is specifically designed taking into consideration, current and future requirements of the industry and will be delivered by an experienced panel of experts drawn from both academia and industry.
Programme Objectives
According to the IT/BPM industry skills survey conducted by the SLASSCOM in 2018, the predicted human resource requirement in the IT/BPM industry by 2022 is 300,000. Moreover, AI/Machine Learning Specialists and Data Scientists have been recognized as top two types of technology professionals to be newly employed. On the other hand, competitive industrial sectors such as banking, insurance, retail, manufacturing, health and telecommunication, have started reviving their strategies with extensive focus on the data resource, generating a range of data related careers such as Data Scientist, Data Architect, Data/Business Analyst, Data Entry/Machine Learning Trainer, etc.
The proposed one-year Master of Data Analytics programme provides the opportunity to the graduates from various academic disciplines (both computing and non-computing related) to acquire the necessary skills in the field of big data analytics and enter into this highly rewarding job market. The specific objectives of the programme could be listed as follows.
- Developing high quality professionals to meet the growing demand for data analytics-related careers in both private and public sector organizations
- Upgrading the professionals who are already in the software industry with the basic knowledge and skills in machine learning and analytics
- Enabling non-computing graduates to secure a career in the IT industry, particularly in the analytics field
Programme Outcomes
The proposed master of data analytics programme is designed in such a way that the candidates are given the opportunity to start from the basic level and then advance their competencies in using cutting edge tools and technologies in diverse domains of applied analytics. Thus, on successful completion of the program, the candidates would be able to:
- appreciate the interdisciplinary nature of data science
- contribute effectively as a member of an analytics team
- recognize problems that can be solved with the support of analytics
- identify the data requirements and, collect, pre-process, and store the right data in right format
- design and conduct experiments using data to solve problems, as well as to analyze and interpret results
- apply knowledge of mathematics, statistics, and programming to extract meaningful patterns from data
- demonstrate a sound knowledge and understanding on the big data tools, technologies, and infrastructure
- handle data with utmost professional and ethical responsibility
- communicate effectively in written and verbal forms
- passionately continue self-study on emerging big data analytics tools, techniques, and technologies
Duration: 12 months (The duration may vary due to unexpected interruptions)
Entry Qualifications
A bachelor’s degree in Science or Engineering
OR
Bachelor’s degree in Arts, Technology, ICT or Commerce and Management, with a mathematical background* and a minimum of one years of managerial/executive/tertiary level teaching experience
OR
any equivalent qualification acceptable to the Senate of the University of Kelaniya**
* - Applicants who have done ICT, Statistics, Logic, Mathematics, Economics and/or Econometrics as a part of their bachelor’s degree programme or for GCE Advanced Level would be considered. The decision of the interview panel would be the final decision.
** - Applications of those who have diplomas/higher diplomas from recognized institutions will be considered based on their experience and other professional qualifications obtained.
Application Procedure
- Application can be submitted online through fgs.kln.ac.lk or collected from the Faculty of Graduate Studies, University of KelaniyaApplication can be submitted online through fgs.kln.ac.lk or collected from the Faculty of Graduate Studies, University of Kelaniya
- Application fee of Rs. 1000/- can be paid to any branch of People’s Bank to the credit of Account No: 055-1-001-1-0667549, People’s Bank, Kelaniya Branch
- Perfected applications should be forwarded to Senior Assistant Registrar, Faculty of Graduate Studies, University of Kelaniya, Kelaniya along with the certified copies of the following documents and the customer’s copy of the paying-in voucher.
- Certificates of educational and professional qualifications
- Proof of work experience
- Birth certificate
- Identity card/passport
- Other relevant documents (if any)
Course Fee
The all-inclusive fee for the Master of Data Analytics course would be Rs. 425,000/-, which could be paid in three installments with an initial upfront fee of Rs. 200,000 at registration. This fee includes a refundable deposit of Rs. 5000/-, which is charged for the use of the university library. The remaining installments should be paid as follows.
- Installment 2: Rs. 125,000/- after three months of the registration
- Installment 3: Rs. 100,000/- after six months of the registration
Delivery Mode
Hybrid mode. 1/3 of the lectures and all examinations will be held physically.
Program Structure
Module
|
Type
|
Trimester
|
|
|
|
Statistics for Data Science
|
Compulsory
|
1
|
Mathematics for Data Science
|
Compulsory
|
1
|
Programming for Data Science
|
Compulsory
|
1
|
|
|
|
Artificial Intelligence
|
Compulsory
|
2
|
Business Intelligence
|
Compulsory
|
2
|
Database Systems
|
Compulsory
|
2
|
|
|
|
Big Data Analytics and Machine Learning
|
Compulsory
|
3
|
Text Analytics
|
Compulsory
|
3
|
Big Data Infrastructure
|
Compulsory
|
3
|
Business Analytics Seminar
|
Compulsory
|
3
|
Contact
- Dr Chathura Rajapakse - MDA Programme Coordinator (071 3102034, 011 2914482) : chathura@kln.ac.lk
- Faculty of Graduate Studies, University of Kelaniya (011 2903953) : http://fgs.kln.ac.lk
For application
Visit: http://fgs.kln.ac.lk