Prog Card
Master of Data Science
Course Fee
MVR 4,599 per month x 15 months
Programme Details
Introduction
The Master of Data Science (MDS) Programme is designed to provide students with the advanced skills and knowledge required to excel in the dynamic field of data science. The curriculum covers a wide range of modules through which students will gain expertise in programming, machine learning, and data management, and will prepare them to tackle complex challenges in various industries. Graduates of this programme will emerge as skilled data scientists capable of handling complex data challenges, making informed decisions and driving innovation across various industries. They will be well-equipped to shape the future through data-driven insights and contribute to the evolving landscape of data science.
This programme provides the necessary skills required for data analysis, data manipulation, writing efficient code, and implementing algorithms. Students learn to build predictive models, understand feature selection, and evaluate model performance. The programme develops students’ analytical thinking and equips them with tools to design algorithms that can handle, integrate and store large and complex datasets and cloud-based technology for big data. It also incorporates a master’s level data science project in which students apply their knowledge to a real-world project, demonstrate their ability to tackle complex data challenges, make informed decisions, and communicate findings, thereby solidifying their skills as professional data scientists.
General Entry Criteria
A related Bachelor’s Degree OR
A related Graduated Diploma at level 8
Alternative Entry Criteria
25 years old AND
Completion of a related Level 5 qualification AND
7 years of relevant experience (of which 3.5 years of experience is obtained after completion of a Level
5 qualification) AND
completion of an MQA approved Pre-Master’s Programme. OR
25 year old AND
Completion of a related Level 6 qualification AND
6 years of relevant experience (of which 3 years of experience is obtained after completion of a Level 6 qualification) AND
Completion of an MQA approved Pre-Master’s Programme
Core Modules
Programming with Python
Data Management
Algorithmic Data Science
Machine Learning and Predictive Analysis
Exit with a Postgraduate Certificate in Data Science
Research Methodology
Big Data Analytics
Cloud Computing
Data Analysis and Visualization
Master Data Science Project
Award Master of Data Science
The broader goals of the programme are as follows:
To equip students with a comprehensive understanding of data science principles, techniques, and methodologies, enabling them to proficiently analyse and interpret complex datasets.
To produce graduates with a strong sense of ethical responsibility in handling data, ensuring that they are well-versed in privacy regulations, bias mitigation, and other ethical considerations.
To collaborate effectively across interdisciplinary teams, enabling graduates to translate data insights into actionable solutions that address real-world challenges in various industries.
To inspire a mindset of continuous learning and innovation, encouraging graduates to stay updated with evolving data science trends.
- Course Fee: MVR 4,599 per month x 15 months
Upon successful completion of this programme, students will be able to:Â
Demonstrate a deep understanding of data science principles, methodologies, and techniques to effectively analyse, interpret, and draw insights from complex datasets.
Apply machine learning techniques to solve complex problems, and carry out predictive modelling, classification, clustering, and regression.
Employ distributed computing frameworks and big data technologies to process, analyse, and extract valuable insights from large-scale datasets.
Create data visualizations and effectively communicate insights, enabling informed decision-making.
Demonstrate ethical responsibility in handling data, considering privacy concerns and ensuring data security.
Apply data mining techniques to uncover hidden patterns, relationships, and trends within diverse datasets, and contribute to strategic decision-making.
Convey technical findings and insights to both technical and non-technical audiences, demonstrating clear and concise communication skills