Duration: 24 months

Entry requirements:

Minimum of 5 Ordinary Levels including Mathematics and English or

Certificate in Data Science

Course Overview

The Diploma in Data Science is a robust and comprehensive two-year program that equips students with the essential knowledge, technical skills, and analytical capabilities required to excel in the rapidly evolving field of data science. Spanning over eight trimesters, the program offers a meticulously crafted curriculum that delves deep into the core pillars of data science, enabling students to become proficient in the latest tools, techniques, and methodologies.

In the first year, students will embark on a foundational journey, beginning with Advanced Python Programming for Data Science, where they will master the art of Python, the primary programming language for data science. They will then explore the intricacies of Data Preprocessing and Feature Engineering, learning to transform raw data into meaningful insights. The curriculum then progresses to Supervised Learning Algorithms and Unsupervised Learning and Clustering, equipping students with the skills to apply cutting-edge machine learning techniques to solve complex problems.

The second trimester focuses on deepening the students’ understanding of statistical methods, neural networks, natural language processing, and time series analysis. Courses like Advanced Statistical Methods for Data Science, Deep Learning and Neural Networks II, Natural Language Processing and Text Mining, and Time Series Forecasting and Analysis will empower students to tackle a wide range of data-driven challenges.

In the third trimester, the program delves into more advanced topics, including Reinforcement Learning, Image and Video Processing for Data Science, Graph Analytics and Network Science, and Advanced Data Visualization and Storytelling. These courses will enable students to develop a comprehensive understanding of cutting-edge techniques and their applications in diverse domains.

The fourth trimester introduces students to the realm of Big Data, Cloud Computing, Causal Inference, and Optimization Techniques, preparing them to tackle large-scale data challenges and leverage the power of distributed computing systems.

In the second year, the curriculum further expands, covering advanced topics such as Bayesian Machine Learning, Anomaly Detection and Fraud Analytics, Reinforcement Learning in Robotics, Advanced Natural Language Understanding, Advanced Deep Learning Architectures, Advanced Data Mining and Pattern Recognition, Social Network Analysis and Community Detection, and Data Science for the Internet of Things (IoT). These specialized courses empower students to become experts in their chosen areas of focus.

The program culminates with the Data Science Capstone Project, where students have the opportunity to apply their accumulated knowledge and skills to tackle a real-world data science challenge, demonstrating their ability to deliver impactful data-driven solutions.

The Diploma in Data Science is designed to cultivate a deep understanding of data science principles, foster critical thinking and problem-solving skills, and equip students with the necessary tools and techniques to drive innovation and transformation in a data-driven world.

Year 1 Trimester 1

Code	Description	Credit
DS1201	Advanced Python Programming for Data Science	12
DS1202	Data Preprocessing and Feature Engineering	12
DS1203	Supervised Learning Algorithms	12
DS1204	Unsupervised Learning and Clustering	12

Year 1 Trimester 2

Code	Description	Credit
DS1205	Advanced Statistical Methods for Data Science	12
DS1206	Deep Learning and Neural Networks II	12
DS1207	Natural Language Processing and Text Mining	12
DS1208	Time Series Forecasting and Analysis	12

Year 1 Trimester 3

Code	Description	Credit
DS1209	Reinforcement Learning	12
DS1210	Image and Video Processing for Data Science	12
DS1211	Graph Analytics and Network Science	12
DS1212	Advanced Data Visualization and Storytelling	12

Year 1 Trimester 4

Code	Description	Credit
DS1213	Big Data Processing and Analytics	12
DS1214	Cloud Computing for Data Science	12
DS1214	Causal Inference and Experimental Design	12
DS1215	Optimisation Techniques for Data Science	12

Year 2 Trimester 1

Code	Description	Credit
DS1301	Bayesian Machine Learning	12
DS1302	Anomaly Detection and Fraud Analytics	12
DS1303	Reinforcement Learning in Robotics	12
DS1304	Advanced Natural Language Understanding	12

Year 2 Trimester 2

Code	Description	Credit
DS1305	Time Series Forecasting with Deep Learning	12
DS1306	Advanced Data Wrangling and Feature Extraction	12
DS1307	Explainable AI and Interpretability	12
DS1308	Privacy-Preserving Data Analysis	12

Year 2 Trimester 3

Code	Description	Code
DS1309	Advanced Deep Learning Architectures	12
DS1310	Advanced Data Mining and Pattern Recognition	12
DS1311	Social Network Analysis and Community Detection	12
DS1312	Data Science for Internet of Things (IoT)	12

Year 2 Trimester 4

Code	Description	Credit
DS1313	Advanced Reinforcement Learning	12
DS1314	Large-Scale Data Processing and Distributed Computing	12
DS1315	Ethics and Responsible AI in Data Science	12
DS1316	Data Science Capstone Project	12