COMP7108A - Network data analytics

Semester 2, 2024-25

Professor
Chrysanthi Kosyfaki
Syllabus In the era of data, numerous real-world applications are best represented as networks. This perspective is vital as analyzing these networks can uncover valuable insights, extract interesting information, and make informed decisions. Modern technologies have significantly enhanced our ability to access vast volumes of data, simplifying and reducing the cost of storage. Understanding the importance of data is crucial in addressing diverse challenges, such as traffic congestion, financial network fraud detection, and the spread of misinformation in social networks, to name a few. Consequently, there is an increasing necessity to develop advanced tools that can address these challenges and further understand the importance of data is more necessary than ever. Examples of these technologies can be machine learning techniques (e.g., modeling different problems using GNNs), and natural language processing (NLP) techniques (text preprocessing and sentiment analysis).
Introduction by Professor  
Learning Outcomes
Course Learning Outcomes Relevant Programme Learning Outcome
CLO1. Able to learn how to use data science analysis tools  
CLO2. Able to understand graph data structure and algorithms for graph analytics  
CLO3. Able to explore provenance tracking methods on graph data  
CLO4. Able to learn flow analytics techniques on graphs and gain insights into spatiotemporal data mining using machine learning  
CLO5. Able to understand and address ethical challenges in data science and AI  
View Programme Learning Outcomes
Pre-requisites Very good knowledge of programming (Python recommended) and knowledge of fundamental data science concepts and techniques (e.g. linear algebra)
Compatibility -
Topics covered
Course Content No. of Hours Course Learning Outcomes
Review on Advanced Machine Learning topics    
Big Data Analytics    
Provenance and Graph Analytics    
Flow Analytics on Graphs and Spatiotemporal Data Mining    
Ethics and Responsible Data Science    
     
     
 
Assessment
Description Type Weighting * Tentative Assessment Period /
Examination Period ^		 Course Learning Outcomes
Assignments Continuous Assessment 50% -  
Written examination covering all the taught contents in the course Written Examination 50% 8 - 27 May 2025  
* The weighting of coursework and examination marks is subject to approval
^ The exact examination date uses to be released when all enrolments are confirmed after add/drop period by the Examinations Office.  Students are obliged to follow the examination schedule.  Students should NOT enrol in the course if they are not certain that they will be in Hong Kong during the examination period.  Absent from examination may result in failure in the course. There is no supplementary examination for all MSc curriculums in the Faculty of Engineering.
Course materials

 
Session dates
Date Time Venue Remark
Session 1 11 Feb 2025 (Tue) 3:30pm - 6:30pm CPD-LG.10  
Session 2 18 Feb 2025 (Tue) 3:30pm - 6:30pm CPD-LG.10  
Session 3 25 Feb 2025 (Tue) 3:30pm - 6:30pm CPD-LG.10  
Session 4 4 Mar 2025 (Tue) 3:30pm - 6:30pm CPD-LG.10  
Session 5 18 Mar 2025 (Tue) 3:30pm - 6:30pm CPD-LG.10  
Session 6 25 Mar 2025 (Tue) 3:30pm - 6:30pm CPD-LG.10  
Session 7 1 Apr 2025 (Tue) 3:30pm - 6:30pm CPD-LG.10  
Session 8 8 Apr 2025 (Tue) 3:30pm - 6:30pm CPD-LG.10  
Session 9 15 Apr 2025 (Tue) 3:30pm - 6:30pm CPD-LG.10  
Session 10 22 Apr 2025 (Tue) 3:30pm - 6:30pm Online Zoom
CPD - Central Podium Levels (Centennial Campus)
Add/drop 20 January, 2025 - 18 February, 2025
Maximum class size 150
Moodle course website
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