CS266-15 Data Analytics

Introductory description

This module provides a gateway for students to understand the fundamental principles, techniques, and tools that underpin the practice of data analytics, empowering them to make decisions based on analytical insights. It also serves as a valuable resource for students undertaking their 3rd-year projects, particularly those delving into the realms of machine learning and data mining.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

• Data analytic tools (e.g. AutoML, Python, R, SQL, R, Tableau, Excel)
• Data cleaning, data visualisation
• Time series, spatial-temporal learning, trajectory, and data flow analysis
• Anomaly, fraud, and malware detection
• Uncertain data (e.g. probabilistic data, fuzzy data), anonymization, differential privacy
• Graph data, representation learning, frequent pattern mining, link analysis
• Big data analytics, data streams, map-reduce, Hadoop, spark
• Reinforcement learning, transfer learning, federated learning (optional)

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate a comprehensive understanding of the fundamental principles, methodologies, and tools necessary for extracting valuable insights from data.
• Gain a cross-disciplinary training in data-driven models, machine learning methods, and big data mining technologies, and prepare for their 3rd-year projects in complex data-driven environments.
• Develop practical programming skills and provide students with real-world case studies to apply theoretical data analytic knowledge in data analytics to practical scenarios.

Subject specific skills

1. Employ statistical and machine learning methods to analyse data, interpret results, and draw meaningful conclusions.
2. Build and evaluate predictive models using data-driven techniques to forecast outcomes based on historical data.
3. Devise optimization methods to enhance decision-making processes and improve the efficiency of data-driven solutions.
4. Apply theoretical knowledge to solve real-world data analytic problems through hands-on case studies and practical scenarios.

Transferable skills

1. Critical Thinking: Developing the ability to analyse complex data-driven problems, evaluate data, and make informed decisions based on data-driven insights.
2. Problem-Solving: Applying analytical techniques to address real-world challenges and optimize decision-making processes.
3. Adaptability: Cultivating adaptability in dealing with diverse datasets, understanding how data analytics integrates into various domains, and contributing to interdisciplinary projects.