Teaching

Practical Data Science (2024)

Supported students in:

  • Using industry-standard tools to transform raw data into analysis-ready formats.
  • Recognising when machine learning can add value to data analysis tasks.
  • Conducting exploratory data analysis in Python, including visualization and interpretation.
  • Designing and documenting experimental methodologies for structured data analysis.
  • Selecting appropriate models and applying simple ML techniques and feature selection.
  • Following professional standards to ensure reproducibility of analyses.

Applied Programming for Data Science (2024)

Guided students in:

  • Applying programming techniques for data parsing, cleaning, integration, and preprocessing.
  • Handling diverse data formats (CSV, JSON, XML) and addressing missing or corrupted data.
  • Using text-preprocessing techniques to convert raw natural language into usable features.
  • Experimenting with lightweight web frameworks (e.g., FastAPI) to deploy data-driven applications online.

Data Science Professional (2022)

Facilitating student learning around:

  • Applying ethical, social, privacy, and governance standards in AI and Data Science practice.
  • Critically evaluating fairness, bias, transparency, accountability, and explainability in AI/DS systems.
  • Analysing professional practice case studies and providing constructive peer assessment.
  • Communicating technical findings effectively across written and oral formats.
  • Employing research methods to analyse and justify conclusions in new professional contexts.

Software Engineering: Processes and Tools (2020)

Assisted students in:

  • Applying software engineering processes to develop user-centred solutions.
  • Practicing structured problem-solving methodologies for real-world challenges.
  • Analysing user requirements and employing modern development tools and emerging technologies.
  • Communicating software solutions to both technical and non-technical audiences.
  • Working effectively in teams to manage project deliverables.
  • Embedding integrity, cultural inclusivity, and ethical standards in software practice.

Algorithms and Programming Foundations in Python (2020)

Introduced students to:

  • Python programming fundamentals, including control structures and object-oriented programming.
  • Core data structures and algorithms (lists, stacks, queues, trees, recursion, sorting/searching).
  • Implementing algorithms in Python to solve computational problems.
  • Building problem-solving and critical thinking skills through practical coding exercises.