Course Introduction

Welcome

  • Financial Technology and Data Science
  • Professor Barry Quinn
  • Ulster University Business School

Who I Am

  • Professor of Finance and Financial Technology, Ulster University Business School
  • Background: applied econometrics and financial markets; interested in how AI/ML are reshaping finance
  • Prior industry: currency trading and liquidity management
  • Teaches: quantitative finance, econometrics, and data science for finance
  • Focus:
    • Applied econometrics + ML for finance (forecasting, anomaly detection)
    • Portfolio optimisation and risk
    • Digital finance adoption/infrastructure and regulation

Who I Am

  • Emphasis: ethical data use, reproducibility, and building confidence, curiosity, and resilience
  • Software: tsfe (Time Series Econometrics), fml (Financial ML)
  • Recent work: IEEE Internet of Things Journal (2025); IEEE TEC (2024)
  • Professional: Chartered Statistician (RSS); Advanced Data Science Professional (Alliance for Data Science Professionals)
  • Office hours: weekly (see course site) · Email: b.quinn1@ulster.ac.uk

Course at a Glance

  • 12-week arc organised around a textbook-style core (chapters + labs + slides)
  • Hands‑on labs each week with “Open in Colab” notebooks
  • Chapters provide context; slides preview; labs build skills
  • See: Weekly Schedule on the site

Assessments

  • Assessment details (briefs, dates, rubrics) are module-specific.
  • Use the module page on the site for the authoritative current assessment information.

How We Work

  • Student workflow (Jupyter/Colab): run notebooks, tweak parameters, explain results
  • Publishing workflow (Quarto): course chapters + slides for reproducible reference
  • Pinned environments; deterministic seeds; evidence‑based claims

Expectations : From You

  • Prepare weekly: skim chapter, open notebook, run top‑to‑bottom
  • Practise actively: modify code, document what changed and why
  • Ask early: use office hours and seminars
  • Academic integrity: cite sources and be transparent about methods

Expectations : From Me

  • Clear structure, runnable examples, and timely feedback
  • Transparent rubrics and realistic, assessment‑aligned tasks
  • Support in seminars/clinics and office hours
  • Professional standards with approachable delivery

Resources

  • Students → Student Guide; Getting Started (Colab/Codespaces)
  • Labs → Colab badges for one‑click notebooks
  • Slides → Decks for each week
  • Schedule → Week‑by‑week topics and checkpoints

Contact

  • Email: b.quinn1@ulster.ac.uk
  • Office hours: see course site
  • Course site: https://quinfer.github.io/financial-data-science

Welcome Aboard

Let’s build practical, evidence‑based skills that transfer beyond this module.