FIN306: Course Introduction

Financial Technology and Data Science with Bloomberg

Welcome

  • FIN306: FinTech and Disruptive Innovation Data Science
  • Level 5 (BSc FinTech, Semester 2)

Note on Module Name

The official module name in university systems is “FinTech and Disruptive Innovation” (administrative). The course content focuses on Financial Technology and Data Science with Bloomberg Terminal expertise. This reflects the module’s evolution and will be updated in university systems next academic year.

  • 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: by appointment · Email: b.quinn1@ulster.ac.uk

Course at a Glance

  • 12-week module combining statistical foundations with practical Bloomberg Terminal expertise
  • Weeks 1-3: Foundations (statistical inference, data science principles)
  • Weeks 4-6: FinTech applications (platforms, robo-advisors)
  • Weeks 7-9: Machine learning and validation
  • Weeks 10-12: Advanced topics and synthesis
  • See: Module Overview for complete schedule

Lab Structure

From Week 2 onwards, labs follow a Homework → In-Class structure:

  • Homework (Colab notebooks): Learn concepts with accessible tools
    • Complete before class
    • Accessible from any device
    • Prepares you for Bloomberg work
  • In-Class (Bloomberg Terminal): Apply with professional data
    • ~20 terminals available in Belfast Campus Bloomberg Room
    • Instructor support during sessions
    • Efficient use of limited terminal access

Week 1: Foundation lab uses Colab only (no Bloomberg yet)

Assessments

  • Coursework 1 : Responsible Data Science in FinTech (30%)
    • Week 6: Submission deadline (recorded 5-min presentation + 6–8 slides + data risk register 1–2 pp, 8–12 risks, one ZIP via Blackboard)
    • Brief on Blackboard; topic confirmation by Week 5
    • Focus: Data quality, selection bias, and responsible practice evaluation
  • Coursework 2 : Applied Data Science with Critical Reflection (70%)
    • Week 13: Due date (see Blackboard)
    • Brief released: Week 7
    • Format: Technical report (2,500 words) + completed scaffold notebook
    • Focus: Critical analysis and reflection on methodology

Full details: Module Handbook on Blackboard

How We Work

  • Student workflow:
    • Review chapter → Complete homework lab (Colab) → Attend in-class Bloomberg session
    • Run notebooks, tweak parameters, document learning
    • Ask questions early (office hours, lab sessions)
  • Course materials:
    • Chapters: Context and theory
    • Slides: Lecture previews
    • Labs: Hands-on practice (Colab + Bloomberg)
    • All available on course website

Expectations : From You

  • Come prepared: Review chapter and complete homework lab before in-class Bloomberg session
  • Practise actively: Modify code, experiment with parameters, document what changed and why
  • Ask early: Use office hours and raise questions during lab sessions
  • Academic integrity: Cite sources, explain reasoning, submit original work

Expectations : From Me

  • Clear weekly structure with runnable examples
  • Timely feedback on assessments
  • Office hours and active support during labs
  • Transparent rubrics and realistic, assessment-aligned tasks
  • Professional standards with approachable delivery

Resources

  • Course Website: https://quinfer.github.io/financial-data-science
    • All chapters, slides, and labs
    • Module-specific schedule and assessments
  • Colab Notebooks: https://github.com/quinfer/fin510-colab-notebooks/labs/
    • Homework labs (Weeks 1+)
    • One-click access via “Open in Colab” buttons
  • Bloomberg Terminal:
    • Belfast Campus Bloomberg Room (~20 terminals)
    • Bloomberg Market Concepts (BMC) certification recommended
    • In-class sessions begin Week 2
  • Module Handbook: Blackboard → Course Content → Module Handbook
    • Complete assessment details, rubrics, academic policies

Contact

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

Welcome Aboard

Let’s build practical, evidence-based skills in financial data science with professional Bloomberg Terminal expertise.

Next: Week 1 Foundations slides → Lab 0 (Colab) → Week 1 lecture