Skip to content

UFCEQA 15 M

UFCEQA-15-M Process and Practice in Data Science

Overview

This module introduces proven practical frameworks for designing and managing data science projects, alongside tools and methods for innovation, personal development and the ethical evaluation of algorithmic and data-driven solutions.

Objectives

  1. Demonstrate key power skills of relevance to data science, including stakeholder communication, leadership, team working, an enterprise mindset and a commitment to sustainability
  2. Apply lifecycle process models and appropriate project management tools and methods, then evaluate and communicate results.
  3. Advocate for, and take a leading role in, transparency, good data governance, accountability, fairness and ethical conduct

Curriculum

Topics are likely to include but are not limited to:

  • Data science landscape: Contemporary and emerging tools and methods; team roles; links to business / organisational goals and strategy.

  • Professional and Employability attributes: Upskilling & reskilling; creativity; domain knowledge; portfolio building; communicating success and learning from failure.

  • Data science lifecycle processes: Cross-Industry Standard Process for Data Mining (CRISP-DM); Team Data Science Process (TDSP);

  • Agile project management: Suitability to data science; philosophy and principles; tools and methods;

  • Enterprise and innovation: Personal / organisational attributes; Innovation; design thinking ; user experience design;

  • Ethics: Philosophy and practice; fairness and bias in data science and machine learning.

Assessment

Individual Presentation (100%)