Data Science · Advanced level

Advanced level · Module 5 of 6 · 24 guided hours

Data Governance and Responsible Data Science

Manage data quality, documentation, reproducibility and responsible sharing so that analyses can be trusted.

Learning objectives

  • Apply data governance roles and quality management
  • Document data and analyses thoroughly
  • Ensure reproducibility and responsible data sharing

Topics

  • governance roles and responsibilities
  • data quality dimensions
  • data dictionaries and datasheets
  • version control and environments
  • code review for analysis
  • responsible and open data sharing

Lessons

  1. Lesson 1 Data Governance and Quality

    Define who is responsible for data and measure and improve its quality.

    data owner and stewardquality dimensionsquality checksissue log

  2. Lesson 2 Documenting Data and Analyses

    Document data sets and analyses so others can understand, trust and reuse them.

    data dictionarydatasheet for data setsmethods notedecision log

  3. Lesson 3 Reproducibility and Review

    Make analyses reproducible with version control and managed environments, and review analysis code.

    Git for analysisenvironment filesrandom seedsanalysis code review

  4. Lesson 4 Responsible Data Sharing and Open Data

    Share data and results to maximise benefit while protecting people and respecting agreements.

    open datarestricted accessdisclosure risk assessmentlicences

Practical activity

Audit a provided analysis project for governance, documentation and reproducibility, fix its problems and prepare a responsible data release.

Expected outcomes

  • Trustworthy, well-documented analyses
  • Governed, responsible data practices

Assessment

Governance audit report and the corrected, documented project.

Taught live by an instructor — online or in person. Free course lessons are for self-study and do not lead to a certificate — see registration options.