Ethical Data Science: Principles and Practice
Are you passionate about data science and seeking to build your programming experience? This module provides you with an understanding of key areas including legal regulations and ethical governance, combined with the opportunity to develop your programming skills.
Duration: 16 weeks
Starting months: November 2022
Our Ethical Data Science: Principles and Practice module will develop your understanding in the evolving area of ethical data science. You will gain an insight to data science methods within a business context and enhance your professional knowledge and skills.
You will improve your data science skill set and find solutions to data science problems by using Python and appropriate machine learning tools (Scikit-Learn). This module also covers topics including the principles of promoting trustworthy data science solutions and how you can build your confidence in the subject to advocate viable changes within a business organisation.
The course is delivered over 16 weeks using hybrid learning, this normally includes teaching sessions and self-paced online activities. Teaching will either be delivered on-campus or online.
You will have practical sessions that will provide you with the opportunity to learn new programming techniques along with building on the stages of the data science life cycle. You will develop your own predictive learning model using Scikit Learn and improve your skills in preparing data from sourcing, loading to cleaning, analysing, visualising and modelling. Python coding is undertaken in Jupyter Notebooks which is used extensively in organisations.
Lectures will focus on data science theory to support the practical work on the module including enhancing your understanding of the ethics, governance and legislation required of a professional data scientist.
You will also be expected to collaborate with your employer to undertake work related activities. If this is not possible then you will be provided with sample scenarios to complete.
Weekly learning topics may include:
- Course Introduction
- Organisational Ethos & Policies
- Problem Defining & Project Planning
- Risk Management & Governance
- Data Sourcing & Pre-processing
- Exploratory Data Analysis
- Consolidation of Learning
- Data Visualisation
- Data Anonymisation
- Planning Model Development
- Model Development & Feature Selection
- Model Development
- Model Explainability
- Model Evaluation, Validation & Monitoring
- Model Reporting & Communication
- Course Summary
Various methods of assessment will be used including portfolios, reports and a reflective diary.
You will require a good standard of maths and English, with a requirement for maths qualification at level 2 or above.
There is a short application form and you will need to provide a short personal statement as a method of assessing your reasons for applying for this module and any relevant experience.
Applicants can access student finance, pay as individuals, or your employer may be able to fund your study.