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Data Science Ethics

Explore the key ethical and legal issues as well as challenges that you might face when working on a data science project.

Data Science Ethics
  • Duration4 weeks
  • Weekly study12 hours
This course is part of the Data Analytics for Decision Making program, which will enable you to Take the first steps to becoming a highly-skilled data scientist.

This course is part of the Data Analytics for Decision Making Microcredential. On this microcredential you will:

Boost your data science career potential

Alongside world-class computer science experts from Queen Mary, on this microcredential you’ll learn the process for collating and cleansing data. You’ll discover how to interpret and communicate data to others and gain valuable insights to inform your decision-making process.

You’ll also explore the essential ethical and legal issues that need to be considered when generating, analysing, and disseminating data.

Gain data analytics certification

Ultimately, you’ll come away with the accredited skills you need to apply for roles as a data scientist, or to enhance your current organisation’s capacity to interpret and manage data to solve complex problems and predict future trends.

Syllabus

  • Week 1

    Data accuracy and validity

    • Welcome to the course

      Welcome to Data Science Ethics. Let's get started and find out more about what you can expect from this course.

    • Introduction to data ethics

      In data science, we need to be aware of the ethical implications of the data we work with. Here, we'll define the term ‘data ethics’, introduce you to the Data Ethics Framework, and highlight the importance of an ethics framework.

    • Identifying the legal requirements of data use

      As a data scientist, you'll have access to a plethora of datasets. But does this mean that you're allowed to use these resources? In these steps, we'll consider the often overlooked legal requirements of data use.

    • Ensuring your data is reliable

      Conducting data analysis that is unreliable is an ethical concern. In these steps, we'll explore how you can determine if your dataset is reliable, the concept of 'data bias' and why this is seen as a limitation within datasets.

    • Weekly wrap-up

      This will conclude the first week of the course and we will take a sneak peek of what we will be seeing in Week 2.

  • Week 2

    Data analysis ethics

    • Welcome to Week 2

      Welcome to Week 2 of the Data Science Ethics course. Let's get started and find out more about what you can expect from this week.

    • How can I ensure that my data analysis does not infringe on privacy?

      Datasets used for decision making are generally about people. In these steps we’ll explore personal vs sensitive data, privacy invading technologies, and the problems with anonymisation techniques and how to address them.

    • Are there errors in my analysis approach?

      Poor data analysis can lead to poor decisions, which in turn can have a negative impact on people’s lives. Here, we will look at the common sources of errors that can lead to poor data analysis.

    • Common fallacies

      Sometimes, a data scientist might be tempted to interpret data to fit into pre-conceived notions in a certain context. These fallacies can impact how people will interpret the data. Here we will explore some common data fallacies.

    • Weekly wrap-up

      This will conclude the second week of the course and we will take a sneak peek of what we will be seeing in Week 3.

  • Week 3

    Data visualisation ethics

    • Welcome to Week 3

      Welcome to Week 3 of the Data Science Ethics course. Let's get started and find out more about what you can expect from this week.

    • Is there a wrong way to present data?

      Data visualisation is a phase where you could end up misinterpreting results. Here, we’ll explore examples of misinterpreted visualisations and how poorly designed visualisations can be misleading and difficult to understand.

    • Deceptive visualisation techniques

      As a data scientist you need to be aware of deceptive visualisation techniques as you do not want to fall in the trap of using these unintentionally. In these steps we’ll explore the common deceptive techniques.

    • Ethical principles for visualisations

      Throughout this course, we have highlighted the importance of ethics within the different phases of a data science project. We’ll now explore some ethical principles that are frequently overlooked when creating visualisations.

    • Weekly wrap-up

      This will conclude the third week of the course, and will introduce the fourth and last week of the course, which is focused on revision of key topics.

  • Week 4

    Revision and assessment preparation

    • Welcome to Week 4

      Welcome to the fourth and final week of the first course. We will revise the contents learnt throughout the previous three weeks.

Who is the course for?

This course is part of the Data Analytics for Decision Making Microcredential. This microcredential would appeal to anyone looking to apply for roles as a data scientist, improve their current organisation’s data analysis, or looking to apply for higher-level study in data science.

Who developed the course?

Queen Mary University of London

Queen Mary University of London is an established university in London’s vibrant East End committed to high-quality teaching and research.

  • Established1887
  • LocationLondon, UK
  • World rankingTop 110Source: Times Higher Education World University Rankings 2020