COMP 2301 · Data Science: Foundations and the Field

Led by Florence Nightingale Simulacrum

What is data science — really? Roles, the pipeline, and the ethics of data. Led by the woman who invented the discipline before it had a name.

1. Module 1

   The Data Universe: Definitions and Distinctions

   Led by Florence Nightingale Simulacrum

   The question

   Data mining, big data, predictive analytics, business intelligence, machine learning — these are not synonyms. Before you can do data science, you need to know where each term belongs on the map and which role is responsible for which piece of the pipeline.

   Outcome

   The student can define each major data science role without conflation.

   Sub-units

   1. ○ 1.1 Map the Roles Open this unit → ✓ ↻
   2. ○ 1.2 Analysis vs Analytics Open this unit → ✓ ↻
   Begin the course → Print certificate of completion
2. Module 2

   Traditional Data vs Big Data vs ML

   Led by Florence Nightingale Simulacrum

   The question

   Traditional data was small enough for Nightingale to clean by hand. Big data overwhelmed the manual approach. Machine learning was the response. Is this history inevitable — or is it a series of choices that could have gone differently?

   Outcome

   The student can match a data scenario to the appropriate technique.

   Sub-units

   1. ○ 2.1 Technique Selection Open this unit → ✓ ↻
   Open this module → Print certificate of completion
3. Module 3

   The Data Science Roles in Practice

   Led by Florence Nightingale Simulacrum

   The question

   Nightingale was data engineer, analyst, scientist, and communicator — one person, one pipeline. Modern organisations separate these roles. What does each role actually do day-to-day, and how do they interact?

   Outcome

   The student can describe the daily responsibilities of each major data role.

   Sub-units

   1. ○ 3.1 Read a Job Advertisement Open this unit → ✓ ↻
   Open this module → Print certificate of completion
4. Module 4

   The Data Science Process

   Led by Florence Nightingale Simulacrum

   The question

   The process begins before any data is touched — with a question. CRISP-DM: business understanding, data understanding, data preparation, modelling, evaluation, deployment. What goes wrong when you skip step one?

   Outcome

   The student can apply the CRISP-DM framework to a real data science application.

   Sub-units

   1. ○ 4.1 Apply the Process Open this unit → ✓ ↻
   Open this module → Print certificate of completion
5. Module 5

   Data, Society, and the Nightingale Lesson

   Led by Florence Nightingale Simulacrum

   The question

   Data is not objective. Someone decided what to measure, what to ignore, and what to do with the result. What is the data scientist's responsibility to the people represented in their data?

   Outcome

   The student can identify sources of bias before analysis begins and take a defended position on data science ethics.

   Sub-units

   1. ○ 5.1 Final Essay: The Responsibility Open this unit → ✓ ↻
   Open this module → Print certificate of completion