1.1. Types and Sources of Data

Learning Outcome

Students will be able to distinguish between types and sources of data.

Sample Tasks:

• Differentiate between categorical and quantitative data.

• Classify data as nominal level, ordinal level or ratio level and discuss its characteristics and limitations.

• Discern between a set of data and a source of data.

[OhioDoHEducation21]

Our first reading, from Learning Data Science [LGN23], describes the feature types one finds in data. (It then does some examples using the pandas library, which we learn about in Section 1.4.1.)

• 10.1. Feature Types

Reading Question

Your data contains a feature with possible entries “small”, “medium”, and “large”. What type is this feature?

Our second set of readings, also from Learning Data Science [LGN23], walks through some of the issues to consider when evaluating data sources and the data sets they provide.

• 2. Questions and Data Scope

  • 2.2. Target Population, Access Frame, Sample

  • 2.3. Instruments and Protocols

  • 2.4. Measuring Natural Phenomenon

  • 2.5. Accuracy

Reading Questions

Your professor’s kid keeps track of all the Halloween candy they receive and takes measurements on it:

• Brand

• Length in the longest direction

• Sugar in grams

1. What are the types of each of these features?

2. What other features could be obtained that would be of other types?

3. How reliable is this data? What does it give us information about?

Further Resource

From the Python Data Science Handbook [Van16]

• 5. Machine Learning

  • Feature Engineering