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  2. Introduction to Data Analysis with Google Sheets & Python
  3. Data Foundations
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Home └Introduction to Data Analysis with Google Sheets & Python └Data Foundations

Introduction to Data Analysis with Google Sheets & Python

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  • Part 0: Data Foundations
    • Data Foundations 10 xp
      • Quiz
  • Part 1: Gsheets Orientation
    • Gsheets Orientation 10 xp
      • Quiz

Data Foundations

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1. Per ISO/IEC 2382:2015, term 2121272, which best defines "data"?
Knowledge concerning objects that has a particular meaning within a context A reinterpretable representation of information in a formalized manner, suitable for interpretation, or processing Any numeric value stored in a computer system The output of a completed analysis
2. What is the key distinction between data and information?
Data is the formalised representation — a cell value, a byte string, a row; information is the interpreted meaning of that value, in context Data and information are synonyms that differ only by discipline Information is raw and unstructured; data is always structured and typed Data exists only in databases; information exists only in reports
3. In Ackoff's hierarchy, what is the correct order from most raw to most applied?
Information → Data → Knowledge → Wisdom Data → Information → Knowledge → Wisdom Data → Knowledge → Information → Wisdom Wisdom → Knowledge → Information → Data
4. Per Stevens' four scales of measurement, which scale admits only equality comparisons, and corresponds to the course's "categorical"/"boolean" terms?
Ratio Interval Nominal Ordinal
5. Why are dates classified as interval-scaled rather than ratio-scaled?
Differences between dates are meaningful, but the zero point (the epoch) is arbitrary, so ratios of dates are not meaningful Dates admit only equality, not ordering Dates have a true zero, so any date is a meaningful multiple of another Dates are categorical, not numerical, and so have no defined scale
6. Which pairing of data-structure term to definition is correct?
Array: ordered sequence, dynamic size. List: fixed-size, contiguous, homogeneous, constant-time indexed access Table (relation): fixed-size, contiguous, homogeneous structure. Array: a set of tuples over a fixed schema Array: fixed-size, contiguous, homogeneous, constant-time indexed access. List: ordered sequence, dynamic size. Table (relation): a set of tuples over a fixed schema List and table are the same structure; only the array differs
7. Per Codd's relational model, what is separated from what?
Structure (schema) is separated from content (rows) — a relation's fixed set of named, typed columns distinct from the set of rows populating it Primary keys are separated from foreign keys and can never coexist in the same schema Physical storage is separated from the number of rows a table may hold Data types are separated from column names, so any column may hold any type at any time
8. Per Codd's relational model, what is separated from what?
Structure (schema) is separated from content (rows) — a relation's fixed set of named, typed columns distinct from the set of rows populating it Primary keys are separated from foreign keys and can never coexist in the same schema Physical storage is separated from the number of rows a table may hold Data types are separated from column names, so any column may hold any type at any time
9. What goes wrong in a fan trap?
Rows are silently dropped because a relationship is missing between two entities connected only through optional intermediary A one-to-many join is fanned out through a second one-to-many relationship, inflating aggregates The join fails outright with a referential-integrity error Cardinality problems only affect primary keys, never joins
10. Which statement about dimensional vs. transactional data is correct?
Dimensional data changes continuously and is high in volume; transactional data changes rarely and is low in volume Dimensional data (e.g., a customer or product) changes rarely and typically plays the "lookup" side of a join; transactional data (e.g., an order or payment) is event-based, high-volume, and typically plays the "fact" side of a join Dimensional and transactional data are interchangeable terms for the same category Transactional data becomes dimensional once it is more than a year old

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