Course 3: Module 1 Flashcards

1
Q

How data is collected

A
  • Interviews
  • Observations
  • Forms
  • Questionnaires
  • Surveys
  • Cookies
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2
Q

Data collection considerations:

A
  • How the data will be collected
  • Choose data sources
  • Decide what data to use
  • Select the right data type
  • Determine the time frame
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3
Q

First-party data

A

Data collected by an individual or group using their own resources

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4
Q

Second-party data

A

Data collected by a group directly form its audience and then sold

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5
Q

Third-party data

A

Data collected from outside sources who did not collect it directly

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6
Q

Population

A

All possible data values in a certain dataset

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7
Q

Sample

A

A part of a population that is representative of the population

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8
Q

Discrete data

A

Data that is counted and has a limited number of values

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9
Q

Continuous data

A

Data that is measured and can have almost any numeric value

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10
Q

Nominal data

A

A type of qualitative data that is categorized without a set order

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11
Q

Ordinal data

A

A type of qualitative data with a set order or scale

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12
Q

Internal data

A

Data that lives within a company’s own systems

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13
Q

External data

A

Data that lives and is generated outside of an organization

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14
Q

Structured data

A

Data organized in a certain format such as towns and columns
( Spreadsheets)

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15
Q

Unstructured data

A

Data that is not organized in any easily identifiable manner
(Audio and video files)

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16
Q

Data model

A

A model that is used for organizing data elements and how they relate to one another

17
Q

Data elements

A

Pieces of information, such as people’s names, account numbers, and addresses

18
Q

Conceptual data modeling

A

gives a high-level view of the data structure, such as how data interacts across an organization. It can be used to define the business requirements for a new database. It also doesn’t contain technical details

19
Q

Logical data modeling

A

focuses on the technical details of a database such as relationships, attributes, and entities. For example, a logical data model defines how individual records are uniquely identified in a database. But it doesn’t spell out actual names of database tables. That’s the job of a physical data model.

20
Q

Physical data modeling

A

depicts how a database operates. A physical data model defines all entities and attributes used; for example, it includes table names, column names, and data types for the database.

21
Q

Two common methods of data modeling

A

Entity Relationship Diagram (ERD) and the Unified Modeling Language (UML)

22
Q

Data type

A

A specific kind of data attribute that tells what kind of value the data is

23
Q

Data types in spreadsheets

A
  • Number
  • Text or string
  • Boolean
24
Q

Text or string data type

A

A sequence of characters and punctuation that contains textual information

25
Boolean data type
A data type with only two possible values, such as TRUE or FALSE
26
Wide data
Data in which every data subject has a single row with multiple columns to hold the values of various attributes of the subject
27
Long data
Data in which each row is one time point per subject, so each subject will have data in multiple rows
28
Data organization
better organized data is easier to use
29
Data compatibility
different applications or systems can use the same data
30
Data migration
data with matching formats can be moved from one system to another
31
Data merging
data with the same organization can be merged together
32
Data enhancement
data can be displayed with more detailed fields
33
Data comparison
apples-to-apples comparisons of the data can be made