Course 3: Module 1

Question 1

How data is collected

Answer 1

• Interviews
• Observations
• Forms
• Questionnaires
• Surveys
• Cookies

Question 2

Data collection considerations:

Answer 2

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

Question 3

First-party data

Answer 3

Data collected by an individual or group using their own resources

Question 4

Second-party data

Answer 4

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

Question 5

Third-party data

Answer 5

Data collected from outside sources who did not collect it directly

Question 6

Population

Answer 6

All possible data values in a certain dataset

Question 7

Sample

Answer 7

A part of a population that is representative of the population

Question 8

Discrete data

Answer 8

Data that is counted and has a limited number of values

Question 9

Continuous data

Answer 9

Data that is measured and can have almost any numeric value

Question 10

Nominal data

Answer 10

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

Question 11

Ordinal data

Answer 11

A type of qualitative data with a set order or scale

Question 12

Internal data

Answer 12

Data that lives within a company’s own systems

Question 13

External data

Answer 13

Data that lives and is generated outside of an organization

Question 14

Structured data

Answer 14

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

Question 15

Unstructured data

Answer 15

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

Question 16

Data model

Answer 16

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

Question 17

Data elements

Answer 17

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

Question 18

Conceptual data modeling

Answer 18

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

Question 19

Logical data modeling

Answer 19

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.

Question 20

Physical data modeling

Answer 20

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.

Question 21

Two common methods of data modeling

Answer 21

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

Question 22

Data type

Answer 22

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

Question 23

Data types in spreadsheets

Answer 23

• Number
• Text or string
• Boolean

Question 24

Text or string data type

Answer 24

A sequence of characters and punctuation that contains textual information

Question 25

Boolean data type

Answer 25

A data type with only two possible values, such as TRUE or FALSE

Question 26

Wide data

Answer 26

Data in which every data subject has a single row with multiple columns to hold the values of various attributes of the subject

Question 27

Long data

Answer 27

Data in which each row is one time point per subject, so each subject will have data in multiple rows

Question 28

Data organization

Answer 28

better organized data is easier to use

Question 29

Data compatibility

Answer 29

different applications or systems can use the same data

Question 30

Data migration

Answer 30

data with matching formats can be moved from one system to another

Question 31

Data merging

Answer 31

data with the same organization can be merged together

Question 32

Data enhancement

Answer 32

data can be displayed with more detailed fields

Question 33

Data comparison

Answer 33

apples-to-apples comparisons of the data can be made