WGU D467 Exploring Data Exam Questions & Answers | 100% Verified solutions |Questions with Correct Answers 2025 latest update!!

Data collection considerations

1. Select the right data type 2. Determine the time frame for data collection 3. How the data will be collected 4. How much data to collect 5. Choose data sources 6. Decide what data to use

Population

All possible data values in a certain dataset

Sample

A part of the population that is representative of the population

First-party data

Data collected by an individual or group using their own resources

Second-party data

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

Third-party data

Data collected from outside sources who did not collect it directly

Internal data (Primary data)

Collected by a researcher from first-hand sources

External data (Secondary data)

Gathered by other people or from other research

Quantitative data

Can be measured and counted using numbers (quantity, amount, range)

Qualitative data

Cannot be counted, measured or easily expressed in numbers (names, categories, descriptions)

Discrete data

Data that is counted and has a limited number of values

Continuous data

Data that is measured and can have any numeric value

Nominal data

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

Ordinal data

A type of qualitative data with a set order or scale

Structured data

Data organized in a certain format such as rows and columns

Unstructured data

Data that is not organized in an easily identifiable manner

Data model

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

Data elements

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

Data modeling

The process of creating diagrams that visually represent how data is organized and structured

Levels of data modeling

1. Conceptual modeling 2. Logical data modeling 3. Physical data modeling

Physical data model

Defines all entities and attributes used.

Entity Relationship Diagram (ERD)

Visual way to understand the relationship between entities in the data model.

Unified Modeling Language (UML)

Detailed diagrams that describe the structure of a system by showing the system's entities, attributes, operations, and their relationships.

Data type

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

Text or string

A sequence of characters and punctuation that contains textual information.

Boolean

Data type with only two possible values: true or false.

Operator

A symbol that names the operation or calculation to be performed.

AND operator

Lets you stack both of your conditions.

OR operator

Lets you move forward if either one of your two conditions is met.

NOT operator

Lets you filter by subtracting specific conditions from the results.

Row

Record.

Column

Field.

Wide data

Data where each row contains multiple data points for the particular items identified in the columns.

Long data

Data where each row contains a single data point for a particular item.

Data transformation

The process of changing the data's format, structure, or values.

Data organization

Better organized data is easier to use.

Data compatibility

Different applications or systems can then use the same data.

Data migration

Data with matching formats can be moved from one system to another.

Data merging

Data with the same organization can be merged together.

Data enhancement

Data can be displayed with more detailed fields.

Data comparison

Apples-to-apples comparisons of the data can then be made.

Bias

A conscious or unconscious preference in favor of or against a person, group of people or thing.

Data bias

A type of error that systematically skews results in a certain direction.

Fairness

A quality of data analysis that does not create or reinforce bias.

Unbiased sampling

When a sample is representative of the population being measured - This is achieved using random sampling.

Sampling bias

When a sample isn't representative of the population as a whole.

Observer bias

The tendency for different people to observe things differently.

Interpretation

The tendency to interpret ambiguous situations in a positive or negative way.

Confirmation bias

The tendency to search for or interpret information in a way that confirms pre-existing beliefs.

Good data

Reliable, accurate, complete, unbiased information.

Bad data

Inaccurate, incomplete or biased information.

Original data

Validated with original source.

Comprehensive data

Contains all critical information needed to answer the question.

Current

Relevant to the task at hand

Cited

Provides credibility

Not current

Out of date and irrelevant

Not cited

Lacks credibility

Ethics

Well-founded standards of right and wrong that prescribe what humans ought to do, usually in terms of rights, obligations, benefits to society, fairness, or specific virtues

Data ethics

Well founded standards of right and wrong that indicate how data is collected, shared, and used

GDPR

General Data Protection Regulation of the European Union

Ownership

Individuals own the raw data they provide and they have control over its usage, how it's processed, and how it's shared

Transaction transparency

All data-processing activities and algorithms should be completely explainable and understood by the individual who provides their data

Consent

An individual's right to know explicit details about how and why their data will be used before agreeing to provide it

Currency

Individuals should be aware of financial transactions resulting from the use of their personal data and the scale of these transactions

Privacy

Preserving a data subject's information and activity any time a data transaction occurs

Openness

Free access, usage, and sharing of data

Data anonymization

The process of protecting people's private or sensitive data by eliminating that kind of information

Personally identifiable information (PII)

Information that can be used by itself or with other data to track down a person's identity

De-identification

A process used to wipe data clean of all personally identifying information

Openness (or open data)

The aspect of data ethics that promotes the free access, usage, and sharing of data

Availability and access

Open data must be available as a whole, preferably by downloading over the Internet in a convenient and modifiable form

Reuse and redistribution

Open data must be provided under terms that allow reuse and redistribution including the ability to use it with other datasets

Universal participation

Everyone must be able to use, reuse, and redistribute the data without discrimination

Data interoperability

The ability of data systems and services to openly connect and share data

Database

A collection of data stored in a computer system

Metadata

Data about data that tells you where the data comes from, when and how it was created, and what it's all about

Relational database

A database that contains a series of related tables that can be connected to form relationships

Primary key

An identifier that references a column in which each value is unique (the unique identifier for each row in a table)

Foreign keys

A field within a table that is primary key in another table (how one table can be connected to another)

Unique data constraint

Used to ensure data in a specific column is unique

Record identification

Uniquely identifies a record in a relational database table

Linking columns

A column or group of columns in a relational database table that provides a link between the data in two tables

Primary key limitation

Only one primary key is allowed in a table

Foreign key reference

Refers to the field in a table that's the primary key of another table

Null value restriction

Cannot contain null or blank values

Foreign key allowance

More than one foreign key is allowed to exist in a table

Normalization

A process of organizing data in a relational database to eliminate data redundancy, increase data integrity, and reduce complexity

Composite key

A primary key constructed using multiple columns of a table

Descriptive metadata

Metadata that describes a piece of data and can be used to identify it at a later point in time (a book's ISBN, author and title)

Structural metadata

Metadata that indicates how a piece of data is organized and whether it is part of one, or more than one, data collection

Administrative metadata

Metadata that indicates the technical source of a digital asset (metadata in a digital photo)

Elements of metadata

File or document type, date, time, and creator, title and description, geolocation, tags and categories, modification history, access permissions

Reliability of metadata

Metadata helps data analysts confirm their data is reliable by making sure it is accurate, precise, relevant, and timely

Consistency in databases

When a database is consistent, it's easier to discover relationships between the data inside the database and data that exists elsewhere

Metadata repositories

Specialized databases specifically created to store and manage metadata, describing where the metadata came from and storing that data in an accessible form

Access to metadata

Provides data analysts with quick and easy access to the data

Data classification

Data analysts can categorize data when it follows a consistent format, which is beneficial in cleaning and processing data

Data storage

Consistent and uniform data can be efficiently stored in various data repositories, streamlining storage management tasks

Data access

Users, applications, and systems can efficiently locate and use data

Data Governance

A process to ensure the formal management of a company's data assets.