PRACTICE QUIZ

If you have a short time frame for data collection and need an answer immediately, you likely will have to use historical data.

“Did anyone recommend our restaurant to you today?”, “Have you heard of our frequent diner program?”, and “Is this your first time dining at this restaurant?” are questions that collect nominal qualitative data.

Internal data is more reliable because it lives within a company’s own systems.

A social media post is an example of unstructured data.

A Boolean data type can have many different types of values, but there can only be two of them.

The selection from the spreadsheet contains long data.

A data analyst using Save As to change a file type from .XLS to .CSV is an example of a data transformation.

The most likely reason for choosing to analyze only historical data is that a project has a very short time frame.

Movie run time is an example of continuous data.

Internal data is considered more reliable and easier to collect than external data because it lives within a company’s own systems.

A relational database is an example of structured data.

In wide data, each column contains a unique data variable. In long data, separate columns contain the values and the context for the values, respectively.

A benefit of internal data is that it’s more reliable and easier to collect than external data.

True or false and yes or no are examples of a Boolean data type.

As a result of the short time frame, they are most likely to use historical data.

“Is this your first time dining at this restaurant?” is a question that collects nominal qualitative data.

Nominal qualitative data does not have a set order or scale.

Structured data is likely to be found in a table or spreadsheet.

The number of actors in a movie, box office returns, and the movie budget are examples of discrete data.

Data transformation enables data analysts to change the structure of the data.

A Boolean data type can have two possible values.

Data transformation can change the structure of the data. An example of this is taking data stored in one format and converting it to another.

The survey data is biased because the sample group lacks inclusivity.

Interpretation bias is the tendency to always construe ambiguous situations in a positive or negative way.

Good data is comprehensive, current, and cited.

This refers to currency, which says that individuals should be aware of financial transactions resulting from the use of their personal data and the scale of these transactions.

The individual who originally generates the data has primary control over its usage, how it’s processed, and how it’s shared.

Data anonymization applies to all personally identifiable information, including text and images.

Open data does not involve granting free access to people’s personal information.

Data bias is a type of error that systematically skews results in a certain direction.

Data ethics refers to well-founded standards of right and wrong that dictate how data is collected, shared, and used.

This refers to transaction transparency, which is the idea that an individual who provides their data has the right to know and understand all of the data-processing activities and algorithms used on that data.

Different databases using common formats and terminology is an example of interoperability.

In general, the usefulness of data decreases as time passes. The best data sources are current and relevant.

The right to inspect, update, or correct your own data is part of data privacy.

Blanking, hashing, and masking are common methods of anonymizing data.

In data ethics, consent gives individuals the right to know why their data is being collected, how it will be used, and how long it will be stored.

An individual who provides their data has the right to know and understand all of the data-processing activities and algorithms used on that data. This is called transaction transparency.

An employer cannot access an employee’s credit report without their consent. This is a violation of the employee’s data privacy.

Unreliable data is inaccurate, incomplete, and biased.

Observer bias, interpretation bias, and confirmation bias are types of bias often encountered in data analytics.

This is an example of sampling bias, which is when a sample isn’t representative of the population as a whole.

Data anonymization removes personally identifiable information, such as home addresses, telephone numbers, credit card numbers, and medical records.

A scholar who only reads sources that support their argument, a daycare that won’t hire men for childcare positions, and a dancing competition judge who is a close friend of the dancer who wins the competition are examples of bias.

This is an example of open data. Everyone must be able to use, re-use, and redistribute open data.

Primary and foreign keys exist in two connected tables in a relational database.

Data analysts use metadata to combine data, evaluate data, and interpret a database.

Structural metadata indicates how a piece of data is organized and whether it’s part of one, or more than one, data collection.

Data governance is the process of ensuring the formal management of a company’s data assets.

External data representing diverse perspectives is not an appropriate reason to choose not to use a dataset. A data analyst might choose not to use external data if the it cannot be confirmed to be reliable.

The analyst can filter out sales outside of Milwaukee in 2019 and sort by date in descending order.

When writing a query, the name of the dataset can either be inside two backticks, or not, and the query will still run properly.

The clause WHERE first_name = 'Mark' will return only customers named Mark. The complete query is SELECT * FROM customer WHERE first_name = 'Mark'. The WHERE clause filters results that meet certain conditions. The WHERE clause includes the name of the column, an equals sign, and the value(s) in the column to include. Place quotes around text values. There are two customers named Mark.

Metadata helps data analysts understand the contents of a database, ensure data is clean and reliable, and understand how to combine data from more than one source.

The license plate number, make and model of the cab, and the company that owns the taxi represent structural metadata.

Some key benefits of using external data are that it has a broad reach and it provides industry-level perspectives.

The analyst can filter out showings outside of San Francisco in 2001 and sort by date in ascending order.

Primary and foreign keys are two connected identifiers within separate tables in a relational database.

Data governance is the process of ensuring that a company’s data assets are managed in a formal manner.

The data analyst should sort the number_of_laptops column numerically in ascending order.

When writing a query, the name of the dataset can either be inside two backticks, or not, and the query will still run properly.

The student ID number, enrollment date, and classes the student is enrolled in represent structural metadata.

Data governance is the process of ensuring the formal management of a company’s data assets.

A data analyst might choose not to use external data in their analysis if the data cannot be confirmed to be reliable.

Primary and foreign keys represent the connection between tables in a relational database.

Metadata helps data analysts identify the type of data, if it is clean and reliable, and how it can be combined with another dataset.

The team is using file-naming conventions, which are consistent guidelines that describe the content, creation date, or version of a file.

Metadata is data about data. Metadata practices can help analytics teams create consistent naming conventions and storage practices for their files.

Elementary_Students_20090221_V03 is an effective file name because it is an appropriate length and references the project name, creation date, and version.

Data analysts use foldering to keep project-related files together and organize them into subfolders.

Archiving involves moving files from completed projects to a separate location.

Data analysts create hierarchies to structure their folders.

To control who can access or edit a spreadsheet, data analysts use encryption and sharing permissions.

Copying tabs 1-4 into a separate spreadsheet, then sharing the new file with the client will keep tab 5 private. In addition, making a copy of the spreadsheet, deleting tab 5, then sharing the new file with the client will keep tab 5 private.

NewCustomerSurvey_2020-6-20_V03 is an effective file name because it is an appropriate length and references the project name, creation date, and version.

Data analysts create hierarchies to organize their folders. They do this by structuring folders with broad topics at the top, then more specific topics below.

Hiding tabs B and C will not protect them from being accessed.

File-naming conventions describe a file’s content, creation date, and version number.

To align file naming and storage practices, it’s useful to develop metadata practices with your data analytics team.

FundDriveDonors_20210216_V03 is an effective file name because it is an appropriate length and references the project name, creation date, version.

Folder hierarchies are structured with broad topics at the top, then more specific topics below.

A data analyst adds sharing permissions to limit who can edit the data contained within a file. This is an example of data security.

Hidden cells can be easily unhidden using the unhide feature. Hiding does not protect data.

Foldering may be used by data analysts to organize folders into subfolders.

Moving project files to another location to keep them separate from their current work is an example of archiving files.

Using encryption to protect data is an example of data security.

File-naming conventions are consistent guidelines that describe a file's content, creation date, or version.

A data analytics team uses metadata to indicate consistent naming conventions for a project. This is an example of using data about data.

First-party data is data collected by an individual or group using their own resources.

A Boolean data type has only two possible values, such as yes or no.

The data in column D is an example of continuous data, which can be measured.

This is unstructured data, which is not organized in an easily identifiable manner.

Building trust involves not sharing private or sensitive client information.

This describes first-party data, which is collected by an individual or group using their own resources.

This is an example of discrete data, which is counted and has a limited number of values. It is also quantitative data, which is specific and measures numerical facts.

Unstructured data is not organized in an easily identifiable manner.

Building trust can be achieved by showing a client that you will keep client data safe by using passwords and user permissions.

First-party data is data collected by an individual or group using their own resources.

Boolean data has only two possible values, such as yes or no.

This is an example of discrete data, which is counted and has a limited number of values. It is also quantitative data, which is specific and measures numerical facts.

You can build trust by showing a client that you will organize their data effectively and keep it safe by implementing appropriate data-security measures.

The way questions are written, the amount of time given to answer each question, and the inclusivity of the participants can cause potential bias.

Internal data lives within a company’s own systems and is typically generated from within the company. External data lives in and is generated outside the organization.

Filtering enables data analysts to show only the data that they need. This makes it easier for data analysts on the same team to use the same dataset for different purposes.

To write a query that retrieves only data about people who joined the firm in 2019 from the Clients table, type SELECT * FROM Clients WHERE start_date=’2019’.

This describes data privacy, which involves protecting an individual's private data.

Data analysts access external data from open-data websites and public databases.

Sorting data enables data analysts on the same team to use the same dataset for different purposes.

Data privacy deals with preserving a data subject’s information and activity any time a data transaction occurs, a person’s legal right to their data, and establishing privacy measures to protect people’s data.

To write a query that retrieves only data about people with the last name Hassan from the Clients table, type SELECT * FROM Clients WHERE last_name=’Hassan’.

Choosing participants at random, including people with disabilities, and including people of all genders help ensure a survey sample represents the population as a whole.

External data can come from a variety of places. This may include other businesses, government sources, professional associations, and more.

Sorting, filtering, and freezing header rows enable data analysts on the same team to use the same dataset for different purposes.

First-party data is data collected by an individual or group using their own resources.

This is an example of unstructured data, which is not organized in an easily identifiable manner.

Data privacy involves preserving a data subject’s information and activity any time a data transaction occurs.

This is Boolean data, which has only two possible values, such as yes or no.