Data, Data Sources, and Data Types
問題一覧
1
consists of only two possible values, often represented as 0 and 1, and is used in various contexts, including machine learning classification problems.
binary data
2
includes information with a geographic or spatial component, such as latitude and longitude coordinates or GIS (Geographic Information System) data.
spatial data
3
it is collected at regular time intervals and is used to analyze trends and patterns over time, such as stock prices, weather data, or website traffic
time series data
4
includes unstructured textual information and is commonly analyzed using natural language processing (NLP) techniques for sentiment analysis, topic modeling, and text classification
text data
5
represents categories or labels and is often used to group data into distinct classes, such as colors, product categories, or customer segments.
categorical data
6
Data that can only take specific, distinct values, such as the number of employees or items sold.
discrete
7
Data that can take any value within a range, such as temperature or age.
continuous
8
consists of numbers
numerical data
9
Data generated by automated processes, such as log files, system metrics, and event logs, is used for monitoring and troubleshooting.
machine-generated data
10
Organizations often collect data through these to gather feedback, preferences, and opinions from customers or respondents.
survey and questionnaires
11
these platforms generate vast amounts of user-generated content that can be analyzed for sentiment analysis, trend identification, and customer insights.
social media
12
involves extracting data from websites and online sources. It is commonly used to gather data for text analytics, price monitoring, and competitive analysis
web scraping
13
In the context of IoT (Internet of Things), data from sensors and devices, such as temperature sensors, GPS devices, and wearable fitness trackers, can provide valuable insights.
sensor data
14
Data obtained from sources outside the organization, including publicly available data, market research reports, government datasets, and social media data.
external data
15
Data generated and collected within an organization, such as sales records, customer data, employee information, and transaction logs.
internal data
16
these are essential steps in data preparation.
data cleansing and data quality assessment
17
This is crucial for accurate analysis. Data may contain errors, duplicates, or inconsistencies, which can lead to incorrect conclusions.
data quality
18
characterized by its volume, velocity, variety, and veracity. Managing and analyzing this require specialized tools and techniques.
big data
19
With the advent of technology and the internet, organizations now have access to vast amounts of data
big data
20
lacks a specific structure and includes text, images, videos, and more.
unstructured data
21
organized and follows a predefined format, often stored in databases or spreadsheets.
structured data
22
refers to raw facts, information, or observations that are typically collected and stored in a structured or unstructured format. It can come in various forms, and it is the primary input for any data analysis process.
data
23
the fundamental building block upon which all analysis and insights are based. Understanding data, its sources, and its types is crucial for any data science practitioner.
data
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13問 • 2年前問題一覧
1
consists of only two possible values, often represented as 0 and 1, and is used in various contexts, including machine learning classification problems.
binary data
2
includes information with a geographic or spatial component, such as latitude and longitude coordinates or GIS (Geographic Information System) data.
spatial data
3
it is collected at regular time intervals and is used to analyze trends and patterns over time, such as stock prices, weather data, or website traffic
time series data
4
includes unstructured textual information and is commonly analyzed using natural language processing (NLP) techniques for sentiment analysis, topic modeling, and text classification
text data
5
represents categories or labels and is often used to group data into distinct classes, such as colors, product categories, or customer segments.
categorical data
6
Data that can only take specific, distinct values, such as the number of employees or items sold.
discrete
7
Data that can take any value within a range, such as temperature or age.
continuous
8
consists of numbers
numerical data
9
Data generated by automated processes, such as log files, system metrics, and event logs, is used for monitoring and troubleshooting.
machine-generated data
10
Organizations often collect data through these to gather feedback, preferences, and opinions from customers or respondents.
survey and questionnaires
11
these platforms generate vast amounts of user-generated content that can be analyzed for sentiment analysis, trend identification, and customer insights.
social media
12
involves extracting data from websites and online sources. It is commonly used to gather data for text analytics, price monitoring, and competitive analysis
web scraping
13
In the context of IoT (Internet of Things), data from sensors and devices, such as temperature sensors, GPS devices, and wearable fitness trackers, can provide valuable insights.
sensor data
14
Data obtained from sources outside the organization, including publicly available data, market research reports, government datasets, and social media data.
external data
15
Data generated and collected within an organization, such as sales records, customer data, employee information, and transaction logs.
internal data
16
these are essential steps in data preparation.
data cleansing and data quality assessment
17
This is crucial for accurate analysis. Data may contain errors, duplicates, or inconsistencies, which can lead to incorrect conclusions.
data quality
18
characterized by its volume, velocity, variety, and veracity. Managing and analyzing this require specialized tools and techniques.
big data
19
With the advent of technology and the internet, organizations now have access to vast amounts of data
big data
20
lacks a specific structure and includes text, images, videos, and more.
unstructured data
21
organized and follows a predefined format, often stored in databases or spreadsheets.
structured data
22
refers to raw facts, information, or observations that are typically collected and stored in a structured or unstructured format. It can come in various forms, and it is the primary input for any data analysis process.
data
23
the fundamental building block upon which all analysis and insights are based. Understanding data, its sources, and its types is crucial for any data science practitioner.
data