Data, Data Sources, and Data Types

Data, Data Sources, and Data Types
23問 • 2年前
  • Rupac, Ashley May
  • 通報

    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

  • CPEL

    CPEL

    Rupac, Ashley May · 46問 · 2年前

    CPEL

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    46問 • 2年前
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    SAS

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    49問 • 2年前
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    Data Science

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    Excel Interface

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    Data Analytics Process

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    Rupac, Ashley May · 14問 · 2年前

    Data Analytics Process

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    14問 • 2年前
    Rupac, Ashley May

    Data analytics and its types

    Data analytics and its types

    Rupac, Ashley May · 18問 · 2年前

    Data analytics and its types

    Data analytics and its types

    18問 • 2年前
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    Basic data quality assessment

    Basic data quality assessment

    Rupac, Ashley May · 20問 · 2年前

    Basic data quality assessment

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    20問 • 2年前
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    Ethical considerations

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    Rupac, Ashley May · 13問 · 2年前

    Ethical considerations

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    13問 • 2年前
    Rupac, Ashley May

    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