27 cards generated

デッキが消える前に保存しよう

このフラッシュカードはまだ保存されてないよ — 離れると消えちゃう。無料アカウントを作ると保存できて、下の機能も全部使えるようになるよ。

保存して学習する
  • Save this deck to your account
  • Study with spaced repetition
  • Export to Anki (.apkg) or PDF
より大きく、より良い生成
  • Process documents up to 100 pages
  • Images extracted from your PDFs
  • Sharper text extraction & a more advanced AI model
Sign up free → Free forever · No credit card
Briefing Document Data Science Fundamentals and Related Concepts
  • 情報
    すべての機能を使うには「保存して編集」をクリック
    学習する
    エクスポート
    編集
    管理
公開

Flashcards in this deck (27)

検索中...

  • What are the two main types of data?

    • Quantitative Data
    • Qualitative Data
    data types

  • What is quantitative data?

    Numerical data that can be measured and compared. Examples: age, height, weight.

    data quantitative

  • What is qualitative data?

    Descriptive data that cannot be measured. Examples: opinions, emotions, perceptions.

    data qualitative

  • What is the difference between primary and secondary data?

    • Primary Data: Collected by the researcher.
    • Secondary Data: Obtained from existing sources.
    data sources

  • What are the four criteria for data quality?

    • Relevance
    • Accuracy
    • Validity
    • Reliability
    data quality

  • What does relevance refer to in data quality?

    The extent to which the data is related to the research question.

    data quality

  • What does accuracy refer to in data quality?

    The degree to which the data reflects true values of the variables.

    data quality

  • What does validity refer to in data quality?

    The degree to which the data measures what it is supposed to measure.

    data quality

  • What does reliability refer to in data quality?

    The consistency of the data over time and across different sources.

    data quality

  • What are the two methods of data collection?

    • Active Data Collection
    • Passive Data Collection
    data collection

  • What is an example of manual active data collection?

    Surveys, interviews, and focus groups.

    data collection

  • What is an example of computerised passive data collection?

    Web analytics and tracking devices like GPS.

    data collection

  • What are some errors that can affect data accuracy?

    Errors can arise in collection, entry, or processing.

    data errors

  • What does uncertainty refer to in data?

    A lack of precision or ambiguity in the data.

    data uncertainty

  • What are limitations in data?

    Inherent constraints like sample size, coverage, or representativeness.

    data limitations

  • What is informatics?

    The study of the processing and management of information.

    informatics data

  • What is the purpose of data visualisation?

    To interpret and present data effectively using tools like graphs, infographics, and maps.

    data visualisation

  • What is structured data?

    Data organized into a specific format, such as tables or spreadsheets.

    data structured

  • What is an example of unstructured data?

    Text, images, or audio files.

    data unstructured

  • What is alternative data?

    Data forms like likes, emoticons, and memes used for feedback.

    data alternative

  • What is blockchain technology?

    A distributed ledger for secure, transparent data management and verification.

    technology blockchain

  • What does each block in a blockchain contain?

    Verified transactions that enhance data security.

    blockchain data

  • What is the impact of public Wi-Fi on data security?

    Public networks are generally unsecured and pose risks.

    security data

  • What are the three Vs of big data?

    Volume, Variety, and Velocity.

    big_data data

  • What does data warehousing involve?

    Centralizing and storing large amounts of data.

    data warehousing

  • What is velocity in data management?

    The speed at which the data is generated and the need to process it in real time.

    data velocity

  • What is data warehousing?

    A process of centralizing and storing large amounts of data.

    data warehousing