暗記メーカー

暗記メーカー

​
ログイン
Module 1 (Data Analytics)
35問 • 12ヶ月前
  • iam noone
    • 通報

    問題一覧

  • 1

    evolved from BI

    Analytics

  • 2

    Analytics is an umbrella term for data analysis application.

    True

  • 3

    Analytics is the use of “rocket science” algorithms to analyze data

    True

  • 4

    process that involves the use of statistical techniques, information system software, and operations research methodologies to explore, visualize, discover, and communicate patterns or trends in data.

    Analytics

  • 5

    Based on Live Data, Tells what's happening in real time

    Descriptive

  • 6

    Automated RCA - Root Cause Analysis Explains "why" things are happening and helps troubleshoot issues.

    Diagnostic

  • 7

    Based on historical future plans, goals, and objectives data, and assumes a static business plans/models

    Predictive

  • 8

    Based on current data analytics, predefined future plans, goals, and objectives To test potential outcomes

    Prescriptive

  • 9

    The process of gaining insights from data, finding meaning in the numbers, and then turning those meaningful numbers into success stories.

    Data Analytics

  • 10

    a process of understanding the scope, objectives, and complexities of business projects.

    Business Analytics

  • 11

    simple collection of data or a data file

    DATA SET

  • 12

    a collection of data files that contain information on people, locationsa collection of data files that contain information on people, locations

    DATABASE

  • 13

    hardware and software used for data remote storage, retrieval, and computational functions

    COMPUTER CLOUD

  • 14

    - A way of organizing the elements of a dataset

    DATA MODEL

  • 15

    Presenting information graphically, to illustrate trends and patterns

    DATA VISUALIZATION

  • 16

    Finding and removing data points that are inaccurate or irrelevant

    DATA CLEANSING

  • 17

    the smaller data segments or files that help individual businesses keep track of customers.

    LITTLE DATA

  • 18

    the collection of data sets that are so large and complex that software systems are hardly able to process them

    BIG DATA

  • 19

    the most frequently used software for databases

    EXCEL

  • 20

    allows analysts to quickly extract specific data from tables

    SQL

  • 21

    relatively easy to use, and makes large amounts of information easier to manipulate.

    PHYTON

  • 22

    specifically designed for data mining and completing statistical functions.

    R

  • 23

    BENEFITS

    READ

  • 24

    APPLICATION

    READ

  • 25

    Steps on how to apply DATA ANALYTICS

    READ

  • 26

    the framework for computer science, information systems, communication networks and their applications in various organized humanitarian work.

    INFORMATION TECHNOLOGY

  • 27

    The 21st century is defined by a tremendous technological revolution resulting to a terrible increase in the volume of data.

    INFORMATION TECHNOLOGY

  • 28

    a subset of Al that involves algorithms that can learn on their own

    MACHINE LEARNING

  • 29

    ensure that your data is high-quality and that you collect it in a central Data Management Plan.

    DATA MANAGEMENT

  • 30

    enables sifting through large datasets and figure out what's relevant.

    DATA MINING

  • 31

    analyze historical data to predict future outcomes

    PREDICTIVE ANALYTICS

  • 32

    can isolate and utilize the information to business benefit

    KNOWLEDGE DISCOVERY

  • 33

    useful for filtering, aggregation, and analysis of big data

    STREAM ANALYTICS

  • 34

    allow businesses to streamline data across a number of big data solution

    DATA INTEGRATION

  • 35

    can conduct cleansing and enrichment of large data sets

    DATA QUALITY

    • Data Structure (history)

    Data Structure (history)

    iam noone · 64問 · 1年前

    Data Structure (history)

    Data Structure (history)

    64問 • 1年前
    iam noone

    Linear Data Structure

    Linear Data Structure

    iam noone · 43問 · 1年前

    Linear Data Structure

    Linear Data Structure

    43問 • 1年前
    iam noone

    Searching for Algorithms

    Searching for Algorithms

    iam noone · 22問 · 1年前

    Searching for Algorithms

    Searching for Algorithms

    22問 • 1年前
    iam noone

    Linked list/ Trees and graphs

    Linked list/ Trees and graphs

    iam noone · 51問 · 1年前

    Linked list/ Trees and graphs

    Linked list/ Trees and graphs

    51問 • 1年前
    iam noone

    Sorting algorithms

    Sorting algorithms

    iam noone · 41問 · 1年前

    Sorting algorithms

    Sorting algorithms

    41問 • 1年前
    iam noone

    INTRO TO MS ACCESS(database)

    INTRO TO MS ACCESS(database)

    iam noone · 19問 · 1年前

    INTRO TO MS ACCESS(database)

    INTRO TO MS ACCESS(database)

    19問 • 1年前
    iam noone

    Introduction To Java Programming Language

    Introduction To Java Programming Language

    iam noone · 80問 · 1年前

    Introduction To Java Programming Language

    Introduction To Java Programming Language

    80問 • 1年前
    iam noone

    Mod 2: Comp Prog

    Mod 2: Comp Prog

    iam noone · 55問 · 1年前

    Mod 2: Comp Prog

    Mod 2: Comp Prog

    55問 • 1年前
    iam noone

    COMP PROG MIDTERM

    COMP PROG MIDTERM

    iam noone · 42問 · 1年前

    COMP PROG MIDTERM

    COMP PROG MIDTERM

    42問 • 1年前
    iam noone

    FUNDAMENTALS OF DATABASE (Lesson 1)

    FUNDAMENTALS OF DATABASE (Lesson 1)

    iam noone · 31問 · 1年前

    FUNDAMENTALS OF DATABASE (Lesson 1)

    FUNDAMENTALS OF DATABASE (Lesson 1)

    31問 • 1年前
    iam noone

    FUNDAMENTALS OF DATABASE (part2)

    FUNDAMENTALS OF DATABASE (part2)

    iam noone · 33問 · 1年前

    FUNDAMENTALS OF DATABASE (part2)

    FUNDAMENTALS OF DATABASE (part2)

    33問 • 1年前
    iam noone

    DATABASE 2-5 MOD

    DATABASE 2-5 MOD

    iam noone · 25問 · 1年前

    DATABASE 2-5 MOD

    DATABASE 2-5 MOD

    25問 • 1年前
    iam noone

    DEPTALS (COMP PROG)

    DEPTALS (COMP PROG)

    iam noone · 49問 · 1年前

    DEPTALS (COMP PROG)

    DEPTALS (COMP PROG)

    49問 • 1年前
    iam noone

    HCI HISTORY

    HCI HISTORY

    iam noone · 18問 · 11ヶ月前

    HCI HISTORY

    HCI HISTORY

    18問 • 11ヶ月前
    iam noone

    Module 1 (System Analysis)

    Module 1 (System Analysis)

    iam noone · 18問 · 12ヶ月前

    Module 1 (System Analysis)

    Module 1 (System Analysis)

    18問 • 12ヶ月前
    iam noone

    Module 2(Data Analytics)

    Module 2(Data Analytics)

    iam noone · 35問 · 11ヶ月前

    Module 2(Data Analytics)

    Module 2(Data Analytics)

    35問 • 11ヶ月前
    iam noone

    Module 1 (Networking)

    Module 1 (Networking)

    iam noone · 25問 · 12ヶ月前

    Module 1 (Networking)

    Module 1 (Networking)

    25問 • 12ヶ月前
    iam noone

    Module 1 (Info Assurance)

    Module 1 (Info Assurance)

    iam noone · 22問 · 12ヶ月前

    Module 1 (Info Assurance)

    Module 1 (Info Assurance)

    22問 • 12ヶ月前
    iam noone

    mod 1 IT LIVING

    mod 1 IT LIVING

    iam noone · 61問 · 11ヶ月前

    mod 1 IT LIVING

    mod 1 IT LIVING

    61問 • 11ヶ月前
    iam noone

    問題一覧

  • 1

    evolved from BI

    Analytics

  • 2

    Analytics is an umbrella term for data analysis application.

    True

  • 3

    Analytics is the use of “rocket science” algorithms to analyze data

    True

  • 4

    process that involves the use of statistical techniques, information system software, and operations research methodologies to explore, visualize, discover, and communicate patterns or trends in data.

    Analytics

  • 5

    Based on Live Data, Tells what's happening in real time

    Descriptive

  • 6

    Automated RCA - Root Cause Analysis Explains "why" things are happening and helps troubleshoot issues.

    Diagnostic

  • 7

    Based on historical future plans, goals, and objectives data, and assumes a static business plans/models

    Predictive

  • 8

    Based on current data analytics, predefined future plans, goals, and objectives To test potential outcomes

    Prescriptive

  • 9

    The process of gaining insights from data, finding meaning in the numbers, and then turning those meaningful numbers into success stories.

    Data Analytics

  • 10

    a process of understanding the scope, objectives, and complexities of business projects.

    Business Analytics

  • 11

    simple collection of data or a data file

    DATA SET

  • 12

    a collection of data files that contain information on people, locationsa collection of data files that contain information on people, locations

    DATABASE

  • 13

    hardware and software used for data remote storage, retrieval, and computational functions

    COMPUTER CLOUD

  • 14

    - A way of organizing the elements of a dataset

    DATA MODEL

  • 15

    Presenting information graphically, to illustrate trends and patterns

    DATA VISUALIZATION

  • 16

    Finding and removing data points that are inaccurate or irrelevant

    DATA CLEANSING

  • 17

    the smaller data segments or files that help individual businesses keep track of customers.

    LITTLE DATA

  • 18

    the collection of data sets that are so large and complex that software systems are hardly able to process them

    BIG DATA

  • 19

    the most frequently used software for databases

    EXCEL

  • 20

    allows analysts to quickly extract specific data from tables

    SQL

  • 21

    relatively easy to use, and makes large amounts of information easier to manipulate.

    PHYTON

  • 22

    specifically designed for data mining and completing statistical functions.

    R

  • 23

    BENEFITS

    READ

  • 24

    APPLICATION

    READ

  • 25

    Steps on how to apply DATA ANALYTICS

    READ

  • 26

    the framework for computer science, information systems, communication networks and their applications in various organized humanitarian work.

    INFORMATION TECHNOLOGY

  • 27

    The 21st century is defined by a tremendous technological revolution resulting to a terrible increase in the volume of data.

    INFORMATION TECHNOLOGY

  • 28

    a subset of Al that involves algorithms that can learn on their own

    MACHINE LEARNING

  • 29

    ensure that your data is high-quality and that you collect it in a central Data Management Plan.

    DATA MANAGEMENT

  • 30

    enables sifting through large datasets and figure out what's relevant.

    DATA MINING

  • 31

    analyze historical data to predict future outcomes

    PREDICTIVE ANALYTICS

  • 32

    can isolate and utilize the information to business benefit

    KNOWLEDGE DISCOVERY

  • 33

    useful for filtering, aggregation, and analysis of big data

    STREAM ANALYTICS

  • 34

    allow businesses to streamline data across a number of big data solution

    DATA INTEGRATION

  • 35

    can conduct cleansing and enrichment of large data sets

    DATA QUALITY