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Beschreibung
Quiz von Carolina Colorado, aktualisiert more than 1 year ago
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Erstellt von Carolina Colorado
vor mehr als 7 Jahre
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Big data analysis differs from trditional data analysis primary because
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volume, value and varirety
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volume, velocity and variety
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veracity, volume and velocity
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In big data analysis and analytics, a fundamental step-by-step process is needed to organize the task involved
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retrieving, processing, producing and visualization data
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retrieving, processing, producing and repurposing data
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retrieving, processing, organize and repurposing data
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which are 1,2 and 3 stages of bigData analysis lifecycle
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Data analysis, data identification and ata extraction
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bussines cased evaluation, data extraction and data analysis
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bussines case evaluation, data identificaction and data acquisition and filtering
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which are 4,5 and 6 stages of bigData analysis lifecycle
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data analysis, data visualization & utilization of analysis results
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data extraction, data validation & cleansing and data aggregation & representation
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data extraction, data aggregation & representation
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which are 7,8 and 9 stages of bigData analysis lifecycle
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data analysis, data visualization and utilization of analysis results
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data aggregation & represntation, data analyisis and data visualization
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data identifcation, data acquisition & filtering and data extraction
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The business case evaluation stage requires that a business case be ________, __________ and ______________ prior to proceeding with the actual hands-on analysis tasks.
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organized, created and approbed
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created, assessed and approbed
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created, organized and analyzed
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An evaluation of a Big Data analysis bussines case helps decision-makers undertand
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the business resources that will need to be utilized and wich bussines challenges the analysis
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the data that will need to be utilized and wich bussines challenges the analysis
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the business resources that will need to be utilized and wich bussines objectives the analysis
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The KPIs is ussefull in Business Case Evaluation
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true
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false
Frage 9
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based on the business requirements documented in the _______________________________ , it can be determined whether the business problems being addresed are really Big data problems
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use case
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business case
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requirements case
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a bussines problem needs to be directly related to one or more of the big data characteristics
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veracity, velocity or variety
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value, velocity or variety
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volume, velocity or variety
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Another outcome in Business case evaluation is
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determination of budget required to carry out the analysis project
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determination of data required to carry out the analysis project
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determination of resources required to carry out the analysis project
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The invesment can be weighed against the expected benefits of achieving the goals
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false
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true
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Initial iterations of the big data analysis lifecycle will not required more up-front invesment of Big Data tecnologies, products and training compared to later iterations
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false
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true
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The data identification stage is dedicated to identifiying the _____________ required for the analysis project
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metadata
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datasets
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datamart
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identifying a wider variety of data sources may increase the probability of finding
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hidden patterns and aggregations
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hidden patterns and correlations
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hidden resources and datasets
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Can be beneficial to identify as many types of releated data sources and insights as possible, especilly when we don´t know exactly what we're looking for.
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TRUE
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FALSE
Frage 17
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Depending on the business scope of analysis project and nature of business problems being adressed, the required dataset and their sourcescan be
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structured and not structured
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big or small of all enterprise
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internal or external to enterprise
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Internal dataset
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data markets and publicly avalaible datasets
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internal sources, such as data marts and operational system
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embedded within blogs or other types of content-based websites
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external datasets
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strudtured data, unstructured data
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data marts and operational systems
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Case they may need to be harvested via automated tools
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data is gathered from all of data sources that were identified during the previous stage, and is then subjected to the automated filtering of corrupt data or data that has been deemed to have no value to the analysis obvjectives
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Data identification
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data acquisition & filtering
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Data aggregation & representation
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Depending on the type of data source , data may come as a dump of files or may require API integration
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false
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true
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in many cases , especially where external, unstructured data is concerned, some or most of the acquired data may be irrelevant (noise) and can be discarded as part of the filtering process
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Data acquisition & filtering
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Data extraction
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Data validation & cleansing
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data classified as "corrupt" can include records with missing or nonsensical values or invalid data type
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false
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true
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Data thah is filtered out for one analysis may not be valueable for a different type of analysis
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false
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true
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it is advisable to store a verbatim copy of the original dataset proceeding with the filtering.
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To save on required storage space, the verbatim copy is compressed after storage
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To save on required storage space, the verbatim copy is compressed before storage
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To save on required storage space, the verbatim copy is compressed in the same time of storage
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to be persisted once it gets generated or enters the enterprise boundary
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internal data
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internal and external data
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external data
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The data is persisted to disk prior to analysis
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realtime analytics
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Batch anlytics
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realtime analytics and batch analytics
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The data is analyzed first and then persisted to disk
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realtime analytics
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batch analytics
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realtime analytics and batch analytics
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Can be added via automation to data from both internal and external data sources to improve the classification an querying
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info data
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data analysis
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metadata
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Metadata example can include
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datamart size and structure, source information, date and time of creation or collection, language-specific information etc.
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database size and structure, source information, date and time of creation or collection, language-specific information etc.
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dataset size and structure, source information, date and time of creation or collection, language-specific information etc.
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it is vital that metadata be machine-readable and passed forward along subsequent analysis stages
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false
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true
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This helps to maintain data provenance throughout the Big Data analysis lifecycle, wich helps establish and preserve data accuracy and quality
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metadata
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source information
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date and time of creation or collection
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Some of the data identified as input for the analysis may arrive in a format incompatible with the big data solution
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true
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false
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the need to address disparate types of data is more likely with data from
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internal sources
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external sources
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is dedicated to extracting disparate data and transforming it into a format that the underliying Big Data solution can use for the purpose of the data analysis
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Data acquisition & filtering
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data validation & cleansing
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data extraction
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The extent of extraction and transformation required depends
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on the types of analytics and capabilities of the Big Data solution
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bussines case
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Data extraction
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Estracting the required fields from delimited textual data (such as with web server log files) may not be necessary
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capabilities of the Big Data Solution
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underlying Big Data solution can already directly process those files
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transforming it into a format that underlying Big Data solution
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example of document that not need further transformation
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XML and JSON
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facebook and twitter
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image and video
Frage 39
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The invalid data can
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Skew an falsify analysis results
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Lose business objectives
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Lose the accuracy of the analysis
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data input into Big Data analyses can be unstructured without any indication of validity
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false
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true
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the complexity can further make it easy to arrive at a set of suitable validation constraint
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false
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true
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Is dedicated to establishing (often complex) validation rules and removing any know invalid data
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Data acquisition and filtering
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Data validation and cleansing
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Data identification
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Big Data solutions often receive redundant data across different datasets, this redundancy can be exploited to explore interconnected datasets in order to assemble validation parameters and fill in missing valid data
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false
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true
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For Batch analytics, data validation and cleansing can be achieved via offline
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data minnig
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ELT operation
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ETL operation
Frage 45
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Data input in Big Data can be unstructured without any indication of validity
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false
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true
Frage 46
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provenance can play an important role in determining the accuracy and quality of questionable data
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false
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true
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data that appears to be invalid may still be valuable in that it may posses
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most important data
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hidden patterns an trends
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noise
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Data may be spread across multiple datasets, requiring that datasets be joined together via common files (date or ID)
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false
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true
Frage 49
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either way a method of data ________________ is required or the dataset representing ther correct value needs to be determined
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aggregation
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reconciliaton
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representation
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Dedicated to integrating multiple datasets together to arrive at a unified view
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Data aggregation & representation
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Data extraction
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Data visualization
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Can become complicated because od differences in : although the data format may be the same, the data model may be different
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semantics
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BD engine
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Data structure
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Can become complicated because od differences in : A valuethat is labelled differently in two different datasets may mean the same thing (surname and last name)
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BD engine
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Semantics
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Data structure
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In data Aggregation & Representation reconciling the differences can required complex logic that is executed ___________________.
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ETL process
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human intervention
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automatically
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Future data analysis requirements need to be considered during the stage ___________________ to help foster data reusability
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Data extraction
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Data aggregation & REpresentation
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Data validation and cleansing
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whether ___________________ is required or not, it is important to understand that the same data can be stored in many different forms. One form may be better suited for a particular type of analysis than another
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data cleansing
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data aggregation
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filtering
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A data structured standarized by the Big Data solution can require establishing a central, standard analysis repository, such as a
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untructured database
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structured database
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NoSQL database
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the data analysis stage is dedicated to carriying out the actual analysis task, which typically involves one or more types of analytics
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data validation %cleansing
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Data Analysis
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Utilization os analysis results
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This stage can be iterative in nature, because repeated until appropiated pattern or correlation is uncovered
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data aggregation & representation
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Data analysis
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Data extraction
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The approach taken when carrying out this stage, data analysis, an be classified as ______________________________
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acquisition analysis and filtering analysis
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confirmatory analysis and exploratory analysis
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validation analysis and cleansing
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___________________________ adata analysis is a deductive approach where the cause of the phenomenon being investigated is proposed beforehand
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Confirmatory analysis
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Exploratory analysis
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Data analysis
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the proposed cause or assumption is called a
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pattern and trend
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deductive approach
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hypotesis
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data samples are tipically used
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exploraty analysis
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confirmatory analysis
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unexpected findings or anomalies are usually ignored since a predetermined cause was assumed
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true
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false
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is an inductive approach that is closely associated to data mining
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exploratory data analysis
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confirmatory data analysis
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correlation analysis
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this analysis provides a general direction that can facilitate the discovery of patterns or annomalies
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confirmation analysis
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Exploratory analysis
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Large amounts of data and visual analysis are typically used
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Confirmatory analysis
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Exploratory analysis
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is dedicated to using _____________________ techniques and tools to graphically communicate the analysis results for effective interpretation by bussines users
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Data analysis
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Data visualization
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Utiolization of analysis results
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Bussines users needs to be able to understand the results in order to obtain value from analysis and subsequently have de ability to provide feedback from_______________ back to stage __________________
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Data validation and cleaning, data extraction
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Data analysis, data aggregation & representation
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Data visualization, Data analysis
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the same results may be presented ina a number a number of different ways.
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false
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true
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another aspect to keep in mind is that providing a method of drilling down to comparatively simple statistics is crucial, in order for users to understand how to statistics were generated
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true
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false
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support businessdecission-making, there may be further opportunieties to utilize the analysis results
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Utilization of analysis results
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Data visualization
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Data analysis
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The utilization os analysis results is dedicated to determining how and where processed analysis data can be further leveraged
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Utilization of analysis results
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Data visualization
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Data analysis
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"models" that encapsulated new insights and understandings about the nature of the patterns and realationships that exist within data that was just analyzed
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utilization of analysis results
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Data analysis
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Data validation &cleansing
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A "model" may look like a
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mathematical equation or a set of rules
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structred database
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the differents datasets
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Models can be used to improved bussines process logic
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new dataset
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form the basis of a new system or software program
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application system logic
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new bussines case
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the data analysis results may be automatically or manually fed directly into enterprise systems to enhace and optimize their behavior and performance
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input for enterprise systems
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Bussines process optimization
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Alerts
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The identiffied patterns correlations and anomalies discovered during the data analysis are used to refine business process
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input for enterprise input
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alerts
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Bussines process optimization
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Data analysis results can be used as input for existing events that requires them to take corrective action
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input for enterprise input
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business process optimization
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alerts
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Big data nalysis concepts
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statical
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aggregation
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visual
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machine learning
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Semantic
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Topic mapping
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feelings
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statistical analysis
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A/B Testing
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heat maps
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correlation
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Regression
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filtering
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visual Analysis
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heat maps
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outlier detection
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time series analysis
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Spatial Data Analysis
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Network analysis
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machine learning
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correlation
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clasification
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clustering
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outlier detection
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filtering
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regression
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semantic analysis
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classification
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network analysis
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Natural language processing
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text analytics
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sentiment analysis
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use statistical methods based on mathematical formulas as means for analizing data
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visual analysis
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statistical analysis
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machine learning
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it can also be used to infer patterns ans relationships within the dataset, such as regression and correlation
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statistical analysis
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semantic analysis
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analysis topic mapping
Frage 86
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also know as split or bucket testing, compares two versions of an element to determine wich version is superior based on a predefined metric
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correlation
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A/B testing
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regression
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A/B testing: the current version of the element is called the ______________ version, whereas the modified version is called the ____________
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official, non official
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control,reatment
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principal, copy
Frage 88
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both version, are subjected to an experiment simultaneously. The observationsare recorded to determine wich version is more sccessful
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correlation
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Regression
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A/B testing
Frage 89
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Athough ________________________can be implemented in almost domain, it is most often used in marketing
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A/B Testing
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Regression
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Correlation
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Generally, the objective is to gauge human behavior with the goal of increasing sales (as per the example below)
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Regression
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A/B testing
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Correlation
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is the new version of a drug better than the old one?
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correlation
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Regression
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A/B testin
Frage 92
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is an analysis tecnique used to determine whether two variables are related to each other
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Regression
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Correlation
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A/B testing
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an example of a relationship between two variables:
The value of variable A increases whenever the value of variable B increases
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Regression
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A/B testing
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Correlation
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Helps to develop an understanding of a dataset and find relationships that can assist in explaining a phenomenon
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Correlation
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Regression
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A/B testing
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commonly used for data mining where the identification between variables in a dataset leads to the discovery of patterns ans anomalies
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regression
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correlation
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A/B testing
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When two variables are considered to be correlated they are considered to be aligned based on a linear relationship
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false
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true
Frage 97
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This mean that when one variable changes, the other variable also changes proportionally and constantly
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A/B testing
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regression
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correlation
Frage 98
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______________________ is expresed a a decimal number between -1 to 1, which is know as the correlation coeficient
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Correlation
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Regression
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A/B testing
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Correlation +1
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Suggest that there is a strong positive relationship between the two variables
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suggests that there is no relationship at between two variables
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Suggest that there is a strong negative relationship between the two variables (hipotesis)
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0 Correlation
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Suggest that there is a strong positive relationship between the two variables
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suggests that there is no relationship at between two variables
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Suggest that there is a strong negative relationship between the two variables (hipotesis)
Frage 101
Frage
-1 Correlation
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Suggest that there is a strong negative relationship between the two variables (hipotesis)
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suggests that there is no relationship at between two variables
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Suggest that there is a strong positive relationship between the two variables
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sample: "Do students who perform well at elementary school perform equally well at high school"
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regression
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Correlation
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A/B testin
Frage 103
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explores how a dependent variable is related to an independent variable within a dataset
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Correlation
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Regression
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A/B Testing
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Helpss determine how the value od dependent variable changes in relation to changes in the value of the independent varible
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Correlation
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Regression
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A/B testing
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what the analysts discover is that 15% of additional stock in required for enery 5-degree increase in temperature
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regression
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correlation
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A/b testing
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more than one independent variable can be tested at the same time
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A/B testing
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Regression
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correlation
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in such cases only one independent variable may change. The others are kept constants
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A/B testing
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Correlation
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Regression
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can help enable a better understanding of what a phenomenin is and why it ocurred
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Correlation
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Regression
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A/B testing
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represents a constant rate of change
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linear regression
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Non-linear regression
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Represents the variable rate of change
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linear regression
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non-linear regression
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what will be the grades of a student studying at a high school based on her primary school grades
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correlation
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regression
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A/B testing
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_________________does not imply a causation. The change in the value of one variable may not be responsible for the change in the value of the second variable. although both may change at the same rate
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A/B testing
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correlation
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Regression
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assumes that both variables are independent
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Regression
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correlation
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A/B testing
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Deals with already identified dependent and independent variables
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Correlation
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Regression
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A/B Testing
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_________________ can be applied to further explore the relationship and predict the values of the dependent variable, based on the know values of the independent variable
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correlation
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Regression
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A/B testing
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is a form of data analysis that involves the graphic representation of data to enable or enhace its visual perception
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statistical analysis
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visual analysis
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semantic analysis
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develop a deeper understanding of the data being analyzed. Specifically, it helps identify and highlight hidden patterns, correlations and anomalies.
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statistical analysis
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visual analysis
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semantic analysis
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visual analysis
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Heat maps
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time series analysis
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outlier detectition
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network analysis
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spatial data analysis
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Are an effective visual analysis technique for expressing patterns, data compositions via part-whole relations and geographic distribution of data
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time series analysis
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heat maps
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spatial data analysis
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They also facilitate the identification of areas of interest ans the discovery of extreme (high/low) values wihin a dataset
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Network analysis
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heat maps
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spatial data analysis
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___________ itself is a visual, color-coded representation of data values
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network analysis
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heat-maps
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spatial data analysis
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time series analysis
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A _______________ can be in the form of a chart or a map, as shown in the following pages
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heat maps
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time series analysis
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network analysis
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spatial data analysis
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A___________ represents a matrix of values in which each cell is color-coded according to the value
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chart
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map
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A ___________ represents a geographic measure by wich different regions are color-code according to certain theme
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chart
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map
Frage 125
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How can i visually identify any patterns related to carbon emission across a large number of cities around the world
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Heat maps
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time series analysis
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network analysis
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spatial data analysis
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____________is the analysis of data that is recorded over periodic intervals of time
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heat maps
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time series analysis
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network analysis
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spatial data analysis
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Helps to uncover patterns within data that are time-dependent. Once identified, the patterns can be axtrapollated for future predictions
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heat maps
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time series analysis
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network analysis
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spatial data analysis
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time series analyses are usually used for forecasting by identifiying long-term trends. seasonal periodic patterns and irregular short-term variations in the dataset
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time series analysis
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heat map
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network analysis
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spatial data analysis
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always includes time as a comparision variable
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network analysis
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heat maps
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time series analysis
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spatial data analysis
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is generally expressed using a line chart, with time plotted on the x-axis and the recorded data values plotted on the y-axis
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time series analysis
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heat map
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network analysis
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spatial data analysis
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how much yield should the farmer expect based on historical yield data
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network analysis
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spatial data analysis
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heat maps
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time series analysis
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is an interconected collection of entities
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heat maps
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time series analysis
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network analysis
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spatial data analysis
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An entity can be a person a group or some other business domain object such as a product
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spatial data analysis
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heat maps
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time series analysis
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network analysis
Frage 134
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some conectios may only be one-way, so that transversal in the reverse direction is nor possible
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true
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false
Frage 135
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is a techniquethat focuses on analizing relationships between entities within the network
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time series analysis
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heat maps
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network analysis
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spatial Data analysis
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There are specialized variations of network analysis
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Graphs
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route optimization
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social network analysis
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spread predictions
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is used to find the shortest routes between the central warehouse and remote stores in order to minimize the durations of deliveries
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heat map
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network analysis
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spatial data analysis
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time series analysis
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How can identify interaction patterns among a very large number of protein-to-protein interactiona?
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spatial data analysis
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network analysis
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heat maps
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time series analysis
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is focused on analizing location-based data in order to find different geographic relationships and patterns between entities
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network analysis
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spatial data analysis
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time series analysis
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Heat maps
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____________________________ is manipulated through a geographical information system (Gis) that plots spatial data on a map generally using its longitude and latitude coordinates
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Spatial data
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structured data
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unstructured data
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no two stores can be within a distance of 5 kilometers of each other to prevent the stores from competing with each other.
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time series analysis
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network analysis
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heat map
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spatial data analysis
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how far do customers have to commute in order to get to a supermartket?
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spatial data analysis
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heat maps
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time series analysis
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network analysis
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if the human knowledge can be combined with the processing speed of machines, machines will be able to process large amounts of data without requiring much human intervention
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statisctical analysis
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visual nalysis
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machine learning
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semantic analysis
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machine learning
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classification
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time series analysis
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clustering
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outlier detection
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filtering
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Two fundamental laws that pertain to machine learning
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law of large numbers
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law commutative
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Law of dimishing marginal utility
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the law _____________________________states that the confidence with wich predictions can be made increases as the size of data that is being analyzed increases
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law of large numbers
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law of dimishing marginal utility
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in other words the accuracy and applicability of the patterns and relationshipsthat are found in a large dataset will be higher that of a smaller dataset
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- True
- False
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the greater the amount of data available for analysis, the better we become of making correct decisions
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- True
- False
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in the context of traditional data analysis, ___________________________ states that, starting with a reasonably large sample size, the value obtained from the analysis of additional data decreases as more data is successively added to the original sample
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the law of diminishing marginal utility
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the law of large number
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The law of dimishing marginal utility does not apply to big data
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- True
- False
Frage 151
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The greater the volume and variety of data that Big Data solutions can process allows for each additional batch of data to carry greater potential of unearthing new patterns and anomalies. Therefore, the value of each additional batch does not diminish value: rather, it provides more value
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- True
- False
Frage 152
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is a supervised learning technique by witch data is classified into relevant, previously learned categories
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classification
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clustering
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outlier detection
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filtering
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Step 1: The system is fed data that is already categorized or labeled, so that it can develop an understanding of different categories
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clustering
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classification
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filtering
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outlier detection
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step 2: The system is fed unknow (but similar) data for classification, based on the understanding it developed
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classification
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filtering
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outlier detection
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clustering
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A common application of this techniques is for the filtering of e-mail spam. Note that classification can be performed for two or more categories
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filtering
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clustering
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classification
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outlier detection
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Based on old data, a training dataset is compiled that contains tagged examples of customers that have or not previously defaulted
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clustering
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filtering
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classification
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outlier detection
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Does a fingerprint belong to a suspect based on a record of this previous fingerprints
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outlier detection
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clustering
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classification
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filtering
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Is an unsupervised learning technique by wich data is divided into different groups so that the data in each group has similar properties
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classification
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clustering
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outlier detection
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filtering
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There is no prior learning of categories required: instead categories are implicity generated based on the data groupings
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outlier detection
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clustering
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filtering
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classification
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Is generally used in data minig to get an understanding of properties of a given dataset. Afterdeveloping this understanding, classificatioin can be used to make better predictions about similar, but new or unseen data
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classification
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clustering
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outlier detection
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filtering
Frage 161
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In a bank each group is the introduced to one or more financial products most suitable to the characteristics of the overall profile of the group
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clustering
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filtering
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outlier detection
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classification
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How many different categories of elements are there in the periodic table
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classification
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clustering
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outlier detection
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filtering
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Detection is the process of finding data that is significantly different from or inconsistent with the rest of the data within a given dataset
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filtering
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calssification
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clustering
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outlier detection
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this machine learning tecnique is used to identify anomalies, abnormalities and deviations that can be opportunities or risks
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outlier detection
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classification
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clustering
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filtering
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it can be bsaed on either supervised or unsupervised learning
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clustering
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outlier detection
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classification
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filtering
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include fraud detection, medical diagnosis, network data analysis and sensor data analysis
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filtering
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outlier detection
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classification
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clustering
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In order ti find if a transaction is likely to be fraudulent or not, the bank´s IT team builds a sustem emplying ____________________ technique that is based on supervised learning
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classificaction
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clustering
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outlier detection
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filtering
Frage 168
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are there any wrongly identified fruits and vegetables in the training dataset used for classification task
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classification
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outlier detection
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clustering
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filtering
Frage 169
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is the automated process of finding relevant items from a pool of items
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classification
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clustering
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outlier detection
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filtering
Frage 170
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items can be filtered either based on a users own behavior or by matching the behavior of multiple users
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classification
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clustering
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outlier detection
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filtering
Frage 171
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_________________ is generally applied viat the following two approaches
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collaborative filtering
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user behavior
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content-based filtering
Frage 172
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items can be filtered either based on a users own behavior or by matching the behavior of multiple users
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clustering
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filtering
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classification
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outlier detection
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A common medium by wich ________________is implemented is via the use of a recomender system
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classification
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clustering
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outlier detection
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filtering
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technique based on the collaboration of users past behavior
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collaborative filtering
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classification
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clustering
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outlier detection
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content-based filtering
Frage 175
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based on the similarityof users behavior, items are filtered for the target user
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classification
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clustering
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outlier detection
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filtering
Frage 176
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is solely based on the similarity between users behavior, and requires a large amount of user behavior data in order to accurately
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filtering
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classification
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clustering
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outlier detection
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filtering collaborative
Frage 177
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collaborative filtering is an example of application of law of large numbers
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- True
- False
Frage 178
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technique focused on the similarity between users an items
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classification
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clustering
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outlier detection
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filtering
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content-based filtering
Frage 179
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A user profile is created based on the users past behavior (likes, ratings, purchase history, etc)
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collaborative filtering
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content_based filtering
Frage 180
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Contrary to collaborative filtering, content-based filtering is solely dedicated to individual user preferences and does not require data about other users
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- True
- False
Frage 181
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A recomender system predicts user preferences and generate suggestions for the user accordingly
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filtering
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classification
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clustering
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outlier detection
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suggestions commonly pertain to recomending items, such as movies, books, web pages, people etc
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clustering
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classification
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filtering
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outlier dtection
Frage 183
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A recomender system typically uses either collaborative filtering or content-based filtering to generate suggestions
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- True
- False
Frage 184
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recommender system may also be based on a hybrid of both collaborative filtering and content-based filtering to fine-tune the accuracy and effectiveness of generated suggestions
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- True
- False
Frage 185
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Based on matches found between financial product purchased by customers and the properties of similar financial products, the recommnder system automates seggestion for potential financial products that customers may also be interested in
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clustering
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classification
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filtering
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outlier detection
Frage 186
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Wich holiday destinations can be recommended based on the travel history of a holiday makes?
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clustering
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classification
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outlier detetcion
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filtering
Frage 187
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A fragment of text or speech data can carry different meanings in different contexts, whereas a complete sentence may retain its meaning, even if structured in different ways. In order for the machines to extract valuable information, text and speech data needs to be understood by the machines in the same way as humans do. Semantic analysis represents practices for extracting meaningful information from textual and speech data
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statistical analysis
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semantic analysis
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visual analysis
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machinne learning
Frage 188
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types of semantic analysis
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natural language processing
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human behavior language
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text analytics
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sentimental analysis
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Is a computers ability to comprehend human speech and text as naturally understood by humans
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text analytics
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Natural language Processing
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sentiment analysis
Frage 190
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This allows computers to perfom a variety of useful task, such as full-text searches
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Text analysis
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sentiment analysis
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Natural language processiing (NLP)
Frage 191
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instead of hard-coding the required learning rules, either supervised or unsupervised machine learning is applied to develop the computer undestanding of the natural language
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text analysis
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natural language processing
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sentiment analysis
Frage 192
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in general the more learning data the computer has, the more correctly it can decipher human text and speech
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natural language Processing
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text analytics
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sentiment analysis
Frage 193
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Natural language processing includes both text and speech recognition
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- True
- False
Frage 194
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For speech recognition the system attempts to comprehend the speech and then performs an action, such as transcribing text
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text analytics
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sentiment analysis
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Natural language processing
Frage 195
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How can grammatical mistakes be automaticalle identified?
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text analytics
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Natural Language processing
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sentiment analysis
Frage 196
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Unstructured text is generally much more difficult to analyze and search, compared to structured text
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- True
- False
Frage 197
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is the specialized analysis of text through the application of data mining, machine learning and natural language processing techniques to extract value out of unstructured text. Text analytics essentially provides the ability to discover text rather than just search it
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Natural language processing
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text analytics
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sentimente analysis
Frage 198
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useful insights from text-based data can be gained by helping business develop an understanding of the information that is contained within a large body of text
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- True
- False
Frage 199
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the basic tenet of text analytics is to turn unstructured text into data that can be searched and analyzed
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Natural language processing
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text analytics
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sentiment analysis
Frage 200
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As the amount of digitized documents, e-mail, social media posts and log files increases, businesses have an increasing need to leverage any value that can be extracted from these forms of semi-structured and unstructured data
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text analytics
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natural language processing
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sentiment analysis
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