Podcast
Questions and Answers
Unstructured data does not have any associated data ______.
Unstructured data does not have any associated data ______.
model
Structured data is usually contained in rows and columns or key and ______.
Structured data is usually contained in rows and columns or key and ______.
values
Semi-structured data has defining or consistent characteristics but does not conform to a rigid ______.
Semi-structured data has defining or consistent characteristics but does not conform to a rigid ______.
structure
Quasi-structured data consists of textual content with erratic data ______.
Quasi-structured data consists of textual content with erratic data ______.
Signup and view all the answers
Getting data from things like sensor data harvesting and wire tapping falls under the category of data ______.
Getting data from things like sensor data harvesting and wire tapping falls under the category of data ______.
Signup and view all the answers
Data governance, data management, data security, data ethics, and data value chain are all aspects of data ______.
Data governance, data management, data security, data ethics, and data value chain are all aspects of data ______.
Signup and view all the answers
Data mining is the process of discovering meaningful new correlation, patterns, and trends by digging into large amounts of data using statistical, machine learning, artificial intelligence, and ______ techniques.
Data mining is the process of discovering meaningful new correlation, patterns, and trends by digging into large amounts of data using statistical, machine learning, artificial intelligence, and ______ techniques.
Signup and view all the answers
Algorithms are part of diagnostic analytics which discovers insights that could be the root cause of a problem or opportunity. Relevant techniques include: Statistical Analysis, Root cause Analysis, Data Storytelling, Machine Learning (Clustering or Classification) and ______.
Algorithms are part of diagnostic analytics which discovers insights that could be the root cause of a problem or opportunity. Relevant techniques include: Statistical Analysis, Root cause Analysis, Data Storytelling, Machine Learning (Clustering or Classification) and ______.
Signup and view all the answers
Predictive Analytics determines insights that could be a future problem or opportunity. Relevant techniques include: Statistical Analysis, Data Storytelling, Machine Learning (Regression/Prediction, Timeseries/Sequential analysis) and ______.
Predictive Analytics determines insights that could be a future problem or opportunity. Relevant techniques include: Statistical Analysis, Data Storytelling, Machine Learning (Regression/Prediction, Timeseries/Sequential analysis) and ______.
Signup and view all the answers
Knowledge is learned information in which coherence between past and future information are formed through the process of ______.
Knowledge is learned information in which coherence between past and future information are formed through the process of ______.
Signup and view all the answers
Usual analysis methods include Qualitative Analysis, Quantitative Analysis, Data Visualization, Numerical Analysis, Statistical Analysis, and ______ Insights.
Usual analysis methods include Qualitative Analysis, Quantitative Analysis, Data Visualization, Numerical Analysis, Statistical Analysis, and ______ Insights.
Signup and view all the answers
In diagnostic analytics, relevant techniques for discovering insights that could be the root cause of a problem or opportunity include Statistical Analysis, Root cause Analysis, Data Storytelling, Machine Learning (Clustering or Classification), and ______.
In diagnostic analytics, relevant techniques for discovering insights that could be the root cause of a problem or opportunity include Statistical Analysis, Root cause Analysis, Data Storytelling, Machine Learning (Clustering or Classification), and ______.
Signup and view all the answers
Functional Analysts utilize data and leverage on derived insights to help organizations make better decisions on a specific functional domain. Their expertise is in business and industry domains. Related job titles include research analyst, human resource analyst, marketing analyst, financial analyst, operations analyst. Analytics Manager ▪ Analytics Managers develop and guide data-driven projects – from initiation to planning, execution to performance monitoring, to closure. Their expertise is in project management. Related job titles include chief data officer, project manager, data engineering manager, data science manager, analytics translator. MLP Lec – Lec 2 Data Value Chain Everything is Connected Data - Data is any collection of facts or description of an entity. Birth of Data o Data is everywhere! You just gotta catch ‘em all (that is relevant to your problem)! Types of Data - - o o ______ (Quantitative) ▪ Data can be quantified or measured, typically represented through numeric values. This type of data can be subdivided to: Discrete Data Mainly integers, whole or natural numbers. Continuous Data - Ranges, decimals, or numbers with floating points Categoric (Qualitative) ▪ Types of data that are based on non-numeric characteristics. Can be further subdivided as: Ordinal - Data in which follows an order or a sequence.
Functional Analysts utilize data and leverage on derived insights to help organizations make better decisions on a specific functional domain. Their expertise is in business and industry domains. Related job titles include research analyst, human resource analyst, marketing analyst, financial analyst, operations analyst. Analytics Manager ▪ Analytics Managers develop and guide data-driven projects – from initiation to planning, execution to performance monitoring, to closure. Their expertise is in project management. Related job titles include chief data officer, project manager, data engineering manager, data science manager, analytics translator. MLP Lec – Lec 2 Data Value Chain Everything is Connected Data - Data is any collection of facts or description of an entity. Birth of Data o Data is everywhere! You just gotta catch ‘em all (that is relevant to your problem)! Types of Data - - o o ______ (Quantitative) ▪ Data can be quantified or measured, typically represented through numeric values. This type of data can be subdivided to: Discrete Data Mainly integers, whole or natural numbers. Continuous Data - Ranges, decimals, or numbers with floating points Categoric (Qualitative) ▪ Types of data that are based on non-numeric characteristics. Can be further subdivided as: Ordinal - Data in which follows an order or a sequence.
Signup and view all the answers
______ can be quantified or measured, typically represented through numeric values. This type of data can be subdivided to: Discrete ______ Mainly integers, whole or natural numbers. Continuous ______ - Ranges, decimals, or numbers with floating points Categoric (Qualitative) ▪ Types of data that are based on non-numeric characteristics. Can be further subdivided as: Ordinal - ______ in which follows an order or a sequence.
______ can be quantified or measured, typically represented through numeric values. This type of data can be subdivided to: Discrete ______ Mainly integers, whole or natural numbers. Continuous ______ - Ranges, decimals, or numbers with floating points Categoric (Qualitative) ▪ Types of data that are based on non-numeric characteristics. Can be further subdivided as: Ordinal - ______ in which follows an order or a sequence.
Signup and view all the answers
Mainly integers, whole or natural numbers. Continuous Data - Ranges, decimals, or numbers with floating points Categoric (Qualitative) ▪ Types of data that are based on non-numeric characteristics. Can be further subdivided as: Ordinal - Data in which follows an order or a sequence.
Mainly integers, whole or natural numbers. Continuous Data - Ranges, decimals, or numbers with floating points Categoric (Qualitative) ▪ Types of data that are based on non-numeric characteristics. Can be further subdivided as: Ordinal - Data in which follows an order or a sequence.
Signup and view all the answers
Ranges, decimals, or numbers with floating points Categoric (Qualitative) ▪ Types of data that are based on non-numeric characteristics. Can be further subdivided as: Ordinal - Data in which follows an order or a sequence.
Ranges, decimals, or numbers with floating points Categoric (Qualitative) ▪ Types of data that are based on non-numeric characteristics. Can be further subdivided as: Ordinal - Data in which follows an order or a sequence.
Signup and view all the answers
Types of data that are based on non-numeric characteristics. Can be further subdivided as: Ordinal - Data in which follows an order or a sequence.
Types of data that are based on non-numeric characteristics. Can be further subdivided as: Ordinal - Data in which follows an order or a sequence.
Signup and view all the answers
Data in which follows an order or a sequence.
Data in which follows an order or a sequence.
Signup and view all the answers
