Online Transaction Processing (OLTP)
OLTPs are designed for short online data transactions (such as INSERT, DELETE, UPDATE, etc.) with an emphasis on fast query processing and maintaining data integrity in a multi-access environment. Contrast with OLAP
systems, which are designed for more complex operations on historic data.
#data_science_glossary
#Part_38
OLTPs are designed for short online data transactions (such as INSERT, DELETE, UPDATE, etc.) with an emphasis on fast query processing and maintaining data integrity in a multi-access environment. Contrast with OLAP
systems, which are designed for more complex operations on historic data.
#data_science_glossary
#Part_38
Operational Data Store (ODS)
An ODS system integrates operational or transactional data from multiple systems to support operational reporting.
#data_science_glossary
#Part_39
An ODS system integrates operational or transactional data from multiple systems to support operational reporting.
#data_science_glossary
#Part_39
Prediction
In the context of data science and machine learning, the task of estimating
the value of a target attribute for a given instance based on the values of other
attributes (or input attributes) for that instance.
#data_science_glossary
#Part_40
In the context of data science and machine learning, the task of estimating
the value of a target attribute for a given instance based on the values of other
attributes (or input attributes) for that instance.
#data_science_glossary
#Part_40
Raw Attribute
An abstraction from an entity that is a direct measurement taken from the
entity—for example, a person’s height. Contrast with derived attribute.
#data_science_glossary
#Part_41
An abstraction from an entity that is a direct measurement taken from the
entity—for example, a person’s height. Contrast with derived attribute.
#data_science_glossary
#Part_41
Regression Analysis
Estimates the expected (or average) value of a numeric target attribute when
all the input attribute values are fixed. Regression analysis assumes a parameterized mathematical model of the hypothesized relationship between the
inputs and output known as a regression function. A regression function may
have multiple parameters, and the focus of regression analysis is to find the
correct settings for these parameters.
#data_science_glossary
#Part_42
Estimates the expected (or average) value of a numeric target attribute when
all the input attribute values are fixed. Regression analysis assumes a parameterized mathematical model of the hypothesized relationship between the
inputs and output known as a regression function. A regression function may
have multiple parameters, and the focus of regression analysis is to find the
correct settings for these parameters.
#data_science_glossary
#Part_42
Relational Database Management System (RDBMS)
Database management systems based on Edgar F. Codd’s relational data model.
Relational databases store data in collection of tables where each table has a structure of one row per instance and one column per attribute. Links between
tables can be created by having key attributes appear in multiple tables. This
structure is suited for SQL queries which define operations on the data in the
tables.
#data_science_glossary
#Part_43
Database management systems based on Edgar F. Codd’s relational data model.
Relational databases store data in collection of tables where each table has a structure of one row per instance and one column per attribute. Links between
tables can be created by having key attributes appear in multiple tables. This
structure is suited for SQL queries which define operations on the data in the
tables.
#data_science_glossary
#Part_43
Smart City
Smart-city projects generally try to integrate real-time data from many different data sources into a single data hub, where they are analyzed and used to
inform city-management and planning decisions.
#data_science_glossary
#Part_44
Smart-city projects generally try to integrate real-time data from many different data sources into a single data hub, where they are analyzed and used to
inform city-management and planning decisions.
#data_science_glossary
#Part_44
Structured Data
Data that can be stored in a table. Every instance in the table has the same set
of attributes. Contrast with unstructured data.
#data_science_glossary
#Part_45
Data that can be stored in a table. Every instance in the table has the same set
of attributes. Contrast with unstructured data.
#data_science_glossary
#Part_45
Structured Query Language (SQL)
An international standard for defining database queries.
#data_science_glossary
#Part_46
An international standard for defining database queries.
#data_science_glossary
#Part_46
Supervised Learning
A form of machine learning in which the goal is to learn a function that maps
from a set of input attribute values for an instance to an estimate of the missing value for the target attribute of the same instance.
#data_science_glossary
#Part_47
A form of machine learning in which the goal is to learn a function that maps
from a set of input attribute values for an instance to an estimate of the missing value for the target attribute of the same instance.
#data_science_glossary
#Part_47
Target Attribute
In a prediction task, the attribute that the prediction model is trained to estimate the value of.
#data_science_glossary
#Part_48
In a prediction task, the attribute that the prediction model is trained to estimate the value of.
#data_science_glossary
#Part_48
Transactional Data
Event information, such as the sale of an item, the issuing of an invoice, the
delivery of goods, credit card payment, and so on.
#data_science_glossary
#Part_49
Event information, such as the sale of an item, the issuing of an invoice, the
delivery of goods, credit card payment, and so on.
#data_science_glossary
#Part_49
Unstructured Data
A type of data where each instance in the data set may have its own internal
structure; that is, the structure is not necessarily the same in every instance.
For example, text data are often unstructured and require a sequence of operations to be applied to them in order to extract a structured representation
for each instance.
#data_science_glossary
#Part_50
A type of data where each instance in the data set may have its own internal
structure; that is, the structure is not necessarily the same in every instance.
For example, text data are often unstructured and require a sequence of operations to be applied to them in order to extract a structured representation
for each instance.
#data_science_glossary
#Part_50
Unsupervised Learning
A form of machine learning in which the goal is to identify regularities in the
data. These regularities may include clusters of similar instances within the
data or regularities between attributes. In contrast to supervised learning, in
unsupervised learning no target attribute is defined in the data set.
#data_science_glossary
#Part_51
A form of machine learning in which the goal is to identify regularities in the
data. These regularities may include clusters of similar instances within the
data or regularities between attributes. In contrast to supervised learning, in
unsupervised learning no target attribute is defined in the data set.
#data_science_glossary
#Part_51
algorithms-14-00186-v2.pdf
2.7 MB
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#python
#data_science
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👇👇👇
https://t.me/datalook_ir