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<title>Plikli CMS / amayra / New</title>
<link>http://affiliated-business.com</link>
<description>Your Source for Social News and Networking</description>
<pubDate>Sat, 20 Aug 2022 06:31:25 +0000</pubDate>
<language>en</language>
<item>
	<title><![CDATA[What are frames and scripts in “Artificial Intelligence”]]></title>
	<link>http://affiliated-business.com/story.php?title=what-are-frames-and-scripts-in-“artificial-intelligence”</link>
	<source url="http://affiliated-business.com/story.php?title=what-are-frames-and-scripts-in-“artificial-intelligence”"><![CDATA[What are frames and scripts in “Artificial Intelligence”]]></source>
	<description><![CDATA[Semantic networks, one of the common ways to present non-procedural knowledge in an expert system, have a variant known as frames. By representing "stereotyped situations," a frame, an artificial data structure, is used to segment knowledge into substructure. With the exception of the requirement for ordered values, scripts are analogous to frames. In order to organise a knowledge base in terms of the situation that the system should understand, natural language understanding systems use scripts. ]]></description>
	<pubDate>Sat, 20 Aug 2022 06:31:25 +0000</pubDate>
	<author>amayra</author>
	<category>News</category>
	<votes>1</votes>
	<guid>http://affiliated-business.com/story.php?title=what-are-frames-and-scripts-in-“artificial-intelligence”</guid>
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<item>
	<title><![CDATA[Data reporting and data analysis have different purposes]]></title>
	<link>http://affiliated-business.com/story.php?title=data-reporting-and-data-analysis-have-different-purposes</link>
	<source url="http://affiliated-business.com/story.php?title=data-reporting-and-data-analysis-have-different-purposes"><![CDATA[Data reporting and data analysis have different purposes]]></source>
	<description><![CDATA[<br /><br />In order to avoid drawing conclusions and to help the user gain a sense of the facts, a report will outline what previously occurred. Analysis, on the other hand, will offer solutions to any queries or problems. Any actions required to obtain the solutions to those questions are taken during an analytical process.<br /><br />Reporting merely offers the requested data, whereas analysis really provides the information or solution.<br /><br />We use a conventional reporting process, but we can tailor the analysis. There are set standard reporting formats, and we do the analysis in accordance with the requirements, customizing it as necessary.<br /><br />We can perform reporting using a tool, and the analysis is often done without any human involvement. While someone else is in charge of conducting the analysis and overseeing the entire process.<br /><br />While analysis is adaptable, reporting is not. Reporting is rigid because it offers little to no context for what is happening in the data, whereas analysis highlights data points that are significant, distinctive, or unique and explains why they are crucial to the organization.<br /><br />We use a conventional reporting process, but we can tailor the analysis. There are set standard reporting formats, and we do the analysis in accordance with the requirements, customizing it as necessary.<br /> ]]></description>
	<pubDate>Wed, 17 Aug 2022 12:14:43 +0000</pubDate>
	<author>amayra</author>
	<category>News</category>
	<votes>1</votes>
	<guid>http://affiliated-business.com/story.php?title=data-reporting-and-data-analysis-have-different-purposes</guid>
</item>

<item>
	<title><![CDATA[Global Health Expenditure Analysis]]></title>
	<link>http://affiliated-business.com/story.php?title=global-health-expenditure-analysis</link>
	<source url="http://affiliated-business.com/story.php?title=global-health-expenditure-analysis"><![CDATA[Global Health Expenditure Analysis]]></source>
	<description><![CDATA[Comparative information on health spending for 192 nations over the past 20 years is provided through a worldwide analysis of health expenditure. In this project, Power BI clustering analysis is implemented using PyCaret, a Python machine learning toolkit. Gathering data items with related properties is known as clustering. These classifications support data analysis, pattern detection, and dataset examination, while data clustering supports the discovery of underlying data structures.<br />Comparative information on health spending for 192 nations over the past 20 years is provided through a worldwide analysis of health expenditure. In this project, Power BI clustering analysis is implemented using PyCaret, a Python machine learning toolkit. Gathering data items with related properties is known as clustering. These classifications support data analysis, pattern detection, and dataset examination, while data clustering supports the discovery of underlying data structures.<br /> ]]></description>
	<pubDate>Sat, 13 Aug 2022 08:44:01 +0000</pubDate>
	<author>amayra</author>
	<category>News</category>
	<votes>1</votes>
	<guid>http://affiliated-business.com/story.php?title=global-health-expenditure-analysis</guid>
</item>

<item>
	<title><![CDATA[Future of Data Science]]></title>
	<link>http://affiliated-business.com/story.php?title=future-of-data-science-5</link>
	<source url="http://affiliated-business.com/story.php?title=future-of-data-science-5"><![CDATA[Future of Data Science]]></source>
	<description><![CDATA[A vast collection of various data operations make up data science. Additionally, statistics and machine learning are used in these data operations. Algorithms for machine learning rely heavily on data. In order to fine-tune our model with different algorithmic parameters, this data is fed to it as a training set and a test set.<br /><br />Undoubtedly, improvements in machine learning will be crucial to the development of data science in the future.<br /><br />In particular, Data Science also covers:<br /><br />Data Integration.<br />Distributed Architecture.<br />Automating Machine learning.<br />Data Visualization.<br />Dashboards and BI.<br />Data Engineering.<br />Deployment in production mode<br />Automated, data-driven decisions.<br /><br />i. Because it involves so many different data operations, data science currently lacks a clear definition. Future data operations will only become more prevalent. However, because it will only include crucial components that characterize the core of data science, the definition of data science will become more limited and specific.<br /><br />ii. In the near future, data scientists will be able to tackle a number of difficult problems as well as those that are business-critical. The businesses will be able to grow exponentially as a result of this. Companies currently face a severe lack of data scientists. Future events, however, are expected to change this.<br /><br /> ]]></description>
	<pubDate>Wed, 27 Jul 2022 12:31:39 +0000</pubDate>
	<author>amayra</author>
	<category>News</category>
	<votes>1</votes>
	<guid>http://affiliated-business.com/story.php?title=future-of-data-science-5</guid>
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