/home/leansigm/public_html/components/com_easyblog/services<title>Principle Experiences of Data Science</title>
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	<h1>Principle Experiences of Data Science</h1>
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			<span class="post-date">Jul 05</span>

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				Posted by <a href="/forum/user/2254-upasana-mukherjee">UPASANA MUKHERJEE</a>				in <a href="/resources/sigmaway-blog/analytics">Analytics</a>			</span>
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	<p>Three principles of data science are: (i) the system built should perform well on future data sets and not just the current data set. Conclusions made on the basis of the current scenario are not always true for future cases, (ii) feature extraction is important, i.e. specifically finding the information that is required, by finding the correct elements, (iii) understanding and developing the correct model is the most important task. These are a few principle experiences which are not stated anywhere. Read more at: <a href="http://www.datasciencecentral.com/profiles/blogs/three-things-about-data-science-you-won-t-find-in-the-books" target="_blank" rel="noopener noreferrer">http://www.datasciencecentral.com/profiles/blogs/three-things-about-data-science-you-won-t-find-in-the-books</a></p>
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		<a href="/blog/tags/data-science-1-2">
		<span itemprop="keywords">Data Science</span>
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		<a href="/blog/tags/feature-extraction">
		<span itemprop="keywords">Feature Extraction</span>
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		<a href="/blog/tags/model-development">
		<span itemprop="keywords">Model Development</span>
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