यथाप्राप्त आंकड़े - Revision history

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	यथाप्राप्त आंकड़े, जिसे प्रायः स्रोत या प्राथमिक आंकड़े के रूप में संदर्भित किया जाता है, वह आंकड़े हैं जिसे अभी तक संसाधित, कोडित, स्वरूपित या विश्लेषण नहीं किया गया है। जबकि यथाप्राप्त आंकड़े एक मूल्यवान संसाधन है, इसके साथ काम करना या समझना चुनौतीपूर्ण हो सकता है क्योंकि यह दृष्टिगत रूप से अव्यवस्थित है और इसमें सामंजस्य की कमी हो सकती है। संगठन ग्राहकों, बिक्री, विपणन अभियानों की सफलता और अन्य उपयोगी लक्ष्यों के बारे में अधिक जानने के लिए यथाप्राप्त आंकड़े को एकत्र और उपयोग कर सकते हैं, लेकिन पहले उन्हें आंकड़ों को ऐसे रूप में संरचना और व्यवस्थित करने की आवश्यकता है जो पढ़ने और कल्पना करने में सुविधा हो।		यथाप्राप्त आंकड़े, जिसे प्रायः स्रोत या [[प्राथमिक आंकड़े]] के रूप में संदर्भित किया जाता है, वह आंकड़े हैं जिसे अभी तक संसाधित, कोडित, स्वरूपित या विश्लेषण नहीं किया गया है। जबकि यथाप्राप्त आंकड़े एक मूल्यवान संसाधन है, इसके साथ काम करना या समझना चुनौतीपूर्ण हो सकता है क्योंकि यह दृष्टिगत रूप से अव्यवस्थित है और इसमें सामंजस्य की कमी हो सकती है। संगठन ग्राहकों, बिक्री, विपणन अभियानों की सफलता और अन्य उपयोगी लक्ष्यों के बारे में अधिक जानने के लिए यथाप्राप्त आंकड़े को एकत्र और उपयोग कर सकते हैं, लेकिन पहले उन्हें आंकड़ों को ऐसे रूप में संरचना और व्यवस्थित करने की आवश्यकता है जो पढ़ने और कल्पना करने में सुविधा हो।

	जो उद्यम विश्लेषण, निर्णय लेने या रिपोर्टिंग के लिए सक्रिय रूप से आंकड़ों का उपयोग करते हैं, उन्हें यह समझना चाहिए कि यथाप्राप्त आंकड़े कैसे काम करता है और यह एक बड़ी यथाप्राप्त युक्ति में कैसे सही स्थिति में रखता है।		जो उद्यम विश्लेषण, निर्णय लेने या रिपोर्टिंग के लिए सक्रिय रूप से आंकड़ों का उपयोग करते हैं, उन्हें यह समझना चाहिए कि यथाप्राप्त आंकड़े कैसे काम करता है और यह एक बड़ी यथाप्राप्त युक्ति में कैसे सही स्थिति में रखता है।
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	== यथाप्राप्त आंकड़ों का संग्रहण ==		== यथाप्राप्त आंकड़ों का संग्रहण ==
	यथाप्राप्त आंकड़े कैसे एकत्र किया जाता है यह इसकी गुणवत्ता और भविष्य की संभावनाओं में महत्वपूर्ण भूमिका निभाता है। सटीकता, विश्वसनीयता और वैधता का मतलब यथाप्राप्त आंकड़े के आँकड़ासंचय(डेटाबेस) के बीच अंतर हो सकता है जो जानकारी और अंतर्दृष्टि का खजाना है, और अप्राप्य आंकड़े जो बस जगह लेते हैं। निम्नलिखित चरण आंकड़ों के संग्रहण के लिए एक पथ तैयार करते हैं ताकि यह सुनिश्चित हो सके कि यह संगठन की आवश्यकताओं को पूरा करता है।		यथाप्राप्त [[आंकड़े]] कैसे एकत्र किया जाता है यह इसकी गुणवत्ता और भविष्य की संभावनाओं में महत्वपूर्ण भूमिका निभाता है। सटीकता, विश्वसनीयता और वैधता का मतलब यथाप्राप्त आंकड़े के आँकड़ासंचय(डेटाबेस) के बीच अंतर हो सकता है जो जानकारी और अंतर्दृष्टि का खजाना है, और अप्राप्य आंकड़े जो बस जगह लेते हैं। निम्नलिखित चरण आंकड़ों के संग्रहण के लिए एक पथ तैयार करते हैं ताकि यह सुनिश्चित हो सके कि यह संगठन की आवश्यकताओं को पूरा करता है।

	=== लक्ष्य परिभाषित करना ===		=== लक्ष्य परिभाषित करना ===

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	=== 4. आंकड़ों का निर्गम ===		=== 4. आंकड़ों का निर्गम ===
	एक बार जब यथाप्राप्त आंकड़े पूरी तरह से उपयोगी और व्यावहारिक ~~डेटा~~ में बदल जाते हैं, तो इसे मानव-अनुकूल रूप में अनुवादित किया जाता है - उदाहरण के लिए आरेख, ग्राफ़, टेबल, वेक्टर फ़ाइलें या सादा पाठ। यह चरण आंकड़े को प्रयोग करने योग्य और कार्रवाई योग्य बनाता है।		एक बार जब यथाप्राप्त आंकड़े पूरी तरह से उपयोगी और व्यावहारिक आंकड़ों में बदल जाते हैं, तो इसे मानव-अनुकूल रूप में अनुवादित किया जाता है - उदाहरण के लिए आरेख, ग्राफ़, टेबल, वेक्टर फ़ाइलें या सादा पाठ। यह चरण आंकड़े को प्रयोग करने योग्य और कार्रवाई योग्य बनाता है।

	=== 5. आंकड़ों का भंडारण ===		=== 5. आंकड़ों का भंडारण ===

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	{\| class="wikitable"		{\| class="wikitable"
	\|'''ग्राहक जानकारी'''		\|'''ग्राहक जानकारी'''
	\|~~Enables targeted results and demographic insights when combined with other data.~~		\|अन्य आंकड़ों के साथ संयुक्त करने पर लक्षित परिणाम और जनसांख्यिकीय अंतर्दृष्टि सक्षम होती है।
	\|-		\|-
	\|'''विक्रय अभिलेख'''		\|'''विक्रय अभिलेख'''
	\|~~Quantifiable data on number and frequency of goods and services sales. Identifies popular products and optimal sales times. Combined with customer info~~, ~~provides insights into customer demographics and preferences.~~		\|वस्तुओं और सेवाओं की बिक्री की संख्या और आवृत्ति पर मात्रात्मक आंकड़े। लोकप्रिय उत्पादों और अनुकूलतम बिक्री समय की पहचान करता है। ग्राहक जानकारी के साथ संयोजन में, ग्राहक जनसांख्यिकी और प्राथमिकताओं में अंतर्दृष्टि प्रदान की जाती है।
	\|-		\|-
	\|'''कर्मचारी कार्य निष्पादन'''		\|'''कर्मचारी कार्य निष्पादन'''
	\|~~Quantifiable data on working hours~~, ~~productivity~~, ~~quality, and compensation. Collected through surveys or internal monitoring software. Assists in calculating staff return on investment.~~		\|कार्य के घंटे, उत्पादकता, गुणवत्ता और पारिश्रमिक पर मात्रात्मक आंकड़े। सर्वेक्षण या आंतरिक निगरानी सॉफ़्टवेयर के माध्यम से एकत्र किया गया। कर्मचारियों के निवेश पर प्रतिफल की गणना करने में सहायता करता है।
	\|-		\|-
	\|'''राजस्व एवं व्यय'''		\|'''राजस्व एवं व्यय'''
	\|~~Strictly quantitative data tracking financial activity, including revenue and expenses. Used to calculate net revenue and analyze return on investment in different areas of the company.~~		\|राजस्व और व्यय सहित वित्तीय गतिविधि पर नज़र रखने वाला सख्त मात्रात्मक आंकड़े। कंपनी के विभिन्न क्षेत्रों में शुद्ध राजस्व की गणना और निवेश पर रिटर्न का विश्लेषण करने के लिए उपयोग किया जाता है।
	\|}		\|}

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	{\| class="wikitable"		{\| class="wikitable"
	\|'''सर्वेक्षण पर खुली प्रतिक्रिया'''		\|'''सर्वेक्षण पर खुली प्रतिक्रिया'''
	\|~~Open~~-~~ended survey questions with unstructured responses provide real insights into respondents’ ideas and opinions. It cannot be easily aggregated like quantitative data~~, ~~but it does give a true viewpoint.~~		\|असंरचित प्रतिक्रियाओं के साथ खुले-आम सर्वेक्षण प्रश्न उत्तरदाताओं के विचारों और राय के बारे में वास्तविक जानकारी प्रदान करते हैं। इसे मात्रात्मक आंकड़े की तरह आसानी से एकत्रित नहीं किया जा सकता है, लेकिन यह एक सच्चा दृष्टिकोण प्रदान करता है।
	\|-		\|-
	\|'''तस्वीरें और वीडियो'''		\|'''तस्वीरें और वीडियो'''
	\|~~Categorizing photos and videos is complex due to overlap~~, ~~but raw data is crucial for training machine learning models in computer vision~~, ~~especially for surveillance and visual scenario analysis.~~		\|ओवरलैप के कारण फ़ोटो और वीडियो को वर्गीकृत करना जटिल है, लेकिन कंप्यूटर विज़न में मशीन लर्निंग मॉडल को प्रशिक्षित करने के लिए यथाप्राप्त आंकड़े महत्वपूर्ण है, विशेष रूप से निगरानी और दृश्य परिदृश्य विश्लेषण के लिए।
	\|-		\|-
	\|'''ग्राहक समीक्षाएँ'''		\|'''ग्राहक समीक्षाएँ'''
	\|~~Star ratings are quantitative~~, ~~while reviews are qualitative~~, ~~assessing responses on a positive to negative scale~~, ~~and highlighting customer suggestions and pain points.~~		\|स्टार रेटिंग मात्रात्मक होती हैं, जबकि समीक्षाएं गुणात्मक होती हैं, जो सकारात्मक से नकारात्मक पैमाने पर प्रतिक्रियाओं का आकलन करती हैं, तथा ग्राहकों के सुझावों और समस्याओं पर प्रकाश डालती हैं।
	\|-		\|-
	\|'''समाचार रिपोर्ट और जनमत'''		\|'''समाचार रिपोर्ट और जनमत'''
	\|~~The data from news reports and articles about your company provides valuable insights into public opinion but requires processing to distinguish positive and negative coverage.~~		\|आपकी कंपनी के बारे में समाचार रिपोर्टों और लेखों से प्राप्त आंकड़े, जनमत के बारे में मूल्यवान जानकारी प्रदान करता है, लेकिन सकारात्मक और नकारात्मक व्याप्ति में अंतर करने के लिए प्रसंस्करण की आवश्यकता होती है।
	\|}		\|}

	[[Category:सांख्यिकी]][[Category:कक्षा-9]][[Category:गणित]]		[[Category:सांख्यिकी]][[Category:कक्षा-9]][[Category:गणित]]

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	मात्रात्मक आंकड़े, यथाप्राप्त आंकड़े होते हैं जिसमें गणनीय आंकड़े होते हैं, जहाँ प्रत्येक आंकड़े बिंदु का एक अद्वितीय संख्यात्मक मान होता है। इस प्रकार के आंकड़ों का उपयोग गणितीय गणनाओं और तकनीकी सांख्यिकीय विश्लेषण के लिए सबसे अच्छा किया जाता है। यहाँ कुछ सामान्य उदाहरण दिए गए हैं:		मात्रात्मक आंकड़े, यथाप्राप्त आंकड़े होते हैं जिसमें गणनीय आंकड़े होते हैं, जहाँ प्रत्येक आंकड़े बिंदु का एक अद्वितीय संख्यात्मक मान होता है। इस प्रकार के आंकड़ों का उपयोग गणितीय गणनाओं और तकनीकी सांख्यिकीय विश्लेषण के लिए सबसे अच्छा किया जाता है। यहाँ कुछ सामान्य उदाहरण दिए गए हैं:
	{\| class="wikitable"		{\| class="wikitable"
	\|'''~~Customer information~~'''		\|'''ग्राहक जानकारी'''
	\|Enables targeted results and demographic insights when combined with other data.		\|Enables targeted results and demographic insights when combined with other data.
	\|-		\|-
	\|'''~~Sales records~~'''		\|'''विक्रय अभिलेख'''
	\|Quantifiable data on number and frequency of goods and services sales. Identifies popular products and optimal sales times. Combined with customer info, provides insights into customer demographics and preferences.		\|Quantifiable data on number and frequency of goods and services sales. Identifies popular products and optimal sales times. Combined with customer info, provides insights into customer demographics and preferences.
	\|-		\|-
	\|'''~~Employee performance~~'''		\|'''कर्मचारी कार्य निष्पादन'''
	\|Quantifiable data on working hours, productivity, quality, and compensation. Collected through surveys or internal monitoring software. Assists in calculating staff return on investment.		\|Quantifiable data on working hours, productivity, quality, and compensation. Collected through surveys or internal monitoring software. Assists in calculating staff return on investment.
	\|-		\|-
	\|'''~~Revenue and expenses~~'''		\|'''राजस्व एवं व्यय'''
	\|Strictly quantitative data tracking financial activity, including revenue and expenses. Used to calculate net revenue and analyze return on investment in different areas of the company.		\|Strictly quantitative data tracking financial activity, including revenue and expenses. Used to calculate net revenue and analyze return on investment in different areas of the company.
	\|}		\|}
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	गुणात्मक आंकड़ों को गैर-मात्रात्मक और गैर-संख्यात्मक प्रकृति में रिकॉर्ड और देखा जा सकता है। इसमें कदाचित् ही कभी संख्याएँ उपस्थित होती हैं और प्रायः ऑडियो और वीडियो रिकॉर्डिंग या आमने-सामने साक्षात्कार के माध्यम से प्रत्येक प्रतिभागी के लिए अलग-अलग उत्तरों से निकाला जाता है। यहाँ कुछ सामान्य उदाहरण दिए गए हैं:		गुणात्मक आंकड़ों को गैर-मात्रात्मक और गैर-संख्यात्मक प्रकृति में रिकॉर्ड और देखा जा सकता है। इसमें कदाचित् ही कभी संख्याएँ उपस्थित होती हैं और प्रायः ऑडियो और वीडियो रिकॉर्डिंग या आमने-सामने साक्षात्कार के माध्यम से प्रत्येक प्रतिभागी के लिए अलग-अलग उत्तरों से निकाला जाता है। यहाँ कुछ सामान्य उदाहरण दिए गए हैं:
	{\| class="wikitable"		{\| class="wikitable"
	\|'''~~Open-ended response on a survey~~'''		\|'''सर्वेक्षण पर खुली प्रतिक्रिया'''
	\|Open-ended survey questions with unstructured responses provide real insights into respondents’ ideas and opinions. It cannot be easily aggregated like quantitative data, but it does give a true viewpoint.		\|Open-ended survey questions with unstructured responses provide real insights into respondents’ ideas and opinions. It cannot be easily aggregated like quantitative data, but it does give a true viewpoint.
	\|-		\|-
	\|'''~~Photographs and videos~~'''		\|'''तस्वीरें और वीडियो'''
	\|Categorizing photos and videos is complex due to overlap, but raw data is crucial for training machine learning models in computer vision, especially for surveillance and visual scenario analysis.		\|Categorizing photos and videos is complex due to overlap, but raw data is crucial for training machine learning models in computer vision, especially for surveillance and visual scenario analysis.
	\|-		\|-
	\|'''~~Customer Reviews~~'''		\|'''ग्राहक समीक्षाएँ'''
	\|Star ratings are quantitative, while reviews are qualitative, assessing responses on a positive to negative scale, and highlighting customer suggestions and pain points.		\|Star ratings are quantitative, while reviews are qualitative, assessing responses on a positive to negative scale, and highlighting customer suggestions and pain points.
	\|-		\|-
	\|'''~~News reports and public opinion~~'''		\|'''समाचार रिपोर्ट और जनमत'''
	\|The data from news reports and articles about your company provides valuable insights into public opinion but requires processing to distinguish positive and negative coverage.		\|The data from news reports and articles about your company provides valuable insights into public opinion but requires processing to distinguish positive and negative coverage.
	\|}		\|}

	[[Category:सांख्यिकी]][[Category:कक्षा-9]][[Category:गणित]]		[[Category:सांख्यिकी]][[Category:कक्षा-9]][[Category:गणित]]

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	=== 5. आंकड़ों का भंडारण ===		=== 5. आंकड़ों का भंडारण ===
	~~Data storage is essential~~, as the processed data may be subjected to future analysis for additional insights—but it becomes even more critical when dealing with sensitive corporate information or user data. Storage quality should be consistent with the overall standards of the company’s data and information, ~~while also complying with any local data privacy and security legislation~~, ~~such as the European Union’s General Data Protection Regulation~~ (GDPR) ~~and the California Consumer Protection Act~~ (CCPA).		आंकड़ों का भंडारण आवश्यक है, क्योंकि संसाधित आंकड़े को अतिरिक्त अंतर्दृष्टि के लिए भविष्य में विश्लेषण के अधीन किया जा सकता है - लेकिन संवेदनशील कॉर्पोरेट जानकारी या उपयोगकर्ता आंकड़े के साथ काम करते समय यह और भी महत्वपूर्ण हो जाता है। संग्रहण गुणवत्ता कंपनी के आंकड़े और जानकारी के यथाप्राप्त मानकों के अनुरूप होनी चाहिए, साथ ही किसी भी स्थानीय आंकड़े गोपनीयता और सुरक्षा कानून, जैसे कि यूरोपीय संघ के सामान्य आंकड़े सुरक्षा विनियमन (GDPR) और कैलिफ़ोर्निया उपभोक्ता संरक्षण अधिनियम (CCPA) का अनुपालन भी करना चाहिए।

	== दो प्रकार के यथाप्राप्त आंकड़े और उनके उदाहरण ==		== दो प्रकार के यथाप्राप्त आंकड़े और उनके उदाहरण ==
	~~Raw data includes a wide range of data types~~, which are typically classified as either qualitative or quantitative. The most important condition for this categorization is that the data is not cleaned or processed in any way. Raw data allows unrivaled flexibility and control over the information collected from the database.		यथाप्राप्त आंकड़ों में आंकड़े के कई प्रकार उपस्थित होते हैं, जिन्हें प्रायः गुणात्मक या मात्रात्मक के रूप में वर्गीकृत किया जाता है। इस वर्गीकरण के लिए सबसे महत्वपूर्ण शर्त यह है कि आंकड़े को किसी भी तरह से साफ या संसाधित नहीं किया जाता है। यथाप्राप्त आंकड़े डेटाबेस से एकत्रित जानकारी पर बेजोड़ लचीलापन और नियंत्रण की अनुमति देता है।

	=== मात्रात्मक यथाप्राप्त आंकड़े ===		=== मात्रात्मक यथाप्राप्त आंकड़े ===
	~~Quantitative data is raw data that consists of countable data~~, ~~where each data point has a unique numerical value. This type of data is best used for mathematical calculations and technical statistical analysis. Here are some common examples~~:		मात्रात्मक आंकड़े, यथाप्राप्त आंकड़े होते हैं जिसमें गणनीय आंकड़े होते हैं, जहाँ प्रत्येक आंकड़े बिंदु का एक अद्वितीय संख्यात्मक मान होता है। इस प्रकार के आंकड़ों का उपयोग गणितीय गणनाओं और तकनीकी सांख्यिकीय विश्लेषण के लिए सबसे अच्छा किया जाता है। यहाँ कुछ सामान्य उदाहरण दिए गए हैं:
	{\| class="wikitable"		{\| class="wikitable"
	\|'''Customer information'''		\|'''Customer information'''
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	=== गुणात्मक यथाप्राप्त आंकड़े ===		=== गुणात्मक यथाप्राप्त आंकड़े ===
	~~Qualitative data can be recorded and observed in a non~~-~~quantifiable and non~~-~~numerical nature. It rarely includes numbers and is usually extracted from answers that vary for each participant through audio and video recordings or one~~-on-~~one interviews. Here are some common examples~~:		गुणात्मक आंकड़ों को गैर-मात्रात्मक और गैर-संख्यात्मक प्रकृति में रिकॉर्ड और देखा जा सकता है। इसमें कदाचित् ही कभी संख्याएँ उपस्थित होती हैं और प्रायः ऑडियो और वीडियो रिकॉर्डिंग या आमने-सामने साक्षात्कार के माध्यम से प्रत्येक प्रतिभागी के लिए अलग-अलग उत्तरों से निकाला जाता है। यहाँ कुछ सामान्य उदाहरण दिए गए हैं:
	{\| class="wikitable"		{\| class="wikitable"
	\|'''Open-ended response on a survey'''		\|'''Open-ended response on a survey'''

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	~~Raw data~~, ~~often referred to as source or [[Primary Data\|primary data]]~~, ~~is data that has not yet been processed~~, ~~coded~~, ~~formatted~~, ~~or analyzed. While raw data is a valuable resource~~, ~~it can be challenging to work with or understand as it’s visually cluttered and can lack cohesion. Organizations can collect and use raw data to learn more about customers~~, ~~sales~~, ~~the success of marketing campaigns, and other useful targets, but first they need to structure and organize the data into a form that’s easier to read and visualize.~~		यथाप्राप्त आंकड़े, जिसे प्रायः स्रोत या प्राथमिक आंकड़े के रूप में संदर्भित किया जाता है, वह आंकड़े हैं जिसे अभी तक संसाधित, कोडित, स्वरूपित या विश्लेषण नहीं किया गया है। जबकि यथाप्राप्त आंकड़े एक मूल्यवान संसाधन है, इसके साथ काम करना या समझना चुनौतीपूर्ण हो सकता है क्योंकि यह दृष्टिगत रूप से अव्यवस्थित है और इसमें सामंजस्य की कमी हो सकती है। संगठन ग्राहकों, बिक्री, विपणन अभियानों की सफलता और अन्य उपयोगी लक्ष्यों के बारे में अधिक जानने के लिए यथाप्राप्त आंकड़े को एकत्र और उपयोग कर सकते हैं, लेकिन पहले उन्हें आंकड़ों को ऐसे रूप में संरचना और व्यवस्थित करने की आवश्यकता है जो पढ़ने और कल्पना करने में सुविधा हो।

	~~Enterprises that actively employ data for analysis~~, ~~decision-making~~, ~~or reporting must understand how raw data works and how it fits into a larger overall data strategy.~~		जो उद्यम विश्लेषण, निर्णय लेने या रिपोर्टिंग के लिए सक्रिय रूप से आंकड़ों का उपयोग करते हैं, उन्हें यह समझना चाहिए कि यथाप्राप्त आंकड़े कैसे काम करता है और यह एक बड़ी यथाप्राप्त युक्ति में कैसे सही स्थिति में रखता है।

	== यथाप्राप्त आंकड़े का उपयोग कैसे किया जाता है? ==		== यथाप्राप्त आंकड़े का उपयोग कैसे किया जाता है? ==

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	Enterprises that actively employ data for analysis, decision-making, or reporting must understand how raw data works and how it fits into a larger overall data strategy.		Enterprises that actively employ data for analysis, decision-making, or reporting must understand how raw data works and how it fits into a larger overall data strategy.

	== ~~'''How is Raw Data Used~~?~~'''~~ ==		== यथाप्राप्त आंकड़े का उपयोग कैसे किया जाता है? ==
	Raw data is [[data]] collected from one or multiple sources that remains in its unaltered initial state. At this point, the data might contain human, machine, or instrumental errors—depending on the collection method—or it could lack validation. Once the data is changed in any way to improve its quality, the data has been processed and is no longer considered raw.		Raw data is [[data]] collected from one or multiple sources that remains in its unaltered initial state. At this point, the data might contain human, machine, or instrumental errors—depending on the collection method—or it could lack validation. Once the data is changed in any way to improve its quality, the data has been processed and is no longer considered raw.

	Raw data has infinite potential as a resource, as it comes in a variety of forms from a wide range of sources. Collecting raw data is the first step toward gaining a more thorough understanding of a demographic, system, concept, or environment. Business intelligence analysts can extract useful and accurate information about the condition of their business from raw data—for example, audience interest, sales figures, marketing campaign performance, and overall productivity.		Raw data has infinite potential as a resource, as it comes in a variety of forms from a wide range of sources. Collecting raw data is the first step toward gaining a more thorough understanding of a demographic, system, concept, or environment. Business intelligence analysts can extract useful and accurate information about the condition of their business from raw data—for example, audience interest, sales figures, marketing campaign performance, and overall productivity.

	== ~~'''Raw Data Collection Steps'''~~ ==		== यथाप्राप्त आंकड़ों का संग्रहण ==
	How raw data is collected plays a key role in its quality and future potential. Accuracy, credibility, and validity can mean the difference between a database of raw data that’s a wealth of information and insights, and unactionable data that simply takes up space. The following steps lay out a path for data collection to help ensure it meets the organization’s needs.		How raw data is collected plays a key role in its quality and future potential. Accuracy, credibility, and validity can mean the difference between a database of raw data that’s a wealth of information and insights, and unactionable data that simply takes up space. The following steps lay out a path for data collection to help ensure it meets the organization’s needs.

	=== ~~'''~~Defining Goals~~'''~~ ===		=== Defining Goals ===
	Define the information you want to extract to lay the groundwork for your raw data-gathering goals. For example, if the desired data is user-base and customer information, online and in-person surveys focused on a specific age and geographical demographic can be used to gather it.		Define the information you want to extract to lay the groundwork for your raw data-gathering goals. For example, if the desired data is user-base and customer information, online and in-person surveys focused on a specific age and geographical demographic can be used to gather it.

	Other types of raw data may require advance planning. For instance, collecting data from log records would require having a monitoring system in place for anywhere from a few weeks to a year to collect data before being able to pull it.		Other types of raw data may require advance planning. For instance, collecting data from log records would require having a monitoring system in place for anywhere from a few weeks to a year to collect data before being able to pull it.

	=== ~~'''~~Choosing a Collection Method~~'''~~ ===		=== Choosing a Collection Method ===
	Choosing the appropriate raw data collection method can reduce the percentage of human or machine errors you’d have to scrub out when cleaning a raw database. Generally, electronic collecting methods tend to result in lower error rates—manual collection can introduce variables that leave room for interpretation, such as illegible handwriting or hard-to-understand accents in audio or video recordings.		Choosing the appropriate raw data collection method can reduce the percentage of human or machine errors you’d have to scrub out when cleaning a raw database. Generally, electronic collecting methods tend to result in lower error rates—manual collection can introduce variables that leave room for interpretation, such as illegible handwriting or hard-to-understand accents in audio or video recordings.

	=== ~~'''~~Collecting Data~~'''~~ ===		=== Collecting Data ===
	Raw data tends to be large in volume and highly complex. During the collection process, the overall volume of data is only an estimate—once you process the data by cleaning it of errors and invalid data points, you’ll have a more accurate sense of scope.		Raw data tends to be large in volume and highly complex. During the collection process, the overall volume of data is only an estimate—once you process the data by cleaning it of errors and invalid data points, you’ll have a more accurate sense of scope.

	== ~~'''~~How Raw Data is Processed in 5 Steps~~'''~~ ==		== How Raw Data is Processed in 5 Steps ==
	Analysts well-versed in data trends and patterns work with modern business intelligence tools and, on occasion, incorporate artificial intelligence (AI) applications to transform raw data into insightful knowledge using the following steps:		Analysts well-versed in data trends and patterns work with modern business intelligence tools and, on occasion, incorporate artificial intelligence (AI) applications to transform raw data into insightful knowledge using the following steps:

	=== ~~'''~~1. Data Preparation~~'''~~ ===		=== 1. Data Preparation ===
	Raw data, by definition, is faulty and may contain errors—it might also lack consistency in structure and format, especially when it’s been obtained from different sources. During data preparation, data is thoroughly cleaned, sorted, and filtered following predefined standards to ensure high-quality, consistent outcomes.		Raw data, by definition, is faulty and may contain errors—it might also lack consistency in structure and format, especially when it’s been obtained from different sources. During data preparation, data is thoroughly cleaned, sorted, and filtered following predefined standards to ensure high-quality, consistent outcomes.

	=== ~~'''~~2. Data Input~~'''~~ ===		=== 2. Data Input ===
	Inputting data—sometimes referred to as data translation—involves converting raw data into a machine-readable form. The specific process will vary depending upon the tools and software that will be used for analysis.		Inputting data—sometimes referred to as data translation—involves converting raw data into a machine-readable form. The specific process will vary depending upon the tools and software that will be used for analysis.

	In the case of digitally collected data, this step is minimal—some structuring and changing of file format might be needed. But for handwritten surveys, audio recordings, and video clips, data will need to be manually or digitally extracted into a form the processing software is capable of understanding.		In the case of digitally collected data, this step is minimal—some structuring and changing of file format might be needed. But for handwritten surveys, audio recordings, and video clips, data will need to be manually or digitally extracted into a form the processing software is capable of understanding.

	=== ~~'''~~3. Data Processing and Analysis~~'''~~ ===		=== 3. Data Processing and Analysis ===
	Raw data is processed and analyzed for insights by searching for trends, patterns, anomalies, and relationships between the various elements. This process varies depending on the source, and can be done manually or using artificial intelligence and machine learning.		Raw data is processed and analyzed for insights by searching for trends, patterns, anomalies, and relationships between the various elements. This process varies depending on the source, and can be done manually or using artificial intelligence and machine learning.

	=== ~~'''~~4. Data Output~~'''~~ ===		=== 4. Data Output ===
	Once the raw data has been fully transformed into usable and insightful data, it’s translated into human-friendly form—diagrams, graphs, tables, vector files, or plain text, for example. This step makes the data usable and actionable.		Once the raw data has been fully transformed into usable and insightful data, it’s translated into human-friendly form—diagrams, graphs, tables, vector files, or plain text, for example. This step makes the data usable and actionable.

	=== ~~'''~~5. Data Storage~~'''~~ ===		=== 5. Data Storage ===
	Data storage is essential, as the processed data may be subjected to future analysis for additional insights—but it becomes even more critical when dealing with sensitive corporate information or user data. Storage quality should be consistent with the overall standards of the company’s data and information, while also complying with any local data privacy and security legislation, such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA).		Data storage is essential, as the processed data may be subjected to future analysis for additional insights—but it becomes even more critical when dealing with sensitive corporate information or user data. Storage quality should be consistent with the overall standards of the company’s data and information, while also complying with any local data privacy and security legislation, such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA).

	== ~~'''~~Two Types Of Raw Data and Their Examples~~'''~~ ==		== Two Types Of Raw Data and Their Examples ==
	Raw data includes a wide range of data types, which are typically classified as either qualitative or quantitative. The most important condition for this categorization is that the data is not cleaned or processed in any way. Raw data allows unrivaled flexibility and control over the information collected from the database.		Raw data includes a wide range of data types, which are typically classified as either qualitative or quantitative. The most important condition for this categorization is that the data is not cleaned or processed in any way. Raw data allows unrivaled flexibility and control over the information collected from the database.

	=== ~~'''~~Quantitative Raw Data~~'''~~ ===		=== Quantitative Raw Data ===
	Quantitative data is raw data that consists of countable data, where each data point has a unique numerical value. This type of data is best used for mathematical calculations and technical statistical analysis. Here are some common examples:		Quantitative data is raw data that consists of countable data, where each data point has a unique numerical value. This type of data is best used for mathematical calculations and technical statistical analysis. Here are some common examples:
	{\| class="wikitable"		{\| class="wikitable"
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	\|}		\|}

	=== ~~'''~~Qualitative Raw Data~~'''~~ ===		=== Qualitative Raw Data ===
	Qualitative data can be recorded and observed in a non-quantifiable and non-numerical nature. It rarely includes numbers and is usually extracted from answers that vary for each participant through audio and video recordings or one-on-one interviews. Here are some common examples:		Qualitative data can be recorded and observed in a non-quantifiable and non-numerical nature. It rarely includes numbers and is usually extracted from answers that vary for each participant through audio and video recordings or one-on-one interviews. Here are some common examples:
	{\| class="wikitable"		{\| class="wikitable"

Mani at 05:19, 15 October 2024

2024-10-15T05:19:03Z

← Older revision		Revision as of 10:49, 15 October 2024
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			Raw data, often referred to as source or [[Primary Data\|primary data]], is data that has not yet been processed, coded, formatted, or analyzed. While raw data is a valuable resource, it can be challenging to work with or understand as it’s visually cluttered and can lack cohesion. Organizations can collect and use raw data to learn more about customers, sales, the success of marketing campaigns, and other useful targets, but first they need to structure and organize the data into a form that’s easier to read and visualize.

			Enterprises that actively employ data for analysis, decision-making, or reporting must understand how raw data works and how it fits into a larger overall data strategy.

			== '''How is Raw Data Used?''' ==
			Raw data is [[data]] collected from one or multiple sources that remains in its unaltered initial state. At this point, the data might contain human, machine, or instrumental errors—depending on the collection method—or it could lack validation. Once the data is changed in any way to improve its quality, the data has been processed and is no longer considered raw.

			Raw data has infinite potential as a resource, as it comes in a variety of forms from a wide range of sources. Collecting raw data is the first step toward gaining a more thorough understanding of a demographic, system, concept, or environment. Business intelligence analysts can extract useful and accurate information about the condition of their business from raw data—for example, audience interest, sales figures, marketing campaign performance, and overall productivity.

			== '''Raw Data Collection Steps''' ==
			How raw data is collected plays a key role in its quality and future potential. Accuracy, credibility, and validity can mean the difference between a database of raw data that’s a wealth of information and insights, and unactionable data that simply takes up space. The following steps lay out a path for data collection to help ensure it meets the organization’s needs.

			=== '''Defining Goals''' ===
			Define the information you want to extract to lay the groundwork for your raw data-gathering goals. For example, if the desired data is user-base and customer information, online and in-person surveys focused on a specific age and geographical demographic can be used to gather it.

			Other types of raw data may require advance planning. For instance, collecting data from log records would require having a monitoring system in place for anywhere from a few weeks to a year to collect data before being able to pull it.

			=== '''Choosing a Collection Method''' ===
			Choosing the appropriate raw data collection method can reduce the percentage of human or machine errors you’d have to scrub out when cleaning a raw database. Generally, electronic collecting methods tend to result in lower error rates—manual collection can introduce variables that leave room for interpretation, such as illegible handwriting or hard-to-understand accents in audio or video recordings.

			=== '''Collecting Data''' ===
			Raw data tends to be large in volume and highly complex. During the collection process, the overall volume of data is only an estimate—once you process the data by cleaning it of errors and invalid data points, you’ll have a more accurate sense of scope.

			== '''How Raw Data is Processed in 5 Steps''' ==
			Analysts well-versed in data trends and patterns work with modern business intelligence tools and, on occasion, incorporate artificial intelligence (AI) applications to transform raw data into insightful knowledge using the following steps:

			=== '''1. Data Preparation''' ===
			Raw data, by definition, is faulty and may contain errors—it might also lack consistency in structure and format, especially when it’s been obtained from different sources. During data preparation, data is thoroughly cleaned, sorted, and filtered following predefined standards to ensure high-quality, consistent outcomes.

			=== '''2. Data Input''' ===
			Inputting data—sometimes referred to as data translation—involves converting raw data into a machine-readable form. The specific process will vary depending upon the tools and software that will be used for analysis.

			In the case of digitally collected data, this step is minimal—some structuring and changing of file format might be needed. But for handwritten surveys, audio recordings, and video clips, data will need to be manually or digitally extracted into a form the processing software is capable of understanding.

			=== '''3. Data Processing and Analysis''' ===
			Raw data is processed and analyzed for insights by searching for trends, patterns, anomalies, and relationships between the various elements. This process varies depending on the source, and can be done manually or using artificial intelligence and machine learning.

			=== '''4. Data Output''' ===
			Once the raw data has been fully transformed into usable and insightful data, it’s translated into human-friendly form—diagrams, graphs, tables, vector files, or plain text, for example. This step makes the data usable and actionable.

			=== '''5. Data Storage''' ===
			Data storage is essential, as the processed data may be subjected to future analysis for additional insights—but it becomes even more critical when dealing with sensitive corporate information or user data. Storage quality should be consistent with the overall standards of the company’s data and information, while also complying with any local data privacy and security legislation, such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA).

			== '''Two Types Of Raw Data and Their Examples''' ==
			Raw data includes a wide range of data types, which are typically classified as either qualitative or quantitative. The most important condition for this categorization is that the data is not cleaned or processed in any way. Raw data allows unrivaled flexibility and control over the information collected from the database.

			=== '''Quantitative Raw Data''' ===
			Quantitative data is raw data that consists of countable data, where each data point has a unique numerical value. This type of data is best used for mathematical calculations and technical statistical analysis. Here are some common examples:
			{\| class="wikitable"
			\|'''Customer information'''
			\|Enables targeted results and demographic insights when combined with other data.
			\|-
			\|'''Sales records'''
			\|Quantifiable data on number and frequency of goods and services sales. Identifies popular products and optimal sales times. Combined with customer info, provides insights into customer demographics and preferences.
			\|-
			\|'''Employee performance'''
			\|Quantifiable data on working hours, productivity, quality, and compensation. Collected through surveys or internal monitoring software. Assists in calculating staff return on investment.
			\|-
			\|'''Revenue and expenses'''
			\|Strictly quantitative data tracking financial activity, including revenue and expenses. Used to calculate net revenue and analyze return on investment in different areas of the company.
			\|}

			=== '''Qualitative Raw Data''' ===
			Qualitative data can be recorded and observed in a non-quantifiable and non-numerical nature. It rarely includes numbers and is usually extracted from answers that vary for each participant through audio and video recordings or one-on-one interviews. Here are some common examples:
			{\| class="wikitable"
			\|'''Open-ended response on a survey'''
			\|Open-ended survey questions with unstructured responses provide real insights into respondents’ ideas and opinions. It cannot be easily aggregated like quantitative data, but it does give a true viewpoint.
			\|-
			\|'''Photographs and videos'''
			\|Categorizing photos and videos is complex due to overlap, but raw data is crucial for training machine learning models in computer vision, especially for surveillance and visual scenario analysis.
			\|-
			\|'''Customer Reviews'''
			\|Star ratings are quantitative, while reviews are qualitative, assessing responses on a positive to negative scale, and highlighting customer suggestions and pain points.
			\|-
			\|'''News reports and public opinion'''
			\|The data from news reports and articles about your company provides valuable insights into public opinion but requires processing to distinguish positive and negative coverage.
			\|}

	[[Category:सांख्यिकी]][[Category:कक्षा-9]][[Category:गणित]]		[[Category:सांख्यिकी]][[Category:कक्षा-9]][[Category:गणित]]

Mani: content added

2024-05-21T08:36:53Z

content added

← Older revision		Revision as of 14:06, 21 May 2024
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	[[Category:सांख्यिकी]][[Category:कक्षा-9]][[Category:गणित]]		[[Category:सांख्यिकी]][[Category:कक्षा-9]][[Category:गणित]]
	~~Raw data~~		सांख्यिकी में, यथाप्राप्त आंकड़े को उस आंकड़े के रूप में परिभाषित किया जा सकता है जो सीधे आंकड़े के प्राथमिक स्रोत से एकत्र किया गया है जिसे किसी के द्वारा संसाधित या साफ नहीं किया गया है। पहली प्रक्रिया आंकड़े के प्राथमिक स्रोत से आंकड़ा एकत्र करना है और फिर जब आंकड़े एकत्र हो जाते हैं, तो इसे अभिक्रियित(साफ़) किया जा सकता है और विभिन्न रूपों में संक्षेपित किया जा सकता है।

			सांख्यिकी में यथाप्राप्त आंकड़े के उदाहरण

			* एक कक्षा के सभी विद्यार्थियों की श्रेणीयाँ(ग्रेड).
			* रेडियो पर प्रसारित होने वाले संगीत की सूची।
			* शिक्षक दिवस समारोह के लिए विद्यार्थियों द्वारा एकत्रित की गई राशि।

			आंकड़ों में यथाप्राप्त आंकडों के ये सामान्य उदाहरण हैं। सामान्यतः सभी आंकडों का उपयोग बेहतर समझ बनाने और उस पर निर्णय लेने के लिए किया जाता है।