Computer Basics In Hindi, कंप्यूटर बेसिक्स, कंप्यूटर बेसिक्स हिंदी में, Computer Basics For Beginners, कंप्यूटर की बेसिक जानकारी

Computer Basics In Hindi | कंप्यूटर बेसिक्स

Computer Basics In Hindi, कंप्यूटर बेसिक्स, कंप्यूटर बेसिक्स हिंदी में, Computer Basics For Beginners, कंप्यूटर की बेसिक जानकारी ……..


1.1 WHAT IS COMPUTER?

अक्सर लोग सोचते हैं कि कम्प्यूटर एक सर्वशक्तिमान सुपरमैन की तरह है, परन्तु ऐसा है नहीं। यह केवल एक स्वचालित इलेक्ट्रॉनिक मशीन है जो तीव्र गति से कार्य करता है और कोई गलती नहीं करता है। इसकी क्षमता सीमित है। यह अंग्रेजी शब्द कम्प्यूट (Compute) से बना है जिसका अर्थ गणना करना है। हिन्दी में इसे संगणक कहते हैं। इसका उपयोग बहुत सारे सूचनाओं को प्रोरोस (Process) करने तथा इकट्ठा करने के लिए होता है।

कम्प्यूटर को कृत्रिम बुद्धि की संज्ञा दी गई है। इसकी स्मरण शक्ति मनुष्य की तुलना में उच्च होती है। कम्प्यूटर एक यंत्र है जो डेटा ग्रहण करता है व इसे सॉफ्टवेयर या प्रोग्राम के अनुसार किसी परिणाम के लिए प्रोसेस (Process) करता है।

1.2 कम्प्यूटर संबंधी प्रारंभिक शब्द

1.2.1 Data
यह अव्यवस्थित आँकडा या तथ्य है। यह प्रोसेस के पहले की अवस्था है। साधारणतः डेटा को दो भागों में विभाजित करते है।

(i) संख्यात्मक डेटा
इस तरह के डेटा में 0 से 9 तक के अंको का प्रयोग होता है।
उदाहरण : कर्मचारियों का वेतन, परीक्षा में प्राप्त अंक, जनगणना, रौल नं. अंकगणितीय संख्याएँ आदि।

(ii) अल्फान्यूमेरिक डेटा
इस तरह के डेटा में अंकों, अक्षरों तथा चिह्नों का प्रयोग किया जाता है।

उदाहरण : पता (address) आदि।


1.2.2 Information

यह अव्यवस्थित डेटा का प्रोसेस करने के बाद प्राप्त परिणाम है जो व्यवस्थित होता है।

1.3 कम्प्यूटर की विशेषताएँ
• यह तीव्र गति से कार्य करता है अर्थात् समय की बचत होती है। 
• यह त्रुटिरहित कार्य करता है।
• यह स्थायी तथा विशाल भंडारण क्षमता की सुविधा देता है। 
• यह पूर्व निर्धारित निर्देशों (Pre defined instructions) के अनुसार तीव्र निर्णय लेने में सक्षम है।

1.4 कम्प्यूटर के उपयोग
• Bank (बैंक में)
• Medical Science (चिकित्सा विज्ञान में)
• Defence (रक्षा के क्षेत्र में)
• Publication (प्रकाशन में)
• Business (व्यापार में)
• Communication (संचार में)
• Administration (प्रशासन में)
• Recreation (मनोरंजन में)
•Education (शिक्षा के क्षेत्र में)
• Scientific Research (वैज्ञानिक अनुसंधान में)
• Railway and Airlines Reservation (रेलवे तथा वायुयान आरक्षण में)

1.5 कम्प्यूटर के कार्य
• Data Collection (डेटा संकलन) 
• Data Output (डेटा निर्गमन)
• Data Processing (डेटा प्रसंस्करण) 
• Data Storage (डेटा संचयन)

1.6 DATA PROCESSING AND ELECTRONIC DATA PROCESSING

कम्प्यूटर के निर्माण से पहले निश्चित लक्ष्य को प्राप्त करने के लिए डेटा का संकलन, संचयन, प्रसंस्करण और निर्गमन हस्तचालित विधि (manual method) से होता था, जिसे डेटा प्रोसेसिंग कहते थे। जैसे-जैसे टेक्नॉलॉजी का विकास हुआ इन सभी कार्यों के लिए कम्प्यूटर का उपयोग होने लगा। इसे इलेक्ट्रॉनिक डेटा प्रोसेसिंग (EDP) कहते हैं।

डेटा प्रोसेसिंग का मुख्य लक्ष्य अव्यवस्थित डेटा (Raw Data) से व्यवस्थित डेटा (Information) प्राप्त करना है जिसका उपयोग निर्णय लेने के लिए होता है।

1.7 COMPUTER SYSTEM

यह उपकरणों का एक समूह है जो एक साथ मिलकर डेटा प्रोसेस करते हैं। कम्प्यूटर सिस्टम में अनेक इकाइयाँ होती हैं जिनका उपयोग इलेक्ट्रॉनिक डेटा प्रोसेसिंग में होता है। बुनियादी कम्प्यूटर प्रोसेसिंग चक्र में इनपुट, प्रोसेसिंग और आउटपुट शामिल होते हैं।
1.7.1 CPU
इसे प्रोसेसर भी कहते हैं। यह एक इलेक्ट्रॉनिक माइक्रोचिप है जो डेटा को इनफॉर्मेशन में बदलते हुए प्रोसेस करता है। इसे कम्प्यूटर का ब्रेन कहा जाता है। यह कम्प्यूटर सिस्टम के सारे कार्यों को नियंत्रित करता है तथा यह इनपुट को आउटपुट में रूपान्तरित करता है। यह इनपुट यूनिट तथा आउटपुट यूनिट से मिलकर पूरा कम्प्यूटर सिस्टम बनाता है। इसके अग्रलिखित भाग होते हैं।
Arithmetic Logic Unit (ALU)
इसका उपयोग अंकगणितीय तथा तार्किक गणना में होता है। अंकगणितीय गणना के अन्तर्गत जोड़, घटाव, गुणा और भाग इत्यादि तथा तार्किक गणना के अन्तर्गत तुलनात्मक गणना जैसे (, या =), हाँ या ना (Yes या No) इत्यादि आते हैं।

Control Unit (CU)
यह कम्प्यूटर के सारे कार्यों को नियंत्रित करता हैं, तथा कम्प्यूटर के सारे भागों जैसे इनपुट, आउटपुट डिवाइसेज, प्रोसेसर इत्यादि के सारे गतिविधियों के बीच तालमेल बैठाता है।

Memory Unit (MU)
यह डोटा तथा निर्देशों के संग्रह करने में प्रयुक्त होता है। इसे मुख्यतः दो वर्गों प्राइमरी तथा सेकेंडरी मेमोरी में विभाजित करते हैं। जब कम्प्यूटर कार्यशील रहता है, अर्थात् वर्तमान में उपयोग हो रहे डेटा तथा निर्देश का संग्रह प्राइमरी मेमोरी (रजिस्टर) में होता है। सेकेंडरी मेमोरी का उपयोग बाद (later) में उपयोग होने वाले डोटा तथा निर्देशों को संग्रहीत करने में होता है।

1.7.2 Output Unit
वैसी इकाई जो सेन्ट्रल प्रोसेसिंग यूनिट से डेटा लेकर उसे यूजर को समझने योग्य बनाता है। जैसा कि, जब हम सुपर मार्केट में बिल अदा करते हैं तो हमें रसीद प्राप्त होता है, जो एक आउटपुट का रूप है। यह आउटपुट उपकरण (Output Device) प्रिन्टर से प्राप्त होता है।

1.7.3 Input Unit
वैसी इकाई जो यूजर से डेटा प्राप्त कर सेन्ट्रल प्रोसेसिंग यूनिट को इलेक्ट्रॉनिक पल्स के रूप में प्रवाहित (transmit) करता है। जैसा कि ऑटोमेटिक टेलर मशीन (Automatic Teller Machine (ATM)) में जब हम निकसी (withdrawal) के लिए जाते हैं तो हमें पिन नम्बर (Personal Identification Number) डालना होता है। उसके लिए इनपुट इकाई के रूप में कीपैड का उपयोग किया जाता है। Garbage in Gabage out (GIGO) कम्प्यूटर विज्ञान में एक concept है, जो बताता है कि इनपुट की Accuracy पर आउटपुट की Accuracy निर्भर करती है।

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Defining a Network. Or What Is A Network In Computer Science?

Topic – Defining a Network
Topic From –  Understanding Network,
Book Name – Computer Science For Dummies,



QuestionDefining a Network. Or What Is A Network In Computer Science?

Answer – A network is two or more of two computers, connected, so that they can exchange information (such as email messages or documents) or share resources (say, disk storage or printers). In maximum cases, this connection is made via electrical cables that carry the information in the form of electrical signals. Other types of connections are used, too. For example, computers can communicate via fiver optic cables at extremely high speeds by using impulses of light. And we all know that in a wireless network, computers communicate by using radio signals.

In addition to the hardware that supports a network, you also need special software to enable communications. Earlier, one had to add this software to each computer on the network. Nowadays, network support is built in to all the major operating systems, including all current versions of Windows, Macintosh operating systems, and Linux.



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What M&A Means for Programmatic Marketers (Digital Marketing)

Topic – What M&A Means for Programmatic Marketers (Digital Marketing)
Topic From – Digital Marketing In Computer Science
Book Name – Big Book Of Digital Marketing



🌸 What M&A Means for Programmatic Marketers (Digital Marketing)


Let’s group M&A deals into three scenarios:

1. The land grab or double-down: Two companies in the space combine, or a big company buys a smaller one to gain market share and/or access to a new  geographic territory.
This situation is a clear chance for the acquirer to gain access to a new set of clients and partners while taking out a competitor at the same time. As an example, Ensighten recently purchased Tag man. These transactions make sense, but if I am on the client side of that table, what does this kind of deal do for me? Usually the acquired-side client is gobbled up into a bigger system with new processes and workflow to deal with, and the consolidation could possibly lead to higher rates.

2. Whitespace: When a company acquires a complementary business to fill in one of its existing soft spots and help complete a strategic vision.
The benefit of filling in the whitespace is that the whole is often greater than the sum of the parts. For a marketer, this is the best-case scenario, because it should allow the two companies to grow with each other, offering better products and services. Examples of this kind of acquisition are Dstillery (formerly Media6Degrees) buying Every screen Media to get into mobile, and Ignition One purchasing notice for the DMP and email capabilities

3. Random:  A big company makes an acquisition just because it can.
Sometimes acquisitions leave you scratching your head, such as Microsoft buying Aquantive, Google purchasing Motorola, or HP buying Palm. Yes, these could be profitable business lines, but it’s not evident that they need to be part of one greater company. Often you’ll see the acquired company shut down a few years later. This situation was common during the dot-com bubble and is less than ideal for clients.





There is uncertainty for incoming clients even in the best-case scenarios. The best that can happen is a warm welcome with new, complementary products and services that add value to the relationship. Marketer clients also need to hope for continued relationships with their day-to-day contacts (there’s always the chance of employee cash-outs).

The worst possibility is that an advertiser’s trusted partner is shut down as a direct result of a sale. That forces the marketer to sign on with another set of partners to maintain their programmatic operation, which means new products, work-flows, and people.

There are plenty of great successes to dispel any doom and gloom, but clients should always do their due diligence and seek out answers for the following:

• Which of the three acquisition scenarios is this?
• What will change in the near- and long-term?
• Will my client-facing teams change?
• Does my contract automatically get assigned to the new entity?

The final piece is culture and client service. Each company has its own DNA that attracts clients. It’s very hard not to change that DNA during a big acquisition, and there is no magic bullet for transposing culture from one entity to another. The best advice for marketers is to do their digging and always maintain a few programmatic partners in a testing pattern. If things start to decline, find a new home for the business.



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Why Retailers Win with Google Shopping (Digital Marketing)

Topic – Why Retailers Win with Google Shopping (Digital Marketing)
Topic From – Digital Marketing In Computer Science
Book Name – Big Book Of Digital Marketing



Why Retailers Win with Google Shopping (Digital Marketing)


So, Why do you think Google Product Listing Ads have been so successful for retailers over the past year?

PLAs have seen tremendous performance in the year and a half since they launched as a paid offering. Just comparing the key Q4 2013 to 2012, impressions and clicks more than tripled, and spend grew at roughly twice that rate. Even compared to traditional PPC, which also saw huge growth, the rates for PLAs were downright astonishing.

The key is in the CTR, which is roughly 50% higher than traditional PPC. The simple fact is PLAs put the product, price and image directly in front of the shopper who raises their hand and says “I want to buy this.” Searchers can virtually window shop from the SERP and know that they’re a click away from buying what they want at the price they’re willing to pay.



But, The Question Is – How do you see Google Shopping playing a larger, perhaps different role in Retail Search in the next 2-3 years?

The most obvious change coming is Google Shopping Campaigns, which essentially cements the concept that PLAs have gone from an optimizable feeds-based system to an optimization system first and foremost, just one that happens to be supported by a product feed. The other thing to keep an eye on is in the mobile space, where PLAs underperformed in the key holiday weekend last year. Expect a lot of attention on bringing up both CTR and spend for mobile traffic.



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The Expense of Discounting (Digital Marketing)

Topic – The Expense of Discounting (Digital Marketing)
Topic From – Digital Marketing In Computer Science
Book Name – Big Book Of Digital Marketing



The Expense of Discounting (Digital Marketing)


Sales are down. While it’s tempting to develop promotions with discounts to invigorate online conversions, you need to step back and see what your data is really telling you.
The fact is that a discount campaign may cause the numbers to jump up in the short term, but it also has the potential to cause more harm than good – especially when you might not even need it to trigger the response. While it can be an effective way to drive sales, it is better for the marketer to understand what actually makes a person tick. More often than not, other things will help solicit favourable response from customers. In fact, sometimes leading with or regularly providing offers to customers can create an expectation for future offers and even a devaluation of the brand.

When you think about this from the customer perspective, what they really want is for the marketer to put the right product in front of them at the right time at a “fair” price. If you consider your own buying habits, even the greatest of discounts cannot move you to buy a product for which you have little or no use. In the same fashion you are always willing to pay full retail price for that product that you love or must have. The successful use of offers must find a balance between those two scenarios.

Offers are best used to entice a customer to try a new product, a substitute product or a product that’s closely related to one that they are already using. They can also be used to successfully cross-sell products or service to the customers already showing loyalty to your brand. This cross-sell product or service does not have to be related to the current products your customers are buying but usually data can suggest to the marketer which products might be a good option.




Strategically placing the right “fringe” product in front of your customers may be enough to elicit response. If not, a discount or special offer can be tested to determine whether or not it is price or something else that’s causing reluctance on the part of the customer.

Interestingly enough, my experience has taught me that discounts and promotions tend to be one of the weaker tools a marketer can use to drive long term, sustained response. I have seen successful campaigns drive sales with the use of no offers whatsoever. The best formula for success seems to be fairly simple: Understand who the customers are and then consistently put relevant products and messages in front of them. And don’t forget that almost all brands have several segments of customers so they need to talk to each of them differently.

There are some really cool things we can do with data that will help to indicate which customers (segments) are likely to buy certain products. Through testing we can learn how to present marketing messages (and yes, some may have offers) in front of them to move them through the customer lifecycle to a purchase. Data management for that clear view of the customer is at the hub of that understanding.



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