These days, news of data theft is heard every day. And every day some company is accused of data theft. In such a situation, the question arises in the mind that what is this data after all ? And why is it stolen? Is it so valuable that it has to be stolen? So for this you have to understand the whole game of your data. Also it has to be understood that what is data science? And how the data is used with its help. So let us know in detail what is data science?
डेटा विज्ञान आज कई उद्योगों का एक अनिवार्य हिस्सा है, जो कि भारी मात्रा में डेटा का उत्पादन किया जाता है, और आईटी सर्किलों में सबसे अधिक बहस वाले विषयों में से एक है। इसकी लोकप्रियता पिछले कुछ वर्षों में बढ़ी है, और कंपनियों ने अपने व्यवसाय को बढ़ाने और ग्राहकों की संतुष्टि बढ़ाने के लिए डेटा विज्ञान तकनीकों को लागू करना शुरू कर दिया है। इस लेख में, हम जानेंगे कि डेटा विज्ञान क्या है, और आप डेटा वैज्ञानिक कैसे बन सकते हैं ।
You must have often noticed that when you watch a channel on Youtube again and again! So videos related to it start getting recommended to you automatically. Similarly when you search a product on Google! So you start seeing ads for the same product everywhere. Now the question is how is this possible? After all, how did Facebook, Instagram and Amazon know about the product searched on Google? So this is really the wonder of Data Science .
डेटा साइंस क्या है?
डेटा विज्ञान अध्ययन का क्षेत्र है जो अनदेखी पैटर्न खोजने, सार्थक जानकारी प्राप्त करने और व्यावसायिक निर्णय लेने के लिए आधुनिक उपकरणों और तकनीकों का उपयोग करके डेटा की विशाल मात्रा से संबंधित है। डेटा साइंस प्रेडिक्टिव मॉडल बनाने के लिए जटिल मशीन लर्निंग एल्गोरिदम का उपयोग करता है।
विश्लेषण के लिए उपयोग किया गया डेटा कई अलग-अलग स्रोतों से आ सकता है और विभिन्न स्वरूपों में प्रस्तुत किया जा सकता है।
Actually companies like Youtube, Facebook and Amazon use data science to reach their customers. And for this use the data given by the users. But data science is not just about tracking customers. Rather, its scope is huge. And as time is progressing, the use of data science is also increasing continuously. That’s why it is important that you know about Data Science.
What is Data Science?
Data Science is a science in which data is studied. That is, information is extracted from the data by churning it. And for this various Algorithms , Systems and Scientific Methods are used. If I say in simple language, it is like extracting gold from e-Waste. That is, a lot of structured and unstructured data is collected and passed through various processes. And from that Knowledge and Insights are separated and used in various works.
Data science is generally used to study, organize and extract information about big data . For example, data science is used to separate the figures of women, men, literate, illiterate, children, youth, old, government employees, private employees, etc. from the data of the country’s population (census).
डेटा विज्ञान के लिए आवश्यक शर्तें
यहां कुछ तकनीकी अवधारणाएं दी गई हैं जिनके बारे में आपको डेटा विज्ञान क्या है, यह जानने से पहले पता होना चाहिए।
1. मशीन लर्निंग
मशीन लर्निंग डेटा साइंस की रीढ़ है। आंकड़ों के बुनियादी ज्ञान के अलावा डेटा वैज्ञानिकों को एमएल की ठोस समझ होनी चाहिए।
गणितीय मॉडल आपको डेटा के बारे में जो पहले से जानते हैं उसके आधार पर आपको त्वरित गणना और भविष्यवाणियां करने में सक्षम बनाता है। मॉडलिंग भी मशीन लर्निंग का एक हिस्सा है और इसमें यह पहचानना शामिल है कि किसी समस्या को हल करने के लिए कौन सा एल्गोरिदम सबसे उपयुक्त है और इन मॉडलों को कैसे प्रशिक्षित किया जाए।
सांख्यिकी डेटा विज्ञान के मूल में हैं। आँकड़ों पर एक मजबूत संभाल आपको अधिक बुद्धिमत्ता निकालने और अधिक सार्थक परिणाम प्राप्त करने में मदद कर सकता है।
एक सफल डेटा साइंस प्रोजेक्ट को निष्पादित करने के लिए प्रोग्रामिंग के कुछ स्तर की आवश्यकता होती है । सबसे आम प्रोग्रामिंग भाषाएं पायथन हैं, और आर। पायथन विशेष रूप से लोकप्रिय है क्योंकि इसे सीखना आसान है, और यह डेटा विज्ञान और एमएल के लिए कई पुस्तकालयों का समर्थन करता है ।
एक सक्षम डेटा वैज्ञानिक को यह समझने की आवश्यकता है कि डेटाबेस कैसे काम करते हैं, उन्हें कैसे प्रबंधित करें और उनसे डेटा कैसे निकालें।
Apart from this, various companies use customer data to improve their product, increase sales and add new cutomers . And use Data Science to study this data. So that they know how much people are liking their products? And what other improvements can be made in them. So that customers can stay connected with them.
How does Data Sciences work?
Now the question is how does data science work? How does data science work? So it’s a complicated process. But let me try to explain you in easy language. Suppose there is a huge pile of garbage, which contains some diamonds. And you have to separate them. What would you do? Obviously, first of all we will make small piles of garbage. And then taking a little bit of garbage out of the heap, they will search it. And the diamonds that are found in it, they will be separated and the waste will be separated. Processing all the waste in this way will remove all the diamonds from it. Isn’t it? Simply, that’s how data science works.
In data science also, by analyzing a lot of raw data , the information of work is found out of it. And for this various Scientific Methods and Algorithms are used. For this a Data Scientist should have sufficient skills. And he should have good knowledge of subjects like Data Engineering , Mathematics, Visualization , Programming. Only then can he get information about the work from the heap of data. Otherwise, very difficult.
A Data Scientist, first of all, finds out the problem. And then collects related data from it. After that processes it for analysis. And then explores the data. After that with the help of his skills, he does In-Depth Analysis . And finally the Analysis Result announces. During this, Machine Learning and Deep Learning are also used to make data models and make predictions.
डेटा साइंस कैसे काम करता है?
डेटा विज्ञान में कच्चे डेटा में एक समग्र, संपूर्ण और परिष्कृत रूप तैयार करने के लिए विषयों और विशेषज्ञता क्षेत्रों की अधिकता शामिल है। डेटा वैज्ञानिकों को डेटा इंजीनियरिंग, गणित, सांख्यिकी, उन्नत कंप्यूटिंग और विज़ुअलाइज़ेशन से लेकर हर चीज़ में कुशल होना चाहिए ताकि वे सूचनाओं के उलझे हुए लोगों के माध्यम से प्रभावी ढंग से झारना कर सकें और केवल सबसे महत्वपूर्ण बिट्स को संप्रेषित कर सकें जो नवाचार और दक्षता को चलाने में मदद करेंगे।
डेटा वैज्ञानिक भी एल्गोरिदम और अन्य तकनीकों का उपयोग करके मॉडल बनाने और भविष्यवाणियां करने के लिए कृत्रिम बुद्धिमत्ता, विशेष रूप से मशीन लर्निंग और डीप लर्निंग के इसके उपक्षेत्रों पर बहुत अधिक भरोसा करते हैं।
Example of Data Science
Now we will ask what is the use of data science in our daily life? Tell me if there is any real life example. So let me tell a Real Life Example of Data Science . You must be using OTT platforms like Netflix, Amazon Prime Videos, Hotstar, JioCinema, Alt Balaji and Zee5. If you do, you will also be watching your favorite Movies, Web Series and TV Shows etc. Isn’t it?
But if you have noticed, then you will know that all these OTT Platforms suggest you the same kind of movies ! what you usually see. Basically these platforms collect your data. And with the help of that you find out what kind of movies do you like to watch? That means Comedy, Action, Drama, Suspense or Science Fiction? What kind of movies do you like? Movies of the same genre are suggested to you. All this is possible only with the help of Data Science.
Who is a Data Scientist?
Data Scientist is a Highly Skilled Person . Who has lots of skills. He has a deep understanding of subjects like Data Engineering, Mathematics, Social Science, Technology, Programming , Machine Learning, Deep Learning, Statistics and Artificial Intelligence. and the ability to detect problems and find solutions
A Data Scientist knows which problem needs to be solved. And from where should the data be taken for that? That is, a data scientist is an expert in mining , cleaning and analyzing data . In simple words, Data Scientist is one such person. Who is expert in collecting, analyzing and presenting very large data .
डेटा वैज्ञानिक क्या है?
एक विशेषता के रूप में, डेटा विज्ञान युवा है। यह सांख्यिकीय विश्लेषण और डेटा खनन के क्षेत्र में विकसित हुआ। डेटा साइंस जर्नल की शुरुआत 2002 में हुई, जिसे इंटरनेशनल काउंसिल फॉर साइंस: कमेटी ऑन डेटा फॉर साइंस एंड टेक्नोलॉजी द्वारा प्रकाशित किया गया। 2008 तक डेटा साइंटिस्ट की उपाधि सामने आई थी, और इस क्षेत्र ने तेजी से उड़ान भरी। तब से डेटा वैज्ञानिकों की कमी हो गई है, भले ही अधिक से अधिक कॉलेजों और विश्वविद्यालयों ने डेटा विज्ञान की डिग्री प्रदान करना शुरू कर दिया है।
एक डेटा वैज्ञानिक के कर्तव्यों में डेटा का विश्लेषण करने के लिए रणनीति विकसित करना, विश्लेषण के लिए डेटा तैयार करना, डेटा की खोज करना, विश्लेषण करना और विज़ुअलाइज़ करना, प्रोग्रामिंग भाषाओं जैसे पायथन और आर का उपयोग करके डेटा के साथ मॉडल बनाना और अनुप्रयोगों में मॉडल को तैनात करना शामिल हो सकता है।
Important Elements of Data Science
Actually Data Science is not as easy as it looks. Its scope is quite wide. And it has many components. In this , many tools and techniques are used to collect and analyze large amount of data and extract important information. But if we talk about the main components of Data Science, then they are as follows:-
Statistics is the most important component. It is an essential component of Data Science . It is used to analyze the numerical data present in the set. For example, statistics are used to present the data of phone numbers, prices, income etc. That is why it is the most important part of data science.
2. Machine Learning
Machine learning (ML) is a part of Artificial Intelligence. Which is also used in data science. It is very easy to analyze Big Data using Machine Learning. Because with the help of this, the machines themselves analyze the data and give the result. For this a data model is prepared first. And he is well trained.
As you have already read in the previous article, How are Machine Learning Models trained ? And how is prediction taught? That’s why there is no point in telling it again. Well, the data model automates the process of data analysis . Due to which it becomes very easy to analyze the data. And also saves a lot of time.
3. Deep Learning
Deep learning is a type of advanced machine learning . Which is a part of Artificial Intelligence itself. With the help of Deep Learning, the ability to think and act like humans is developed in machines. But if we talk about Data Science, then Deep Learning is used for In-Depth Data Analysis in this. That is, data is analyzed with the help of machines.
Machine learning is generally used to process structured data . But Deep Learning is used for Unstructured and Complicated Data. Because with the help of Deep Learning, Unstructured Data can be easily processed without human help.
Data Science Life Cycle
There is a fixed process to analyze the data and extract valuable information from it! Which is called Data Science Life Cycle. By following this process only a Data Scientist can get the desired result. What is this process? Come on, let’s know. Data Science Life Cycle Step By Step :-
1. Problem Identification
The first step of data science is to identify the problem. That is, finding the problem. It is also called Business Understanding . Because for this one has to understand each and every aspect of the business. And you have to get to the root of the problem. That’s why it is very difficult. Especially when a strategy has to be made for a successful business model .
2. Collecting Data
The second step is to collect the data. This is the most important step. Because the whole further process depends on this step. That is why quality data is collected from various sources . And for this Valid and Reliable Sources are selected. That is, such sources are selected. From where you can get Fresh, Relevant and High Quality Data. This process is known as Data Mining .
This data can be anything. Like which toothpaste do you use? Which brands of clothes do you wear? Which products do you buy frequently? What kind of books do you like to read? etcetera etcetera. Apart from this, this data can be collected from any Trusted Source . Such as Social Media, Webserver, APIs etc. There are generally two ways to collect data:
- By Web Scrapping in Python
- by APIs.
3. Data Cleaning & Processing
After collecting the data, it is the turn of the next step. That is, of data preparation . This is an important stage in which the data is prepared for analysis. That is, the already collected data is cleaned. And the shortcomings therein are removed.
During this Unwanted, Duplicate and Low Quality Data is removed. And Missing Values , Rows and Columns are fixed. That is, whatever deficiencies or errors occur in the data. They are corrected so that accurate figures are obtained. This is a time consuming process. But the result is quite pleasant.
Data processing is an important process to analyze data . Usually the data that is collected from various sources is Raw Data. That is, there are many types of impurities in it. It is completely Noisy, Unfiltered and Unstructured data. Therefore it is very important to clean and process it. In this process, the help of techniques like Data Modeling and Data Clustering is taken. After processing the data properly, it becomes ready for analysis.
4. Exploratory Data Analysis
After data processing, it is the turn of Exploratory Data Analysis (EDA). This is an important step, in which in-depth analysis of the processed data is done. That is, all the data features and data properties are studied in depth. And the datasets are visualized to find the patterns and valuable insights present in the data.
5. Model Building & Evaluation
After data analysis comes the turn of Model Building and Evaluation . In this phase the data of the previous phase is divided into 2 sets. One Training Set and another Testing Set. The training set is used to train the model. For this, first a model ( ML Model ) is constructed keeping the problem in mind . And he is well trained.
Evaluation of the model is done after training. That is, by testing it is seen whether it is working properly or not? Testing set is used for this. That is , the dataset , which is kept separate from the training set, is used. So that the accuracy of the model can be accurately assessed.
6. Result Announcement
The next and last step is Result Announcement. When the model is passed in the evaluation. And starts making correct predictions. So its result is communicated. That is, the result of the model is presented through visualization. In this way the life cycle of data science goes on.
Uses of Data Science
Now a question must be coming in your mind that what is the use of data science? What are the usage of data science? So I would like to tell you that there are many uses of data science. It is used almost everywhere. But here we will talk about its main uses. So these are the main uses of data science :
- Platforms like Youtube, Facebook, Google and Netflix use data science to recommend their content. For this, user data is used. And the content is suggested according to their interest .
- Google uses data science to improve its search engine and show better search results to users, as well as spam filtering in Gmail .
- Data science is also widely used in Speech Recognition Systems like Google Assistant, Alexa, and Siri . All such Virtual Assistants learn only by utilizing the data of the users.
- Data science is also used in driverless cars . In this, Traffic Lights and other vehicles running on the road are recognized through Machine Learning.
- Transport companies like Uber and Ola also use data science to set their prices in weather, traffic and other situations .
Data Science Tools
A Data Scientist has to collect a lot of data for each of his projects. And it has to be cleaned, processed and analyzed. That’s why it is a very difficult and tiring job. But there are many such tools, which make this task easy. Come, let’s know about these tools. The Tools of Data Science :-
If you have a little knowledge about programming, then you must have known about Python . It is actually a programming language, which is used a lot in data science. If you are thinking of becoming a successful Data Scientist then it is very important for you to have knowledge of Python.
2. R Programming
It is a Statistical Programming Tool . With the help of which Data Scientist can analyze any big data.
SQL is also a programming language which is used in data science. Its full name is Structured Query Language, it is used to analyze the structured data present in the relational database.
Apache Hadoop is a popular tool for data science . It is an open source software. Which is used in data science to store large datasets .
The way continuous development is being seen in the field of Data Science. More advanced applications of Data Science can be seen in the coming times . Big tech companies are using Data Science. Along with this, its use is also being seen in the Medical Sector, Security and Transport Sector.
Hope you got data science through this article ? How does this work? And what are its uses? You must have found useful information in this topic! If you liked this article then like and share it.
Data Science : FAQs
Question 1. What is Data Science?
Answer: Data Science is a science in which data is studied. And Valuable Information is extracted from the data through various algorithms and scientific methods.
Question 2. What is a Data Scientist?
Answer: Data scientist is a professional person. Who has a good understanding of Data Engineering, Mathematics, Visualization , Programming, Statistics and Analytics. And have the ability to identify the problem and solve it.
Question 3. What does a Data Scientist do?
Answer: Data Scientist, first of all find out the problem and collect the related data. After that cleans, processes and analyzes the data. After that, builds the data model and trains and evaluates it. And finally communicates the result of the model.
Question-4. What are the main elements of data science?
Answer: Statistics , Visualization , Machine Learning and Deep Learning are the main elements of Data Science .
Question-5. What is Data Science Life Cycle?
Answer: Data Science Life Cycle is a step-by-step process of extracting information from data. In which problem detection, data collection, data cleaning and processing, analysis, modeling and evaluation are involved and finally result communication .
Question-6. What are the uses of Data Science?
Answer: Data science has many uses. For example, data science is used to recommend content to social media users and show relevant ads. Similarly, data science is used extensively in search engines to show better search results, recommend products on eCommerce websites, sell insurance policies, show traffic reports and virtual assistants.
Question-7. What tools are used in data science?
Answer: Many tools are used in data science. Such as Excel, Python, SQL, Qlik, BigML , Tableau, Machine Learning, SAS , Apache Hadoop etc.