GDP or Gross Domestic Product GDP or Gross domestic product refers to total market value of all the final goods and services produced in an economy in a given period of time. For India, this time period is from 1st April to 31st March. This means it measures the value of final goods and services produced within a geographic boundary regardless of the nationality of the individual or firm. For instance, cars manufactured in India by Japanese company will be included in Indian GDP. Similarly, the Jaguar cars manufactured in UK by Tata will not be counted in India’s GDP. It refers to only final output of such goods and services. The rule that only finished or final goods must be counted is necessary to avoid double or triple counting of raw materials, intermediate products, and final products. For example, the value of automobiles already includes the value of the steel, glass, rubber, and other components that have been used to make them. To be precise, we define the following: a) Final Output: Goods and Services purchased for final use. b) Intermediate Goods/Factors of Production/Raw Materials: Products used as input in the production of some other product. There are two ways to take into account double counting: i. Calculate only the value of the final product. ii. Calculate the value added at each stage of production, from the beginning of the process to the end. Specifically, it is derived by subtracting the value of the intermediate good from the value of the sale.
What is Data Science? Data science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms. It is a multidisciplinary field that uses tools and techniques to manipulate the data so that you can find something new and meaningful. Data science uses the most powerful hardware, programming systems, and most efficient algorithms to solve the data related problems. It is the future of artificial intelligence. In short, we can say that data science is all about: Asking the correct questions and analyzing the raw data. Modeling the data using various complex and efficient algorithms. Visualizing the data to get a better perspective. Understanding the data to make better decisions and finding the final result. Example: Let suppose we want to travel from station A to station B by car. Now, we need to take some decisions such ...
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