Introduction to data science

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Data Science refers to the science behind data. What kind of science and how it works is explains actual meaning. The science of studying data analyzing deep information about that data extracted is what data scientist do. Knowing the business for which data need to be analyzed, scientist firs understand the business thoroughly, broad research, in depth understanding about the history, past transactions, business relation, brand value, and many more such information is very necessary for a scientist to perform their jobs effectively. Finding the right knowledge takes both time and effort.

Data Scientist doesn't do only analysis part but also perform as a cleanser to the data. Removes irrelevant information from the data base helps to find the correct details to work on. At the time collecting records cleaning is not possible, as what is required in long term need to give a thought upon. Cleaning take place after the collection overview. Filtering includes inconsistent data types, misspelled attributes, missing values, copied stuff etc. and many more minor and major unwanted material. Transformation can only takes place after purifying data completely in order to focus of the relevant figures and facts. Data modification is done of defined mapping rules. There are many different tools to handle the transformation of different type of data like complex or not so complex, those basically helps in better understanding the data structure. Scientist apply different tools and methods to efficiently execute the tasks.

Data science is so very important for every business organization to plan their further moves and analyze the information that is collected. Every source of details are examined very crucially to make it happen. When we talk about data science most only thinks of technology or software companies, here let me clear that it is applied on every business. If any company is missing this vital part then they might be missing a big win.

One of the way it's done that can explain in simple terms is something like this- Picking up a converting tool and feed the data, after setting up the goal we allow the tool or technology to show results in positive and negative goal achievement. This is an easy example of machine learning way, the outcome shows out of machine learning can be expected to a complex one, because machine picks complex relativity between the data. Using machine learning sounds simple to implement data science, but it's just a helping hand to deal with big data. The real task starts from here data scientist needs to clean the data, code the right learning pattern to ensure correct organization, adding feature to it. 

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