Applications of Data Science
Applications of Data Science
There are various applications of Data Science in real world, here are few of them.
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Gain Customer Understanding
Information about your customers can reveal details about their habits, demographics, preferences, interests, and more. With so many sources of customer information, a basic understanding of data science can help with understanding. In the meantime, you can collect customer data every time they visit your website or brick and mortar store, add something to their cart, complete a purchase, open an email, or engage in social media posts. After verifying that the data from each source is accurate, you need to integrate it into a process called data conflict. This may include matching the customer’s email address with its credit card details, social media handles, and purchase identification. By combining data, you can reach conclusions and identify trends in their behavior.
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Increase Security
You can also use data science to increase the security of your business and protect sensitive information. For example, banks use sophisticated machine-learning techniques to detect fraud by deviating from the user's general financial activities. These algorithms can capture fraud faster and with greater accuracy than humans, simply because of the large amount of data generated on a daily basis.
Even if you do not work in a bank, algorithms can be used to protect sensitive information through the encryption process. Learning about data privacy can ensure that your company does not misuse or share sensitive customer information, including credit card details, medical information, Social Security numbers, and contact details.
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Report Internal Funds
Your organization's financial team can use data science to create reports, generate forecasts, and analyze financial trends. Information on company cash flows, assets, and liabilities is collected on a regular basis, which financial analysts can use to manually or algorithmically determine the trend of financial growth or decline.
For example, if you are a financial analyst given a forecasting function, you can use forecasting analysis to do so. This will require calculating the average selling price predicted for each unit in future periods and multiplying it by the number of units expected to be sold at those times. You can estimate both the average price and the expected number of units sold by finding company data trends and industry history, which must be relevant, refined, and organized, this is how data science works.
