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What is Difference Between Data Science and Big Data

Data Science is the field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining. it mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization’s competitive advantage.

Big Data is a term used to refer to data sets that are too large or complex for traditional data-processing application software to adequately deal with. Data with many cases offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate.

Difference Between Data Science and Big Data 

Data Science involves the processing of big data. You can say that data science is a broader term for the techniques involved in retrieving insights and information from the data. Applications of Data Science

  • Digital Advertisements data Science algorithms hugely benefit the digital marketing world, ranging from the display banners but not limited to digital billboards. This is the mean reason for digital ads getting higher CTR than traditional advertisements.
  • Recommender System does not only make it easy to find relevant products from billions of products available but also adds a lot to user-experience. Companies promote a huge range of products and give you suggestions, while you browse the internet or through in-app ads, depending on the demand and relevance, which are influenced by your search history.
  • Internet search Data Science is the backbone that determines the underlying algorithm behind search engine results. Search engines make use of data science algorithms to deliver the best results for search queries in a fraction of seconds.
  • Image/Speech Recognition provides an enhanced user experience to the individuals over the internet. It offers barcode scanning facility in mobile, tag your friend’s facility on Facebook, and to perform an image search on google by using a face recognition algorithm.

Big Data implies an enormous volume of raw data which a usual application such as a Traditional Database Management System can’t process efficiently. Applications of Big Data

  • financial services are consumed by organizations such as retail banks, credit card companies, insurance firms, private wealth management advisories, venture capitalists, as well as investment banks.
  • Education It is the application area of Big Data where the individuals can make a bright career by yielding big data professionals for the businesses, companies, and industries.
  • communications Gaining new subscribers, retaining customers, and expanding within current subscriber bases are top priorities for telecommunication service providers. Telecommunication companies need big data to gather new subscribers, retain the old ones, as well as spreading their base with existing customers.
  • Retail requires the ability to analyze all the disparate data sources that companies deal with every day, including the weblogs, customer transaction data, social media, store-branded credit card data, and loyalty program data. The capability of analyzing diverse sources of data that businesses handle on a daily basis is what big data stands for. Be it customer transaction data, weblogs, data from store-branded credit cards, loyalty program data, or social media, big data is mighty enough to take charge of it.