Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity. Big Data has the potential to help companies improve operations and make faster, more intelligent decisions. This data, when captured, formatted, manipulated, stored, and analyzed can help a company to gain useful insight to increase revenues, get or retain customers, and improve operations. An Example An example of Big Data might be petabytes (1,024 terabytes) or exabytes (1,024 petabytes) of data consisting of billions to trillions of records of millions of people—all from different sources (e.g. Web, sales, customer contact center, social media, mobile data and so on). The data is typically loosely structured data that is often incomplete and inaccessible.
What is Data Science? "We have lots of data – now what?" (How can we unlock real value from our data?) Data science is a multidisciplinary blend of data inference, algorithmm development, and technology in order to solve analytically complex problems. At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value: Data Warehouse Discovery of Data Insight Quantitative data analysis to help steer strategic business decisions Development of Data Product Algorithm solutions in production, operating at scale (e.g. recommendation engines) Business Value I For more details visits: https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-1-python-basics?ex=1
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