What is Data Science?
Data Science refers to the multi-disciplinary field that extracts knowledge and insights from data using processes, algorithms, and systems based in the scientific method. Data science is a melting pot of statistics, machine learning, and data analysis, using each to their strengths to understand and conceptualize real-world phenomena. Referenced as early as 1960, the term "data science" didn't really come into its own until 1990's. As technology became more powerful, and the access to information became more widespread, the applications of data science proliferated. Turing award winner Jim Gray refers to data science as the "fourth paradigm" of science, along with empirical, theoretical, and computational. Data science offers a way of finding patterns, making predictions, and performing analysis to better understand a world that is more connected than it ever has been before.
Image from Flatiron School
Data Science vs. Statistics
Applications of Data Science
Since the exponential increase in computational power, and the proliferation of a more connected world, data science has become more relevant than ever. Data science can be applied in almost any discipline that involves taking in large data sets and extracting insight. From warehouse delivery logistics, to the cutting edge of artificial intelligence, data science is an invaluable tool for the creation and function of much of our modern world.
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Banks use data science to comb through their financial data in order to detect fraud. In order to minimize the bank's losses, customers are now profiled based on their spending and saving data. Using data science, banks are able to better understand their customer's past expenditures, creating probabilities for potential risk and default. Similarly, by using data science tools to understand a customer's spending profile, a bank's customers are now alerted more promptly to potential compromises to their account.