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Data science involves extracting insights and knowledge from data using various tools and techniques, including statistics. However, statistics primarily focuses on collecting, analyzing, interpreting, and presenting data, while data science encompasses a broader range of skills, including programming, machine learning, and domain expertise, to derive insights and make decisions.
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Statistics is like the toolkit you use to understand and make predictions based on data. It's all about collecting, analyzing, and interpreting numbers. Think of it as trying to find patterns or insights from data, using graphs or specific statistical tests.
Data science, on the other hand, is a bigger field that uses statistics but also includes other techniques. It involves collecting both structured (neatly organized) and unstructured (messy and harder to organize) data, cleaning it up, and then analyzing it with statistics, machine learning, and programming. Data science aims to solve real-world problems by making sense of and using large amounts of data in practical ways.
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Related Questions
Currently I am working as a tester now, and looking to get trained in Data scientist.
Will that be a good decision, if I change my stream and move to data scientist field ?
Is that possible to do machine learning and Data science course after B.com, MBA Finance and marketing students and how is career growth?
I want to get into data science but I dont have any prior knowledge on any of the programing languages, how do I go about it?
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