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Step 1: Define the aim of your research Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement: what is the practical or scientific issue that you want to address and why does it matter?


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Data big or small requires scrubbing to improve data quality and get stronger results; all data must be formatted correctly, and any duplicative or irrelevant data must be eliminated or accounted for. Dirty data can obscure and mislead, creating flawed insights. 4. Analyze Data. Getting big data into a usable state takes time.


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The Computer-Aided Design ("CAD") files and all associated content posted to this website are created, uploaded, managed and owned by third-party users. Each CAD and any associated text, image or data is in no way sponsored by or affiliated with any company, organization or real-world item, product, or good it may purport to portray.


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Written by Coursera Staff • Updated on Nov 20, 2023. Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions. "It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock.


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Data are made up of numbers, letters and symbols. When organized into sets, phrases, or patterns, data become information. We use information to identify needs, measure impacts and inform decisions. If the data underlying that information are incorrect in some respect, then our conclusions and decisions could also be wrong or misleading.


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