Store data Extract data Preproce ss data Analyze data Present results Publish

Store data extract data preproce ss data analyze data

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Store data Extract data Preproce ss data Analyze data Present results Publish data Pri v acy/ e thic s
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Frequency Granularity Cost Takes many forms: money, time, storage, processing, effort, etc. Utility 14
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15 Define questio ns Collect/fin d data Store data Extract data Preproce ss data Analyze data Present results Publish data Pri v acy/ e thic s
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Compression vs plain Publish Open access Metadata Security 16
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17 Define questio ns Collect/fin d data Store data Extract data Preproce ss data Analyze data Present results Publish data
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Data queries to extract useful subsets or slices of the data 18
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19 Define questio ns Collect/fin d data Store data Extract data Preproce ss data Analyze data Present results Publish data
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Reformatting Conversion Cleaning Imputation Integration Feature generation Feature construction Feature selection 20 PRE-PROCESS DATA
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Possible Pre-processing Steps Data reformatting: changing the format or encoding of the data E.g.: Changing an image from JPG to PDF Data conversion: changing the unit of measurement or representation E.g.: Average temperatures in different countries, data is in C and F Data cleaning: detecting and correcting errors E.g.: Temperature time series: 70, 68, 65, 60, 60, 60, 500 , 59, 57,… Data imputation: hypothesizing missing values E.g.: Temperature time series: 9am 50, 10am --, 11am 60, 12pm 65 Data integration: mapping objects across datasets, merging them E.g.: Use data from two separate social networks 21
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Possible Pre-processing Steps Feature generation:
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  • Fall '17
  • Data Management, Computational Thinking and Data Science, Compression, Utility

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