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## TODO
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- [x] viz: add 'back to top' link next to each company
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- - [ o ] data collection: extend scraper to collect both problem ** type** and ** company**
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- - [ ] viz: add a new 'type' column for the problem frequency table
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- - [ ] viz: add the 'major problem type' info for each company
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- - [ ] data analysis: run UMAP to on company-indexed pivot wider dataset to find clusters for company interview profiles
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- - [ ] refactor: convert jupyter notebook to .py file for ETL
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+ - [ ] data collection: extend scraper to collect both problem ** type** and ** company**
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+ - [ ] viz: add a new 'type' column for the problem frequency table
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+ - [ ] viz: add the 'major problem type' info for each company
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+ - [ ] data analysis: run UMAP to on company-indexed pivot wider dataset to find clusters for company interview profiles
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+ - [ ] refactor: convert jupyter notebook to .py file for ETL
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},
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{
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"cell_type" : " code" ,
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- "execution_count" : 13 ,
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+ "execution_count" : 16 ,
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"id" : " d1410d74" ,
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"metadata" : {},
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"outputs" : [],
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