This database is a collection of maps created from the 28 SAS-2 observation files. The original observation files can be accessed within BROWSE by changing to the SAS2RAW database. For each of the SAS-2 observation files, the analysis package FADMAP was run and the resulting maps, plus GIF images created from these maps, were collected into this database. Each map is a 60 x 60 pixel FITS format image with 1 degree pixels. The user may reconstruct any of these maps within the captive account by running FADMAP from the command line after extracting a file from within the SAS2RAW database. The parameters used for selecting data for these product map files are embedded keywords in the FITS maps themselves. These parameters are set in FADMAP, and for the maps in this database are set as 'wide open' as possible. That is, except for selecting on each of 3 energy ranges, all other FADMAP parameters were set using broad criteria. To find more information about how to run FADMAP on the raw event's file, the user can access help files within the SAS2RAW database or can use the 'fhelp' facility from the command line to gain information about FADMAP. This is a service provided by NASA HEASARC .

This database is a collection of maps created from the 28 SAS-2 observation files. The original observation files can be accessed within BROWSE by changing to the SAS2RAW database. For each of the SAS-2 observation files, the analysis package FADMAP was run and the resulting maps, plus GIF images created from these maps, were collected into this database. Each map is a 60 x 60 pixel FITS format image with 1 degree pixels. The user may reconstruct any of these maps within the captive account by running FADMAP from the command line after extracting a file from within the SAS2RAW database. The parameters used for selecting data for these product map files are embedded keywords in the FITS maps themselves. These parameters are set in FADMAP, and for the maps in this database are set as 'wide open' as possible. That is, except for selecting on each of 3 energy ranges, all other FADMAP parameters were set using broad criteria. To find more information about how to run FADMAP on the raw event's file, the user can access help files within the SAS2RAW database or can use the 'fhelp' facility from the command line to gain information about FADMAP. This is a service provided by NASA HEASARC .

Context

Companies and individuals are storing increasingly more data digitally; however, much of the data is unused because it is unclassified. How many times have you opened your downloads folder, found a file you downloaded a year ago and you have no idea what the contents are? You can read through those files individually but imagine doing that for thousands of files. All that raw data in storage facilities create data lakes. As the amount of data grows and the complexity rises, data lakes become data swamps. The potentially valuable and interesting datasets will likely remain unused. Our tool addresses the need to classify these large pools of data in a visually effective and succinct manner by identifying keywords in datasets, and classifying datasets into a consistent taxonomy.

The files listed within kaggleDatasetSummaryTopicsClassification.csv have been processed with our tool to generate the keywords and taxonomic classification as seen below. The summaries are not generated from our system. Instead they were retrieved from user input as they uploaded the files on Kaggle. We planned to utilize these summaries to create an NLG model to generate summaries from any input file. Unfortunately we were not able to collect enough data to build a good model. Hopefully the data within this set might help future users achieve that goal.

Acknowledgements

Developed with Senior Design Center at NC State in collaboration with SAS. Senior Design Team: Tanya Chu, Katherine Marsh, Nikhil Milind, Anna Owens SAS Representatives: : Nancy Rausch, Marty Warner, Brant Kay, Tyler Wendell, JP Trawinski

Description

This data set is scraped and cleaned from the glassdoor data science job openings in India in this Covid 19 situation

Data Sets

'Job.Title' : Job title 'Job.Description': Job description 'Rating': Job rating 'Company.Name': Company name 'Location': Location of the company 'Headquarters': Company headquarters 'Size': the Size of the company 'Founded': The year it was founded 'Type.of.ownership': Public or private ownership 'Industry': Type of industry company belongs 'Sector': Type of sector company belongs 'Revenue': Yearly revenue by company 'Competitors': Company competitors 'Python': Does description has python keyword in it 1 if yes 0 if no 'R.Prog': Does description has R prog keyword in it 1 if yes 0 if no 'Excel': Does description has Excel keyword in it 1 if yes 0 if no 'Hadoop': Does description has Hadoop keyword in it 1 if yes 0 if no 'SQL': Does description has SQL keyword in it 1 if yes 0 if no 'SAS: Does description has SAS keyword in it 1 if yes 0 if no

Fatturato per gli ultimi anni, elenco utili/perdita, costo dipendenti, soci esponenti e contatti per KEYWORD S.A.S. DI MAFFEI IGOR in Rapolano Terme (SI)