This dataset includes six tables which were custom ordered from Statistics Canada. All tables include commuting characteristics (mode of commuting, duration/distance), labour characteristics (employment income groups in 2015, Industry by the North American Industry Classification System 2012), and visible minority groups. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and variables: Geography: Place of Work (POW), Census Tract (CT) within CMA Vancouver. The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. However, it will be provided upon request. GNR values for POR and POW are different for each geography. Universe: The Employed Labour Force having a usual place of work for the population aged 15 years and over in private households that are rented (Tenure rented), full year-full time workers (40-52weeks) Variables: Visible minority (15) 1. Total - Visible minority 2. Total visible minority population 3. South Asian 4. Chinese 5. Black 6. Filipino 7. Latin American 8. Arab 9. Southeast Asian 10. West Asian 11. Korean 12. Japanese 13. Visible minority, n.i.e. 14. Multiple visible minorities 15. Not a visible minority Commuting duration and distance (18) 1. Total - Commuting duration 2. Less than 15 minutes 3. 15 to 29 minutes 4. 30 to 44 minutes 5. 45 to 59 minutes 6. 60 minutes and over 7. Total - Commuting distance 8. Less than 1 km 9. 1 to 2.9 km 10. 3 to 4.9 km 11. 5 to 6.9 km 12. 7 to 9.9 km 13. 10 to 14.9 km 14. 15 to 19.9 km 15. 20 to 24.9 Km 16. 25 to 29.9 km 17. 30 to 34.9 km 18. 35 km or more Main mode of commuting (7) 1. Total - Main mode of commuting 2. Driver, alone 3. 2 or more persons shared the ride to work 4. Public transit 5. Walked 6. Bicycle 7. Other method Employment income groups in 2015 (39) 1. Total – Total Employment income groups in 2015 2. Without employment income 3. With employment income 4. Less than $30,000 (including loss) 5. $30,000 to $79,999 6. $30,000 to $39,999 7. $40,000 to $49,999 8. $50,000 to $59,999 9. $60,000 to $69,999 10. $70,000 to $79,999 11. $80,000 and above 12. Median employment income ($) 13. Average employment income ($) 14. Total – Male Employment income groups in 2015 15. Without employment income 16. With employment income 17. Less than $30,000 (including loss) 18. $30,000 to $79,999 19. $30,000 to $39,999 20. $40,000 to $49,999 21. $50,000 to $59,999 22. $60,000 to $69,999 23. $70,000 to $79,999 24. $80,000 and above 25. Median employment income ($) 26. Average employment income ($) 27. Total – Female Employment income groups in 2015 28. Without employment income 29. With employment income 30. Less than $30,000 (including loss) 31. $30,000 to $79,999 32. $30,000 to $39,999 33. $40,000 to $49,999 34. $50,000 to $59,999 35. $60,000 to $69,999 36. $70,000 to $79,999 37. $80,000 and above 38. Median employment income ($) 39. Average employment income ($) Industry - North American Industry Classification System (NAICS) 2012 (54) 1. Total - Industry - North American Industry Classification System (NAICS) 2012 2. 11 Agriculture, forestry, fishing and hunting 3. 21 Mining, quarrying, and oil and gas extraction 4. 22 Utilities 5. 23 Construction 6. 236 Construction of buildings 7. 237 Heavy and civil engineering construction 8. 238 Specialty trade contractors 9. 31-33 Manufacturing 10. 311 Food manufacturing 11. 41 Wholesale trade 12. 44-45 Retail trade 13. 441 Motor vehicle and parts dealers 14. 442 Furniture and home furnishings stores 15. 443 Electronics and appliance stores 16. 444 Building material and garden equipment and supplies dealers 17. 445 Food and beverage stores 18. 446 Health and personal care stores 19. 447 Gasoline stations 20. 448 Clothing and clothing accessories stores 21. 451 Sporting goods, hobby, book and music stores 22. 452 General merchandise stores 23. 453 Miscellaneous store retailers 24. 454 Non-store retailers 25. 48-49 Transportation and warehousing 26. 481 Air transportation 27. 482 Rail transportation 28. 483 Water...

Data on immigrant status and period of immigration by place of birth, citizenship, age and gender for the population in private households in Canada, provinces and territories, census metropolitan areas and parts.

The purpose of this project is to write a large and in sync dataset focused patient characteristics for identify the Risk groups and characteristics human-level that impact on infection, Complication and Death as a result of the disease

for more detail about the data:

https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

last date for update 06.12.2020

4535323 rows

Version 5:

A version that includes cleaning the data and engineering new features for more detail : https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

Version 6:

Machine-ready version of machine learning model Consists only of INT and FLOAT for more detail : https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

problem with dataset

• There may be duplicate cases (which come from different data systems) Focusing on countries: France, Korea, Indonesia, Tunisia, Japan, canada, new_zealand, singapore, guatemala, philippines, india, vietnam, hong kong , Toronto, Mexico.

• I did not check the credibility of the sources

• Concerns of the credibility of the Mexican government's data

• Concerns about the credibility of the data of the Chinese government

Acknowledgements and Sources

india_wiki https://www.kaggle.com/karthikcs1/covid19-coronavirus-patient-list-karnataka-india

philippines https://www.kaggle.com/sundiver/covid19-philippines-edges

france https://www.kaggle.com/lperez/coronavirus-france-dataset

korea https://www.kaggle.com/kimjihoo/coronavirusdataset

indonesia https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

tunisia https://www.kaggle.com/ghassen1302/coronavirus-tunisia

japan https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan

world https://github.com/beoutbreakprepared/nCoV2019/tree/master/latest_data

canada https://www.kaggle.com/ryanxjhan/coronaviruscovid19-canada

new_zealand https://www.kaggle.com/madhavkru/covid19-nz

singapore https://www.kaggle.com/rhodiumbeng/singapores-covid19-cases

guatemala https://www.kaggle.com/ncovgt2020/covid19-guatemala

colombia https://www.kaggle.com/sebaxtian/covid19co

mexico https://www.kaggle.com/lalish99/covid19-mx

india_data https://www.kaggle.com/samacker77k/covid19india

vietnam https://www.kaggle.com/nh

kerla https://www.kaggle.com/baburajr/covid19inkerala

hong_kong https://www.kaggle.com/teddyteddywu/covid-19-hong-kong-cases

toronto https://www.kaggle.com/divyansh22/toronto-covid19-cases

Determining the severity illness according to WHO: https://www.who.int/publications/i/item/clinical-management-of-covid-19

• Each update contains the information found in the previous version

*Thanks to all sources

*If you have any helpful information or suggestions for improvement, write

Building notebook

• netbook PART A - cleaning and conact the data: https://www.kaggle.com/shirmani/characteristics-of-corona-patient-ds-v4

• netbook PART B- features Engineering: https://www.kaggle.com/shirmani/build-characteristics-corona-patients-part-b/edit

• part C data QA https://www.kaggle.com/shirmani/qa-characteristics-corona-patients-part-c

• netbook PART D - format the data to int and float cols (model preparation): https://www.kaggle.com/shirmani/build-characteristics-corona-patients-part-d

This table provides the number of temporary foreign workers in Canada and in provinces by their country of citizenship.

Average and median market, total and after-tax income of individuals by visible minority group, Indigenous group and immigration status, Canada and provinces.

Abstract

This study provides an update on measures of educational attainment for a broad cross section of countries. In our previous work (Barro and Lee, 1993), we constructed estimates of educational attainment by sex for persons aged 25 and over. The values applied to 129 countries over a five-year intervals from 1960 to 1985.

The present study adds census information for 1985 and 1990 and updates the estimates of educational attainment to 1990. We also have been able to add a few countries, notably China, which were previously omitted because of missing data.

Dataset:

Educational attainment at various levels for the male and female population. The data set includes estimates of educational attainment for the population by age - over age 15 and over age 25 - for 126 countries in the world. (see Barro, Robert and J.W. Lee, "International Measures of Schooling Years and Schooling Quality, AER, Papers and Proceedings, 86(2), pp. 218-223 and also see "International Data on Education", manuscipt.) Data are presented quinquennially for the years 1960-1990;

Educational quality across countries. Table 1 presents data on measures of schooling inputs at five-year intervals from 1960 to 1990. Table 2 contains the data on average test scores for the students of the different age groups for the various subjects.Please see Jong-Wha Lee and Robert J. Barro, "Schooling Quality in a Cross-Section of Countries," (NBER Working Paper No.w6198, September 1997) for more detailed explanation and sources of data.

Geographic coverage

The data set cobvers the following countries: - Afghanistan - Albania - Algeria - Angola - Argentina - Australia - Austria - Bahamas, The - Bahrain - Bangladesh - Barbados - Belgium - Benin - Bolivia - Botswana - Brazil - Bulgaria - Burkina Faso - Burundi - Cameroon - Canada - Cape verde - Central African Rep. - Chad - Chile - China - Colombia - Comoros - Congo - Costa Rica - Cote d'Ivoire - Cuba - Cyprus - Czechoslovakia - Denmark - Dominica - Dominican Rep. - Ecuador - Egypt - El Salvador - Ethiopia - Fiji - Finland - France - Gabon - Gambia - Germany, East - Germany, West - Ghana - Greece - Grenada - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong Kong - Hungary - Iceland - India - Indonesia - Iran, I.R. of - Iraq - Ireland - Israel - Italy - Jamaica - Japan - Jordan - Kenya - Korea - Kuwait - Lesotho - Liberia - Luxembourg - Madagascar - Malawi - Malaysia - Mali - Malta - Mauritania - Mauritius - Mexico - Morocco - Mozambique - Myanmar (Burma) - Nepal - Netherlands - New Zealand - Nicaragua - Niger - Nigeria - Norway - Oman - Pakistan - Panama - Papua New Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Romania - Rwanda - Saudi Arabia - Senegal - Seychelles - Sierra Leone - Singapore - Solomon Islands - Somalia - South africa - Spain - Sri Lanka - St.Lucia - St.Vincent & Grens. - Sudan - Suriname - Swaziland - Sweden - Switzerland - Syria - Taiwan - Tanzania - Thailand - Togo - Tonga - Trinidad & Tobago - Tunisia - Turkey - U.S.S.R. - Uganda - United Arab Emirates - United Kingdom - United States - Uruguay - Vanuatu - Venezuela - Western Samoa - Yemen, N.Arab - Yugoslavia - Zaire - Zambia - Zimbabwe