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@@ -1,3 +1,8 @@
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# ПИбд-31, Ярускин Салих Александрович, Вариант 9
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https://www.kaggle.com/datasets/surajjha101/stores-area-and-sales-data
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https://www.kaggle.com/datasets/muhammedtausif/world-population-by-countries
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https://www.kaggle.com/datasets/surajjha101/stores-area-and-sales-data
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https://www.kaggle.com/datasets/surajjha101/forbes-billionaires-data-preprocessed
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Датасет для 4 лабы: https://www.kaggle.com/datasets/henryshan/2023-data-scientists-salary?resource=download
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lab_10/laba10.ipynb
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lab_10/laba10.ipynb
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lab_11/laba11.ipynb
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lab_11/laba11.ipynb
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lab_5/laba5.ipynb
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lab_6/laba6.ipynb
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lab_6/test_data.csv
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lab_6/test_data.csv
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work_year,experience_level,employment_type,job_title,salary,salary_currency,salary_in_usd,employee_residence,remote_ratio,company_location,company_size,experience_level_encoded,employee_residence_encoded
|
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2022,SE,FT,Machine Learning Software Engineer,168000,USD,168000,CA,100,CA,M,3,6
|
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2023,SE,FT,Data Analyst,179975,USD,179975,US,100,US,M,3,11
|
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2022,SE,FT,Data Scientist,144000,USD,144000,US,100,US,M,3,11
|
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2023,SE,FT,Applied Scientist,222200,USD,222200,US,0,US,L,3,11
|
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2021,EX,FT,Head of Data,230000,USD,230000,RU,50,RU,L,1,39
|
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2022,EN,FT,Applied Data Scientist,40000,USD,40000,AU,100,PK,M,0,5
|
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2023,SE,FT,Data Scientist,105000,USD,105000,US,0,US,M,3,11
|
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2023,EN,FT,Data Analyst,100000,USD,100000,US,50,US,M,0,11
|
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2021,EN,FT,Data Scientist,2200000,INR,29751,IN,50,IN,L,0,47
|
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2023,SE,FT,Machine Learning Engineer,153090,USD,153090,US,0,US,M,3,11
|
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2022,MI,FT,ETL Developer,50000,EUR,52533,GR,0,GR,M,2,24
|
||||
2022,SE,FT,Data Engineer,115000,USD,115000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,128000,USD,128000,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Scientist,2400000,INR,30523,IN,100,IN,L,2,47
|
||||
2023,SE,FT,Data Analyst,128500,USD,128500,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Architect,92000,USD,92000,GB,100,GB,M,3,8
|
||||
2022,EN,FT,Data Engineer,160000,USD,160000,US,0,US,M,0,11
|
||||
2022,SE,FT,Data Scientist,168000,USD,168000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,210000,USD,210000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,153400,USD,153400,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Engineer,140000,USD,140000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Analyst,99050,USD,99050,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,259000,USD,259000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,200000,USD,200000,PR,100,PR,M,3,33
|
||||
2023,SE,FT,Data Scientist,134236,USD,134236,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Engineer,170000,USD,170000,US,100,US,M,2,11
|
||||
2023,SE,FT,Computer Vision Engineer,200000,USD,200000,US,100,US,S,3,11
|
||||
2023,SE,FT,Data Analyst,120250,USD,120250,US,100,US,M,3,11
|
||||
2021,SE,FT,Data Science Manager,4000000,INR,54094,IN,50,US,L,3,47
|
||||
2022,MI,FT,Data Scientist,145000,USD,145000,US,0,US,M,2,11
|
||||
2023,SE,FT,Analytics Engineer,143860,USD,143860,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,138000,USD,138000,US,100,US,M,3,11
|
||||
2022,SE,FT,Machine Learning Scientist,216000,USD,216000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,61300,USD,61300,CA,100,CA,M,3,6
|
||||
2020,MI,FT,Data Scientist,37000,EUR,42197,FR,50,FR,S,2,10
|
||||
2022,MI,FT,Data Analytics Manager,140000,USD,140000,US,0,US,M,2,11
|
||||
2021,EN,FT,Power BI Developer,400000,INR,5409,IN,50,IN,L,0,47
|
||||
2020,EN,FT,Data Analyst,10000,USD,10000,NG,100,NG,S,0,36
|
||||
2023,SE,FT,Data Analyst,180180,USD,180180,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,188100,USD,188100,US,0,US,M,3,11
|
||||
2022,EX,FT,Analytics Engineer,150000,USD,150000,US,100,US,M,1,11
|
||||
2021,EN,FT,Research Scientist,100000,USD,100000,JE,0,CN,L,0,48
|
||||
2023,MI,FT,Applied Machine Learning Engineer,130000,USD,130000,US,0,US,M,2,11
|
||||
2022,SE,FT,Big Data Engineer,210000,CAD,161311,CA,50,CA,M,3,6
|
||||
2023,SE,FT,Data Engineer,205600,USD,205600,US,0,US,L,3,11
|
||||
2023,SE,FT,Data Analyst,140000,USD,140000,US,0,US,M,3,11
|
||||
2022,SE,FT,Machine Learning Developer,100000,CAD,76814,CA,100,CA,M,3,6
|
||||
2022,SE,FT,Data Engineer,102100,USD,102100,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,83500,USD,83500,US,100,US,M,2,11
|
||||
2022,SE,FT,Machine Learning Engineer,65000,EUR,68293,IE,100,IE,S,3,9
|
||||
2020,MI,FT,Product Data Analyst,450000,INR,6072,IN,100,IN,L,2,47
|
||||
2022,EN,FT,AI Scientist,50000,USD,50000,US,100,US,M,0,11
|
||||
2023,SE,FT,Data Scientist,370000,USD,370000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,99450,USD,99450,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,257000,USD,257000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,160000,USD,160000,US,0,US,L,3,11
|
||||
2022,MI,FT,Data Analyst,109000,USD,109000,US,0,US,M,2,11
|
||||
2023,SE,FT,Data Scientist,155000,USD,155000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,230000,USD,230000,US,100,US,M,3,11
|
||||
2023,EX,FT,Head of Data Science,131899,GBP,160288,GB,0,GB,M,1,8
|
||||
2023,SE,FT,Data Engineer,90700,USD,90700,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Analyst,150000,USD,150000,US,100,US,M,2,11
|
||||
2023,SE,FT,Data Analyst,38000,EUR,40777,ES,0,ES,M,3,12
|
||||
2023,SE,FT,Data Scientist,165000,USD,165000,US,0,US,M,3,11
|
||||
2021,MI,FT,Data Architect,180000,USD,180000,US,100,US,L,2,11
|
||||
2021,SE,FT,Data Scientist,4000000,INR,54094,IN,100,IN,L,3,47
|
||||
2023,MI,FT,Data Engineer,175000,USD,175000,US,0,US,M,2,11
|
||||
2023,MI,FT,MLOps Engineer,134000,USD,134000,US,100,US,M,2,11
|
||||
2023,MI,FT,Data Scientist,180000,USD,180000,US,100,US,M,2,11
|
||||
2022,SE,FT,Data Scientist,140000,USD,140000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,205300,USD,205300,US,0,US,L,3,11
|
||||
2023,MI,FT,Data Analyst,75000,USD,75000,US,0,US,M,2,11
|
||||
2023,EN,FT,Applied Scientist,130760,USD,130760,US,0,US,L,0,11
|
||||
2022,MI,FT,Data Engineer,105000,USD,105000,US,0,US,M,2,11
|
||||
2022,MI,FT,ML Engineer,98200,USD,98200,US,100,US,L,2,11
|
||||
2023,MI,FT,Machine Learning Engineer,280700,USD,280700,US,100,US,M,2,11
|
||||
2023,SE,FT,Data Scientist,195000,USD,195000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Science Consultant,128000,USD,128000,US,0,US,M,3,11
|
||||
2023,MI,FT,Research Engineer,230000,USD,230000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Operations Analyst,92250,USD,92250,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Engineer,55000,GBP,67723,GB,100,GB,M,2,8
|
||||
2022,SE,FT,Data Scientist,140000,USD,140000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,230000,USD,230000,US,0,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,120000,USD,120000,US,100,US,M,3,11
|
||||
2022,MI,FT,Data Scientist Lead,85000,EUR,89306,AT,50,AT,L,2,13
|
||||
2021,SE,FT,Data Scientist,65720,EUR,77684,FR,50,FR,M,3,10
|
||||
2022,SE,FT,Analytics Engineer,130000,USD,130000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,220000,USD,220000,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Scientist,115000,CHF,120402,CH,0,CH,L,2,4
|
||||
2021,MI,FT,Data Science Lead,150000,USD,150000,US,100,US,M,2,11
|
||||
2023,MI,FT,Machine Learning Engineer,100000,GBP,121523,GB,0,GB,M,2,8
|
||||
2021,SE,FT,Data Engineer,153000,USD,153000,US,100,US,L,3,11
|
||||
2022,SE,FT,Data Scientist,130000,USD,130000,US,100,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,65000,USD,65000,US,100,US,M,2,11
|
||||
2022,SE,FT,Analytics Engineer,170000,USD,170000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,115000,USD,115000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,170000,USD,170000,US,0,US,M,3,11
|
||||
2023,EN,FT,Data Engineer,92700,USD,92700,US,100,US,M,0,11
|
||||
2023,SE,FT,Machine Learning Engineer,304000,USD,304000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,170000,USD,170000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,145000,USD,145000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,185800,USD,185800,CA,100,CA,M,3,6
|
||||
2023,SE,FT,Data Scientist,110000,USD,110000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,247500,USD,247500,US,0,US,M,3,11
|
||||
2022,EN,PT,BI Analyst,12000,USD,12000,MX,100,US,L,0,32
|
||||
2023,MI,FT,Data Science Manager,104500,USD,104500,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Scientist,80000,GBP,98506,GB,0,GB,M,3,8
|
||||
2022,SE,FT,Data Architect,195400,USD,195400,US,100,US,L,3,11
|
||||
2023,EN,FT,Data Engineer,90000,USD,90000,US,0,US,M,0,11
|
||||
2022,SE,FT,Data Scientist,123400,USD,123400,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Analytics Engineer,135000,USD,135000,US,100,US,L,2,11
|
||||
2023,SE,FT,Data Analyst,165000,USD,165000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,148500,USD,148500,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,252000,USD,252000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,106020,USD,106020,US,0,US,M,3,11
|
||||
2020,EN,FT,Data Scientist,105000,USD,105000,US,100,US,S,0,11
|
||||
2022,MI,FT,Data Engineer,65000,USD,65000,US,0,US,M,2,11
|
||||
2022,MI,FT,Data Scientist,52000,EUR,54634,NL,100,NL,S,2,3
|
||||
2022,SE,FT,Data Engineer,200000,USD,200000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,136000,USD,136000,US,100,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,90000,GBP,109371,HR,0,HR,M,2,20
|
||||
2022,SE,FT,Data Engineer,205000,USD,205000,US,100,US,M,3,11
|
||||
2022,MI,FT,Data Analyst,100000,USD,100000,US,0,US,M,2,11
|
||||
2023,SE,FT,Data Analyst,95000,USD,95000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,80000,USD,80000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,247300,USD,247300,US,0,US,M,3,11
|
||||
2022,SE,FT,Machine Learning Engineer,200000,USD,200000,US,100,US,L,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,220000,USD,220000,US,0,US,M,3,11
|
||||
2023,MI,FT,Machine Learning Engineer,38000,GBP,46178,GB,100,GB,M,2,8
|
||||
2023,SE,FT,Machine Learning Engineer,247500,USD,247500,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,183500,USD,183500,US,100,US,M,3,11
|
||||
2023,EX,FT,Head of Data,269600,USD,269600,US,0,US,M,1,11
|
||||
2020,MI,FT,Data Scientist,55000,EUR,62726,FR,50,LU,S,2,10
|
||||
2021,SE,FT,Machine Learning Infrastructure Engineer,195000,USD,195000,US,100,US,M,3,11
|
||||
2023,MI,FT,Research Scientist,185000,USD,185000,US,100,US,M,2,11
|
||||
2022,SE,FT,Data Scientist,198440,USD,198440,US,100,US,M,3,11
|
||||
2021,MI,FT,Data Engineer,24000,EUR,28369,MT,50,MT,L,2,48
|
||||
2022,SE,FT,Data Engineer,63000,USD,63000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,130000,USD,130000,US,0,US,M,3,11
|
||||
2023,SE,FT,Analytics Engineer,173000,USD,173000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,208450,USD,208450,US,100,US,M,3,11
|
||||
2022,EX,FT,Data Engineer,116100,USD,116100,US,100,US,M,1,11
|
||||
2023,SE,FT,Applied Scientist,230000,USD,230000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,205300,USD,205300,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,169000,USD,169000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,185900,USD,185900,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,260000,USD,260000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,218500,USD,218500,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,99000,USD,99000,US,0,US,M,3,11
|
||||
2023,SE,FT,Business Intelligence Engineer,156400,USD,156400,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,150000,USD,150000,US,0,US,M,2,11
|
||||
2021,SE,FT,Data Specialist,165000,USD,165000,US,100,US,L,3,11
|
||||
2020,MI,FT,Data Scientist,60000,GBP,76958,GB,100,GB,S,2,8
|
||||
2023,SE,FT,Data Analyst,102500,USD,102500,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,115000,USD,115000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,200000,USD,200000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,120000,USD,120000,US,0,US,M,3,11
|
||||
2022,MI,FT,Machine Learning Scientist,112300,USD,112300,US,100,US,L,2,11
|
||||
2023,SE,FT,Data Engineer,138750,USD,138750,US,100,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,153400,USD,153400,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Analyst,136000,USD,136000,US,100,US,M,2,11
|
||||
2023,SE,FT,Data Engineer,66000,USD,66000,US,100,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,60000,GBP,72914,HR,0,HR,M,2,20
|
||||
2022,MI,FT,Data Analyst,450000,INR,5723,IN,100,IN,S,2,47
|
||||
2023,EN,FT,Data Analyst,55000,USD,55000,US,0,US,M,0,11
|
||||
2022,EX,FT,Data Engineer,260000,USD,260000,US,100,US,M,1,11
|
||||
2023,SE,FT,Data Scientist,239748,USD,239748,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,220000,USD,220000,US,100,US,M,3,11
|
||||
2022,EN,FT,Computer Vision Software Engineer,150000,USD,150000,AU,100,AU,S,0,5
|
||||
2022,SE,FT,Data Science Manager,299500,USD,299500,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,116250,USD,116250,US,100,US,M,3,11
|
||||
2023,MI,FT,Machine Learning Engineer,135000,USD,135000,US,50,US,L,2,11
|
||||
2023,MI,FT,ETL Engineer,70000,GBP,85066,GB,100,GB,M,2,8
|
||||
2023,SE,FT,Data Engineer,137500,USD,137500,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,130000,USD,130000,CA,100,CA,M,3,6
|
||||
2022,MI,FT,Data Analyst,150000,USD,150000,US,0,US,M,2,11
|
||||
2023,SE,FT,Data Scientist,258000,USD,258000,CA,0,CA,M,3,6
|
||||
2021,MI,FL,Autonomous Vehicle Technician,45555,USD,45555,AS,50,BS,M,2,48
|
||||
2023,SE,FT,Machine Learning Engineer,163800,USD,163800,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,185900,USD,185900,US,0,US,M,3,11
|
||||
2022,MI,FT,Analytics Engineer,122500,USD,122500,US,100,US,M,2,11
|
||||
2022,EN,FT,BI Data Analyst,633000,INR,8050,IN,100,IN,M,0,47
|
||||
2023,SE,FT,Data Manager,120000,USD,120000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,65000,EUR,68293,ES,0,ES,M,3,12
|
||||
2023,SE,FT,Computer Vision Engineer,342810,USD,342810,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,150000,USD,150000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Science Manager,245100,USD,245100,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,190000,USD,190000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,140000,USD,140000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,106000,USD,106000,US,0,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,142200,USD,142200,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Engineer,118000,USD,118000,US,100,US,M,2,11
|
||||
2023,SE,FT,Research Engineer,102544,USD,102544,US,0,US,M,3,11
|
||||
2023,EX,FT,Director of Data Science,353200,USD,353200,US,0,US,M,1,11
|
||||
2022,SE,FT,Data Engineer,185900,USD,185900,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,94000,USD,94000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Architect,192564,USD,192564,US,100,US,M,3,11
|
||||
2022,SE,FT,Machine Learning Engineer,167100,USD,167100,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Analyst,160000,USD,160000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Engineer,84958,GBP,104611,GB,100,GB,M,3,8
|
||||
2022,SE,FT,Data Scientist,225000,USD,225000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,207000,USD,207000,US,100,US,M,3,11
|
||||
2022,EN,FT,Financial Data Analyst,100000,USD,100000,US,50,US,L,0,11
|
||||
2023,SE,FT,Data Infrastructure Engineer,143000,USD,143000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,126000,USD,126000,US,0,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,176100,USD,176100,US,0,US,L,3,11
|
||||
2023,SE,FT,Data Scientist,155000,USD,155000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,115000,USD,115000,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Scientist,115360,USD,115360,US,100,US,M,2,11
|
||||
2023,SE,FT,Machine Learning Scientist,220000,USD,220000,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Engineer,175000,USD,175000,US,100,US,M,2,11
|
||||
2021,EN,FT,Data Analyst,60000,USD,60000,US,100,US,S,0,11
|
||||
2022,MI,FT,Data Scientist,120000,USD,120000,US,100,US,M,2,11
|
||||
2023,MI,FT,Data Analyst,121500,USD,121500,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Analyst,95000,USD,95000,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Engineer,140000,USD,140000,US,100,US,M,2,11
|
||||
2023,SE,FT,Data Engineer,147000,USD,147000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,241871,USD,241871,US,0,US,M,3,11
|
||||
2020,EN,FT,Data Engineer,4450000,JPY,41689,JP,100,JP,S,0,41
|
||||
2022,SE,FT,Data Scientist,186000,USD,186000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,129000,USD,129000,US,0,US,M,3,11
|
||||
2022,MI,FT,Analytics Engineer,60000,GBP,73880,GB,0,GB,M,2,8
|
||||
2022,SE,FT,Machine Learning Engineer,210000,USD,210000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,152000,USD,152000,US,0,US,M,3,11
|
||||
2023,EN,FT,Data Analyst,55000,CAD,40663,CA,0,CA,L,0,6
|
||||
2022,EN,FT,Data Scientist,49500,EUR,52008,BE,50,BE,S,0,15
|
||||
2022,SE,FT,Data Scientist,100000,USD,100000,BR,100,US,M,3,28
|
||||
2022,MI,FT,Data Analyst,100000,USD,100000,US,0,US,M,2,11
|
||||
2023,SE,FT,Data Analyst,385000,USD,385000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Science Engineer,100000,USD,100000,US,0,US,M,3,11
|
||||
2023,SE,FT,Analytics Engineer,120000,USD,120000,US,100,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,100000,USD,100000,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Analyst,79000,USD,79000,US,0,US,M,2,11
|
||||
2023,SE,FT,Data Engineer,163800,USD,163800,US,0,US,M,3,11
|
||||
2022,EX,FT,Data Manager,164000,CAD,125976,CA,50,CA,L,1,6
|
||||
2023,SE,FT,Data Engineer,133300,USD,133300,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,70000,USD,70000,US,0,US,M,3,11
|
||||
2021,EN,FT,Machine Learning Engineer,85000,USD,85000,NL,100,DE,S,0,3
|
||||
2023,SE,FT,Data Engineer,160000,USD,160000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,131000,USD,131000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,129400,USD,129400,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,100000,USD,100000,US,100,US,M,3,11
|
||||
2022,SE,FT,Analytics Engineer,170000,USD,170000,US,100,US,M,3,11
|
||||
2022,MI,FT,Data Analyst,150000,USD,150000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Scientist,156600,USD,156600,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,169000,USD,169000,US,0,US,M,3,11
|
||||
2023,SE,FT,Analytics Engineer,143860,USD,143860,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,169000,USD,169000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,144000,USD,144000,US,0,US,M,3,11
|
||||
2020,MI,FT,Data Engineer,106000,USD,106000,US,100,US,L,2,11
|
||||
2022,SE,FT,Data Engineer,170000,USD,170000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,100000,USD,100000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,65000,USD,65000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,113750,USD,113750,IE,0,IE,M,3,9
|
||||
2023,SE,FT,Data Scientist,183500,USD,183500,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,80000,USD,80000,US,0,US,L,3,11
|
||||
2022,SE,FT,Data Scientist,185000,USD,185000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,45000,EUR,48289,ES,0,ES,M,3,12
|
||||
2022,SE,FT,Data Scientist,168000,USD,168000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,185900,USD,185900,US,0,US,M,3,11
|
||||
2023,EX,FT,Data Scientist,100000,USD,100000,US,0,US,M,1,11
|
||||
2023,SE,FT,Analytics Engineer,207000,USD,207000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,180560,USD,180560,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,125000,USD,125000,US,0,US,M,3,11
|
||||
2022,SE,FT,ML Engineer,84000,USD,84000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,75000,USD,75000,US,100,US,M,3,11
|
||||
2022,MI,FT,Analytics Engineer,85000,GBP,104663,GB,0,GB,M,2,8
|
||||
2022,SE,FT,Data Engineer,150000,USD,150000,US,100,US,M,3,11
|
||||
2021,EN,FT,3D Computer Vision Researcher,20000,USD,20000,AS,0,AS,M,0,48
|
||||
2023,SE,FT,Data Scientist,154000,USD,154000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,95000,USD,95000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,175000,USD,175000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,250000,USD,250000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,165000,USD,165000,US,100,US,M,3,11
|
||||
2020,EX,FT,Data Engineer,70000,EUR,79833,ES,50,ES,L,1,12
|
||||
2023,SE,FT,Machine Learning Engineer,323300,USD,323300,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,135000,USD,135000,PR,100,PR,M,3,33
|
||||
2023,SE,FT,Data Analyst,187000,USD,187000,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Analyst,160000,USD,160000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Scientist,122600,USD,122600,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,48000,EUR,51508,ES,0,ES,M,3,12
|
||||
2023,SE,FT,Data Engineer,221484,USD,221484,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Science Manager,137141,USD,137141,US,100,US,M,3,11
|
||||
2020,EX,FT,Staff Data Analyst,15000,USD,15000,NG,0,CA,M,1,36
|
||||
2023,SE,FT,Machine Learning Engineer,135000,USD,135000,US,0,US,M,3,11
|
||||
2023,SE,FT,Deep Learning Researcher,115000,EUR,123405,DE,0,DE,L,3,7
|
||||
2023,SE,FT,Machine Learning Engineer,200000,USD,200000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Architect,115000,USD,115000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,138784,USD,138784,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,141525,USD,141525,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,127000,USD,127000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,240500,USD,240500,US,0,US,L,3,11
|
||||
2022,MI,FT,Data Scientist,47000,GBP,57872,GB,50,GB,M,2,8
|
||||
2022,SE,FT,Data Analyst,50000,GBP,61566,GB,0,GB,M,3,8
|
||||
2022,SE,FT,Product Data Scientist,8000,USD,8000,IN,100,SG,L,3,47
|
||||
2023,SE,FT,Machine Learning Engineer,128280,USD,128280,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,185900,USD,185900,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,160000,USD,160000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,138900,USD,138900,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,165220,USD,165220,US,100,US,M,3,11
|
||||
2022,MI,FT,Machine Learning Researcher,100000,USD,100000,US,100,US,M,2,11
|
||||
2023,SE,FT,Data Scientist,225000,USD,225000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,153000,USD,153000,CA,100,CA,M,3,6
|
||||
2023,SE,FT,Data Scientist,297300,USD,297300,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,200000,USD,200000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M,3,11
|
||||
2022,SE,FT,ETL Developer,63000,USD,63000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,90700,USD,90700,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,178500,USD,178500,US,100,US,M,3,11
|
||||
2023,EN,FT,Data Engineer,65000,USD,65000,US,0,US,M,0,11
|
||||
2022,MI,FT,Data Scientist,55000,GBP,67723,GB,0,GB,M,2,8
|
||||
2023,SE,FT,Analytics Engineer,87000,USD,87000,US,0,US,M,3,11
|
||||
2023,MI,FT,Machine Learning Engineer,175000,USD,175000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Engineer,100000,USD,100000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Science Consultant,139000,USD,139000,US,0,US,M,3,11
|
||||
2023,SE,FT,Research Scientist,110000,USD,110000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,226700,USD,226700,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,150000,USD,150000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,128000,USD,128000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,25000,USD,25000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,107250,USD,107250,US,100,US,M,3,11
|
||||
2022,SE,FT,Machine Learning Engineer,131300,USD,131300,US,100,US,L,3,11
|
||||
2023,EN,FT,Data Scientist,110000,USD,110000,US,50,US,S,0,11
|
||||
2023,SE,FT,ML Engineer,203500,USD,203500,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,150000,USD,150000,US,0,US,M,3,11
|
||||
2021,MI,FT,Data Scientist,76760,EUR,90734,DE,50,DE,L,2,7
|
||||
2022,SE,FT,Data Engineer,170000,USD,170000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,129000,USD,129000,US,0,US,M,3,11
|
||||
2022,SE,FT,Analytics Engineer,122500,USD,122500,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Architect,113900,USD,113900,US,100,US,M,3,11
|
||||
2023,MI,FT,Data Engineer,70000,USD,70000,US,0,US,M,2,11
|
||||
2022,MI,FT,Data Scientist,78000,USD,78000,US,100,US,M,2,11
|
||||
2023,SE,FT,Machine Learning Engineer,204500,USD,204500,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,45000,EUR,48289,ES,0,ES,M,3,12
|
||||
2023,EX,FT,Data Scientist,258750,USD,258750,US,0,US,M,1,11
|
||||
2023,SE,FT,Data Architect,280100,USD,280100,US,100,US,M,3,11
|
||||
2023,MI,FT,Data Architect,167500,USD,167500,US,0,US,M,2,11
|
||||
2022,MI,FT,Data Engineer,70000,EUR,73546,GR,100,GR,M,2,24
|
||||
2022,SE,FT,Machine Learning Engineer,255000,USD,255000,MX,100,MX,M,3,32
|
||||
2022,SE,FT,Data Engineer,115000,USD,115000,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Scientist,110000,EUR,115573,NL,0,NL,M,2,3
|
||||
2021,MI,FT,Data Engineer,60000,GBP,82528,GB,100,GB,L,2,8
|
||||
2022,MI,FT,Data Scientist,90000,GBP,110820,GB,0,GB,M,2,8
|
||||
2023,SE,FT,Data Analyst,106020,USD,106020,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Analyst,40000,EUR,42026,ES,100,ES,M,2,12
|
||||
2022,EN,FT,Data Analyst,500000,INR,6359,FR,100,IN,L,0,10
|
||||
2022,MI,FT,Data Scientist,2500000,INR,31795,IN,100,US,M,2,47
|
||||
2023,MI,FT,Data Engineer,120000,USD,120000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Scientist,127000,USD,127000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,45000,EUR,47280,ES,0,ES,M,3,12
|
||||
2023,SE,FT,Analytics Engineer,214200,USD,214200,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,210000,USD,210000,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Science Manager,241000,USD,241000,US,100,US,M,2,11
|
||||
2023,SE,FT,Data Engineer,109000,USD,109000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,230000,USD,230000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,40000,EUR,42026,PT,0,PT,M,3,40
|
||||
2022,SE,FT,AI Scientist,125000,USD,125000,CO,100,CO,L,3,31
|
||||
2023,EX,FT,Data Engineer,194500,USD,194500,US,0,US,M,1,11
|
||||
2022,SE,FT,Data Analyst,110925,USD,110925,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,270703,USD,270703,US,0,US,M,3,11
|
||||
2022,MI,FT,Applied Machine Learning Scientist,173000,USD,173000,US,50,US,M,2,11
|
||||
2023,MI,FT,Machine Learning Engineer,150000,USD,150000,US,100,US,M,2,11
|
||||
2022,SE,FT,Data Scientist,138600,USD,138600,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,175000,USD,175000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,45000,EUR,47280,ES,0,ES,M,3,12
|
||||
2023,MI,FT,Research Engineer,120000,USD,120000,US,100,US,M,2,11
|
||||
2023,SE,FT,Data Strategist,72000,USD,72000,CA,0,CA,M,3,6
|
||||
2023,SE,FT,Data Analyst,153600,USD,153600,US,0,US,M,3,11
|
||||
2023,EX,FT,Data Engineer,220000,USD,220000,US,0,US,M,1,11
|
||||
2023,SE,FT,Machine Learning Engineer,220000,USD,220000,US,0,US,M,3,11
|
||||
2023,SE,FT,Research Engineer,80000,EUR,85847,DE,100,DE,S,3,7
|
||||
2022,MI,FL,Applied Machine Learning Scientist,2400000,INR,30523,IN,100,IN,S,2,47
|
||||
2023,SE,FT,Data Scientist,110500,USD,110500,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Science Consultant,145000,USD,145000,US,0,US,M,3,11
|
||||
2023,MI,FT,Research Scientist,84570,GBP,102772,GB,0,GB,M,2,8
|
||||
2023,SE,FT,Machine Learning Engineer,109400,USD,109400,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,175308,USD,175308,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,170000,USD,170000,US,0,US,M,3,11
|
||||
2023,MI,FT,Applied Data Scientist,1700000,INR,20670,IN,100,IN,L,2,47
|
||||
2023,SE,FT,Data Engineer,129000,USD,129000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,198800,USD,198800,US,0,US,M,3,11
|
||||
2022,EN,FT,Data Analyst,46000,USD,46000,US,100,US,L,0,11
|
||||
2023,SE,FT,Data Scientist,228000,USD,228000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,95000,USD,95000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,141525,USD,141525,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,236900,USD,236900,US,100,US,L,3,11
|
||||
2022,MI,FT,Data Engineer,75000,GBP,92350,GB,100,GB,M,2,8
|
||||
2023,SE,FT,Data Engineer,129300,USD,129300,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,135000,USD,135000,US,0,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,150000,USD,150000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,85500,USD,85500,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,206699,USD,206699,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,163800,USD,163800,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Architect,213120,USD,213120,US,100,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Software Engineer,170000,USD,170000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,63000,USD,63000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,125600,USD,125600,US,100,US,M,3,11
|
||||
2022,EN,PT,Data Scientist,110000,USD,110000,DO,100,FR,M,0,48
|
||||
2022,EN,FT,Data Scientist,80000,EUR,84053,BE,100,BE,L,0,15
|
||||
2023,EN,FT,Data Scientist,1060000,INR,12888,IN,50,IN,S,0,47
|
||||
2023,SE,FT,Data Analyst,48000,EUR,51508,ES,0,ES,M,3,12
|
||||
2022,EN,FT,Machine Learning Developer,40000,USD,40000,PK,100,AU,M,0,48
|
||||
2022,SE,FT,Analytics Engineer,193750,USD,193750,US,100,US,M,3,11
|
||||
2023,EN,FT,Data Engineer,115100,USD,115100,US,0,US,M,0,11
|
||||
2023,SE,FT,Data Scientist,190000,USD,190000,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,79000,USD,79000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Analyst,99000,USD,99000,US,0,US,M,3,11
|
||||
2021,EN,FT,Machine Learning Developer,15000,USD,15000,TH,100,TH,L,0,44
|
||||
2023,SE,FT,Data Engineer,135000,USD,135000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Strategist,90000,USD,90000,CA,0,CA,M,3,6
|
||||
2023,SE,FT,Machine Learning Engineer,180000,USD,180000,US,100,US,M,3,11
|
||||
2023,EX,FT,Data Engineer,116704,USD,116704,US,100,US,M,1,11
|
||||
2022,SE,FT,Data Scientist,190000,USD,190000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,105000,USD,105000,US,100,US,M,3,11
|
||||
2022,EN,FT,Data Engineer,129000,USD,129000,US,100,US,L,0,11
|
||||
2022,MI,FT,Business Data Analyst,150000,USD,150000,US,100,US,L,2,11
|
||||
2022,SE,FT,Data Engineer,250000,USD,250000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Science Manager,243225,USD,243225,US,100,US,M,3,11
|
||||
2022,MI,FT,Data Engineer,75000,USD,75000,US,100,US,M,2,11
|
||||
2022,SE,FT,Lead Data Scientist,28500,EUR,29944,PT,50,PT,S,3,40
|
||||
2022,SE,FT,Data Scientist,120000,USD,120000,US,100,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,181000,USD,181000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,35000,EUR,36773,ES,0,ES,M,3,12
|
||||
2022,SE,FT,Data Scientist,152000,USD,152000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,129300,USD,129300,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,100000,USD,100000,US,0,US,M,2,11
|
||||
2023,SE,FT,Data Engineer,120000,USD,120000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,70500,USD,70500,US,0,US,M,3,11
|
||||
2021,SE,FT,Data Scientist,130000,CAD,103691,CA,100,CA,L,3,6
|
||||
2023,SE,FT,Data Engineer,145000,USD,145000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,153600,USD,153600,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,136000,USD,136000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,160000,USD,160000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,145000,USD,145000,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Scientist,127500,USD,127500,US,100,US,M,2,11
|
||||
2023,MI,FT,Data Scientist,510000,HKD,65062,HK,0,HK,L,2,48
|
||||
2023,SE,FT,Machine Learning Engineer,139500,USD,139500,US,0,US,M,3,11
|
||||
2023,SE,FT,Applied Scientist,136000,USD,136000,US,0,US,L,3,11
|
||||
2020,EN,FT,Data Scientist,43200,EUR,49268,DE,0,DE,S,0,7
|
||||
2022,SE,FT,Machine Learning Engineer,204500,USD,204500,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,129300,USD,129300,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,215000,USD,215000,US,100,US,M,3,11
|
||||
2022,EN,FT,AI Programmer,40000,USD,40000,PK,100,AU,M,0,48
|
||||
2023,SE,FT,Data Scientist,134000,USD,134000,US,0,US,M,3,11
|
||||
2022,SE,FT,BI Developer,120000,USD,120000,US,100,US,M,3,11
|
||||
2023,EX,FT,Data Engineer,235000,USD,235000,US,0,US,M,1,11
|
||||
2023,SE,FT,Data Analyst,110000,USD,110000,US,100,US,S,3,11
|
||||
2023,MI,FT,Research Scientist,185000,USD,185000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Analyst,90320,USD,90320,US,0,US,M,3,11
|
||||
2022,EN,FT,Data Scientist,82000,USD,82000,US,0,US,L,0,11
|
||||
2023,SE,FT,Data Analyst,130000,USD,130000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,105200,USD,105200,US,0,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,122700,USD,122700,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Engineer,120000,USD,120000,US,100,US,M,2,11
|
||||
2022,SE,FT,Data Engineer,132320,USD,132320,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,130000,USD,130000,US,100,US,M,3,11
|
||||
2023,MI,FT,Data Engineer,55000,EUR,59020,ES,100,ES,M,2,12
|
||||
2020,MI,FT,Data Engineer,130800,USD,130800,ES,100,US,M,2,12
|
||||
2022,MI,FT,Data Engineer,70000,GBP,86193,GB,100,GB,M,2,8
|
||||
2021,SE,FT,Machine Learning Scientist,225000,USD,225000,US,100,CA,L,3,11
|
||||
2023,MI,FT,Deep Learning Engineer,150000,USD,150000,US,100,US,M,2,11
|
||||
2023,SE,FT,Data Engineer,130000,USD,130000,US,0,US,M,3,11
|
||||
2022,SE,FT,ETL Developer,250000,USD,250000,US,100,US,M,3,11
|
||||
2022,EN,FT,Data Engineer,85000,USD,85000,US,0,US,M,0,11
|
||||
2023,SE,FT,Data Analyst,70000,USD,70000,US,0,US,M,3,11
|
||||
2023,EN,FT,BI Developer,160000,USD,160000,US,0,US,M,0,11
|
||||
2022,SE,FT,Data Architect,63000,USD,63000,US,0,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,184700,USD,184700,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Engineer,90000,GBP,110820,GB,0,GB,M,2,8
|
||||
2022,MI,FT,Data Analyst,80000,USD,80000,US,100,US,L,2,11
|
||||
2021,SE,FT,Research Scientist,50000,USD,50000,FR,100,US,S,3,10
|
||||
2022,SE,FT,Data Engineer,190000,USD,190000,US,100,US,M,3,11
|
||||
2022,SE,FT,Research Scientist,85000,EUR,89306,FR,50,FR,L,3,10
|
||||
2022,MI,FT,Research Scientist,80000,EUR,84053,DE,0,DE,S,2,7
|
||||
2023,MI,FT,Data Engineer,115092,USD,115092,US,0,US,M,2,11
|
||||
2023,EX,FT,Data Architect,180000,USD,180000,US,0,US,M,1,11
|
||||
2023,EX,FT,Data Engineer,115000,USD,115000,US,0,US,M,1,11
|
||||
2022,EN,FT,Data Manager,45600,USD,45600,US,100,US,M,0,11
|
||||
2023,SE,FT,Machine Learning Engineer,142200,USD,142200,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,170000,USD,170000,US,100,US,M,3,11
|
||||
2022,SE,FT,ML Engineer,160000,USD,160000,US,0,US,M,3,11
|
||||
2023,EN,FT,Data Analyst,150000,USD,150000,US,0,US,M,0,11
|
||||
2022,MI,FT,Data Analyst,40000,GBP,49253,GB,100,GB,M,2,8
|
||||
2021,EN,FT,Data Analyst,80000,USD,80000,US,100,US,M,0,11
|
||||
2023,MI,FT,Data Engineer,70000,GBP,85066,GB,100,GB,M,2,8
|
||||
2022,SE,FT,Data Engineer,120000,USD,120000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,130000,USD,130000,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Specialist,165000,USD,165000,US,0,US,M,2,11
|
||||
2023,SE,FT,Applied Scientist,136000,USD,136000,US,0,US,L,3,11
|
||||
2022,EN,FT,Business Data Analyst,50000,USD,50000,IN,100,AS,L,0,47
|
||||
2023,SE,FT,Data Scientist,176000,USD,176000,CA,0,CA,M,3,6
|
||||
2022,SE,FT,Analytics Engineer,205300,USD,205300,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,220000,USD,220000,US,100,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,38000,GBP,46178,GB,0,GB,M,2,8
|
||||
2022,SE,FT,Data Analyst,110000,USD,110000,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Engineer,154000,USD,154000,US,0,US,M,2,11
|
||||
2023,MI,FT,Data Engineer,149600,USD,149600,US,0,US,M,2,11
|
||||
2023,SE,FT,Analytics Engineer,143200,USD,143200,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,206000,USD,206000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Engineer,131300,USD,131300,US,100,US,L,3,11
|
||||
2022,MI,FT,Data Engineer,70000,USD,70000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Scientist,51000,USD,51000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,65000,USD,65000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Specialist,70000,USD,70000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Architect,250000,USD,250000,US,0,US,M,3,11
|
||||
2022,SE,FT,Cloud Database Engineer,160000,USD,160000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,203500,USD,203500,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Science Consultant,94500,USD,94500,US,0,US,M,3,11
|
||||
2023,EN,FT,AI Programmer,70000,USD,70000,IN,0,AU,L,0,47
|
||||
2022,SE,FT,Data Science Tech Lead,375000,USD,375000,US,50,US,L,3,11
|
||||
2022,SE,FT,Data Analyst,84900,USD,84900,US,100,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,174000,USD,174000,DE,0,DE,M,3,7
|
||||
2022,SE,FT,Data Scientist,185900,USD,185900,US,0,US,M,3,11
|
||||
2022,MI,FT,Machine Learning Scientist,160000,USD,160000,US,100,US,L,2,11
|
||||
2023,SE,FT,Data Engineer,252000,USD,252000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,140000,USD,140000,US,0,US,M,3,11
|
||||
2022,SE,FT,Analytics Engineer,150000,USD,150000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,48000,EUR,51508,ES,100,ES,M,3,12
|
||||
2023,MI,FT,Data Analyst,130000,USD,130000,CA,100,CA,M,2,6
|
||||
2023,SE,FT,Machine Learning Engineer,106900,USD,106900,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Engineer,161000,USD,161000,US,100,US,M,2,11
|
||||
2022,MI,FT,Data Scientist,130000,USD,130000,US,100,US,M,2,11
|
||||
2022,MI,FT,Data Analyst,100000,USD,100000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Analyst,116150,USD,116150,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Architect,110000,USD,110000,US,100,US,M,3,11
|
||||
2023,EN,FT,Data Engineer,135000,USD,135000,US,0,US,M,0,11
|
||||
2022,MI,FT,Data Analyst,150000,USD,150000,US,0,US,M,2,11
|
||||
2023,SE,FT,Analytics Engineer,197000,USD,197000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,45000,EUR,47280,ES,0,ES,M,3,12
|
||||
2023,EN,FT,BI Developer,100000,USD,100000,US,0,US,M,0,11
|
||||
2020,SE,FT,Data Scientist,120000,USD,120000,US,50,US,L,3,11
|
||||
2023,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M,3,11
|
||||
2023,SE,FT,BI Developer,140000,USD,140000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,129000,USD,129000,US,0,US,M,3,11
|
||||
2023,EN,FT,Autonomous Vehicle Technician,7000,USD,7000,GH,0,GH,S,0,48
|
||||
2022,EN,FT,Data Engineer,135000,USD,135000,US,0,US,M,0,11
|
||||
2021,SE,FT,Data Science Engineer,159500,CAD,127221,CA,50,CA,L,3,6
|
||||
2022,SE,FT,Analytics Engineer,135000,USD,135000,US,0,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,204500,USD,204500,US,0,US,M,3,11
|
||||
2021,SE,FT,Lead Data Engineer,160000,USD,160000,PR,50,US,S,3,33
|
||||
2023,SE,FT,Data Analyst,94000,USD,94000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,135000,USD,135000,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Science Manager,70000,USD,70000,US,0,US,M,2,11
|
||||
2022,SE,FT,Applied Data Scientist,177000,USD,177000,US,100,US,L,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,288000,USD,288000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,129300,USD,129300,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,203000,USD,203000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,L,3,11
|
||||
2022,SE,FT,Research Engineer,149850,USD,149850,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M,3,11
|
||||
2022,MI,FT,Analytics Engineer,54000,USD,54000,US,0,US,M,2,11
|
||||
2022,EN,FT,Data Engineer,40000,GBP,49253,GB,100,GB,M,0,8
|
||||
2023,SE,FT,Machine Learning Infrastructure Engineer,100000,EUR,107309,FR,100,FR,M,3,10
|
||||
2023,SE,FT,Applied Scientist,205000,USD,205000,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Scientist,180000,USD,180000,US,0,US,M,2,11
|
||||
2023,SE,FT,Data Manager,120000,USD,120000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,155000,USD,155000,US,0,US,M,3,11
|
||||
2023,EN,FT,AI Developer,60000,EUR,64385,DE,0,DE,M,0,7
|
||||
2022,SE,FT,Machine Learning Engineer,189650,USD,189650,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Engineer,95000,USD,95000,US,0,US,M,2,11
|
||||
2021,MI,FT,Data Engineer,250000,TRY,28016,TR,100,TR,M,2,30
|
||||
2022,SE,FT,Research Engineer,249500,USD,249500,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,81666,USD,81666,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,95000,USD,95000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,227000,USD,227000,US,0,US,M,3,11
|
||||
2023,SE,FT,Applied Scientist,222200,USD,222200,US,0,US,L,3,11
|
||||
2022,SE,FT,Data Scientist,151800,USD,151800,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,115000,USD,115000,US,0,US,M,3,11
|
||||
2021,MI,FT,Data Scientist,130000,USD,130000,US,50,US,L,2,11
|
||||
2023,SE,FT,Data Analyst,170550,USD,170550,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,185000,USD,185000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,120000,USD,120000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,130000,USD,130000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,275300,USD,275300,US,100,US,M,3,11
|
||||
2023,EX,FT,Director of Data Science,249300,USD,249300,US,0,US,M,1,11
|
||||
2023,MI,FT,Machine Learning Engineer,100000,GBP,121523,GB,0,GB,M,2,8
|
||||
2023,MI,FT,Data Engineer,75000,USD,75000,US,0,US,M,2,11
|
||||
2022,MI,FT,Data Engineer,75000,GBP,92350,GB,100,GB,M,2,8
|
||||
2022,MI,FT,Data Scientist,110000,USD,110000,US,100,US,L,2,11
|
||||
2022,SE,FT,Data Analyst,127000,USD,127000,US,0,US,M,3,11
|
||||
2022,SE,FT,Analytics Engineer,140250,USD,140250,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,300000,USD,300000,US,0,US,M,3,11
|
||||
2023,SE,FL,Machine Learning Researcher,50000,USD,50000,UA,50,UA,S,3,43
|
||||
2023,SE,FT,Data Scientist,160000,USD,160000,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,128000,USD,128000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Scientist,141525,USD,141525,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,36000,EUR,37824,ES,0,ES,M,3,12
|
||||
2022,SE,FT,Data Engineer,216000,USD,216000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Science Manager,245100,USD,245100,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Analyst,85000,USD,85000,US,0,US,M,2,11
|
||||
2023,SE,FT,Data Engineer,166000,USD,166000,US,100,US,M,3,11
|
||||
2022,SE,FT,Machine Learning Engineer,135000,USD,135000,US,0,US,M,3,11
|
||||
2023,SE,FT,Research Engineer,100000,EUR,107309,DE,100,DE,S,3,7
|
||||
2023,SE,FT,Research Engineer,293000,USD,293000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,165000,USD,165000,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Engineer,60000,USD,60000,MX,100,MX,M,2,32
|
||||
2023,SE,FT,Data Manager,140000,USD,140000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,35000,EUR,37558,PT,0,PT,M,3,40
|
||||
2023,SE,FT,Data Analyst,120000,USD,120000,US,0,US,M,3,11
|
||||
2023,EN,FT,Data Engineer,160000,USD,160000,US,0,US,M,0,11
|
||||
2023,MI,FT,Data Scientist,97750,USD,97750,US,100,US,M,2,11
|
||||
2023,SE,FT,Data Engineer,175000,USD,175000,US,100,US,M,3,11
|
||||
2023,SE,FT,BI Analyst,135000,USD,135000,US,0,US,M,3,11
|
||||
2022,SE,FT,Machine Learning Manager,200000,USD,200000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,99100,USD,99100,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,100000,USD,100000,US,0,US,M,2,11
|
||||
2023,SE,FT,BI Developer,135000,USD,135000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,170000,USD,170000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Science Engineer,60000,USD,60000,AR,100,MX,L,3,48
|
||||
2022,SE,FT,Data Scientist,210000,USD,210000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,110000,USD,110000,US,100,US,M,3,11
|
||||
2021,MI,PT,Data Engineer,59000,EUR,69741,NL,100,NL,L,2,3
|
||||
2023,SE,FT,Data Scientist,120250,USD,120250,GB,0,GB,M,3,8
|
||||
2022,SE,FT,Data Engineer,139860,USD,139860,US,0,US,M,3,11
|
||||
2022,EN,FT,Data Science Consultant,26000,EUR,27317,ES,50,ES,L,0,12
|
||||
2023,SE,FT,Data Analyst,81666,USD,81666,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,127075,USD,127075,US,100,US,M,3,11
|
||||
2023,EX,FT,Data Engineer,175000,USD,175000,US,0,US,M,1,11
|
||||
2022,SE,FT,Data Scientist,120000,USD,120000,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Engineer,90000,USD,90000,US,100,US,M,2,11
|
||||
2023,SE,FT,Data Scientist,113000,USD,113000,CA,0,CA,M,3,6
|
||||
2023,SE,FT,Applied Scientist,262500,USD,262500,US,0,US,L,3,11
|
||||
2022,SE,FT,Data Engineer,130000,USD,130000,US,0,US,M,3,11
|
||||
2022,MI,FT,BI Data Analyst,65000,AUD,45050,AU,50,AU,L,2,5
|
||||
2023,SE,FT,Data Engineer,146000,USD,146000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,150000,USD,150000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,122000,USD,122000,US,0,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,172200,USD,172200,US,0,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,109400,USD,109400,US,0,US,M,3,11
|
||||
2022,SE,FT,Analytics Engineer,75000,GBP,92350,GB,0,GB,M,3,8
|
||||
2022,MI,FT,Data Analyst,75000,USD,75000,US,100,US,M,2,11
|
||||
2023,SE,FT,Data Architect,174500,USD,174500,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,85500,USD,85500,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,275300,USD,275300,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,153600,USD,153600,US,0,US,M,3,11
|
||||
2022,MI,FT,Machine Learning Researcher,150000,USD,150000,US,100,US,M,2,11
|
||||
2023,EN,FT,Data Engineer,61800,USD,61800,US,100,US,M,0,11
|
||||
2023,SE,FT,Applied Scientist,136000,USD,136000,US,0,US,L,3,11
|
||||
2023,MI,FT,Data Analyst,100000,USD,100000,US,100,US,M,2,11
|
||||
2023,SE,FT,Applied Data Scientist,100000,AUD,68318,AU,100,FI,M,3,5
|
||||
2022,SE,FT,Data Engineer,117000,USD,117000,US,0,US,M,3,11
|
||||
2021,MI,FT,Data Engineer,100000,USD,100000,US,100,US,L,2,11
|
||||
2022,SE,FT,Machine Learning Engineer,214000,USD,214000,US,100,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,70000,GBP,85066,GB,0,GB,M,2,8
|
||||
2021,SE,FT,Lead Data Engineer,276000,USD,276000,US,0,US,L,3,11
|
||||
2022,SE,FT,Data Engineer,132320,USD,132320,US,100,US,M,3,11
|
||||
2021,SE,FT,Data Science Manager,7000000,INR,94665,IN,50,IN,L,3,47
|
||||
2023,SE,FT,Data Analyst,125000,USD,125000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,110000,USD,110000,US,0,US,M,3,11
|
||||
2022,SE,FT,Machine Learning Manager,150000,USD,150000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,36000,EUR,38631,ES,0,ES,M,3,12
|
||||
2023,SE,FT,Data Analyst,125000,USD,125000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,141525,USD,141525,US,100,US,M,3,11
|
||||
2023,SE,FT,Research Scientist,141288,USD,141288,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,65000,GBP,78990,GB,0,GB,M,2,8
|
||||
2022,SE,FT,Analytics Engineer,190000,USD,190000,US,100,US,M,3,11
|
||||
2021,EN,FT,Data Analyst,50000,EUR,59102,FR,50,FR,M,0,10
|
||||
2023,MI,FT,Data Engineer,125000,USD,125000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Analytics Manager,109280,USD,109280,US,100,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,143000,USD,143000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Architect,190000,USD,190000,US,100,US,M,3,11
|
||||
2023,SE,FT,Machine Learning Engineer,276000,USD,276000,US,0,US,M,3,11
|
||||
2023,SE,FT,Principal Data Scientist,80000,EUR,85847,ES,100,ES,L,3,12
|
||||
2020,EN,FT,Big Data Engineer,70000,USD,70000,US,100,US,L,0,11
|
||||
2022,EN,FT,Data Scientist,80000,USD,80000,US,0,US,M,0,11
|
||||
2020,SE,FT,Machine Learning Scientist,260000,USD,260000,JP,0,JP,S,3,41
|
||||
2022,MI,FT,Data Engineer,95000,USD,95000,US,0,US,M,2,11
|
||||
2023,SE,FT,Data Scientist,36000,EUR,38631,LV,0,LV,M,3,18
|
||||
2023,SE,FT,Data Engineer,153600,USD,153600,US,0,US,M,3,11
|
||||
2023,SE,FT,Analytics Engineer,190000,USD,190000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,203500,USD,203500,US,0,US,M,3,11
|
||||
2023,EX,FT,Data Engineer,106500,USD,106500,US,0,US,M,1,11
|
||||
2022,MI,FT,Deep Learning Engineer,40000,GBP,49253,GB,100,GB,M,2,8
|
||||
2023,EN,FT,Data Analyst,100000,USD,100000,UZ,100,US,L,0,48
|
||||
2023,SE,FT,Data Architect,213120,USD,213120,US,100,US,M,3,11
|
||||
2023,SE,FT,Research Scientist,200000,USD,200000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,90000,USD,90000,US,0,US,M,3,11
|
||||
2023,SE,FT,Research Scientist,200000,USD,200000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,45000,GBP,54685,CF,100,CF,M,3,48
|
||||
2023,SE,FT,Research Scientist,130000,USD,130000,US,100,US,M,3,11
|
||||
2022,EN,FT,Machine Learning Engineer,28500,GBP,35093,GB,100,GB,L,0,8
|
||||
2021,EX,FT,Director of Data Science,120000,EUR,141846,DE,0,DE,L,1,7
|
||||
2022,SE,FT,Data Scientist,249500,USD,249500,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Scientist,108000,USD,108000,US,50,US,L,2,11
|
||||
2022,EN,FT,Machine Learning Developer,33000,USD,33000,IT,100,DE,S,0,14
|
||||
2023,SE,FT,Data Engineer,126000,USD,126000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,147100,USD,147100,US,0,US,M,3,11
|
||||
2021,SE,FT,Data Analyst,115000,USD,115000,US,100,US,S,3,11
|
||||
2023,SE,FT,Data Scientist,145000,USD,145000,US,100,US,M,3,11
|
||||
2022,SE,FT,Machine Learning Engineer,65000,USD,65000,AE,100,AE,S,3,34
|
||||
2023,EN,FT,Data Engineer,1400000,INR,17022,IN,100,IN,L,0,47
|
||||
2022,SE,FT,Data Engineer,100000,USD,100000,US,0,US,M,3,11
|
||||
2023,EN,FT,Data Engineer,125000,USD,125000,US,0,US,M,0,11
|
||||
2022,SE,FT,Data Scientist,191475,USD,191475,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,225000,USD,225000,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Scientist,90000,USD,90000,US,100,US,M,2,11
|
||||
2022,SE,FT,Data Engineer,95000,USD,95000,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,60000,USD,60000,US,0,US,M,3,11
|
||||
2023,EN,FT,Data Analyst,75000,USD,75000,US,100,US,M,0,11
|
||||
2023,MI,FT,Data Analyst,182500,USD,182500,US,0,US,M,2,11
|
||||
2023,EN,FT,Data Analyst,85000,USD,85000,US,100,US,M,0,11
|
||||
2023,SE,FT,Data Analytics Manager,120000,USD,120000,US,100,US,M,3,11
|
||||
2021,SE,FT,Machine Learning Engineer,200000,USD,200000,US,100,US,L,3,11
|
||||
2023,SE,FT,Data Manager,60027,GBP,72946,GB,0,GB,M,3,8
|
||||
2022,SE,FT,Data Scientist,140700,USD,140700,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Manager,135000,USD,135000,US,0,US,M,2,11
|
||||
2023,EN,FT,Data Analyst,60000,USD,60000,US,100,US,M,0,11
|
||||
2023,MI,FT,Data Specialist,130000,USD,130000,US,0,US,M,2,11
|
||||
2022,MI,FT,Analytics Engineer,200000,USD,200000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Scientist,198800,USD,198800,US,0,US,M,3,11
|
||||
2021,EN,FT,Machine Learning Engineer,125000,USD,125000,US,100,US,S,0,11
|
||||
2023,MI,FT,Data Scientist,100000,USD,100000,US,0,US,M,2,11
|
||||
2023,EX,FT,Data Engineer,236000,USD,236000,US,100,US,M,1,11
|
||||
2023,SE,FT,Data Engineer,106800,USD,106800,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,135000,USD,135000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Analyst,121700,USD,121700,US,0,US,M,3,11
|
||||
2023,MI,FT,Data Scientist,82920,USD,82920,US,100,US,M,2,11
|
||||
2022,MI,FT,Data Analyst,100000,USD,100000,US,0,US,M,2,11
|
||||
2022,SE,FT,Data Engineer,178750,USD,178750,US,0,US,M,3,11
|
||||
2022,MI,FT,Data Engineer,50000,GBP,61566,GB,0,GB,M,2,8
|
||||
2021,MI,FT,Data Scientist,109000,USD,109000,US,50,US,L,2,11
|
||||
2023,SE,FT,Data Science Manager,134236,USD,134236,US,0,US,M,3,11
|
||||
2021,SE,FT,Data Engineer,82500,GBP,113476,GB,100,GB,M,3,8
|
||||
2023,SE,FT,Machine Learning Engineer,247500,USD,247500,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,104000,USD,104000,US,100,US,M,3,11
|
||||
2023,SE,FT,Data Scientist,70000,USD,70000,US,0,US,M,3,11
|
||||
2022,SE,FT,Machine Learning Engineer,131300,USD,131300,US,100,US,L,3,11
|
||||
2022,SE,FT,Data Scientist,180000,USD,180000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Engineer,50000,GBP,61566,GB,0,GB,M,3,8
|
||||
2022,MI,FT,Data Scientist,72000,EUR,75648,DE,100,DE,S,2,7
|
||||
2023,SE,FT,Analytics Engineer,116450,USD,116450,US,0,US,M,3,11
|
||||
2021,SE,FT,ML Engineer,256000,USD,256000,US,100,US,S,3,11
|
||||
2023,SE,FT,Data Science Manager,299500,USD,299500,US,0,US,M,3,11
|
||||
2022,EN,FT,Data Engineer,135000,USD,135000,US,0,US,M,0,11
|
||||
2022,SE,FT,Data Engineer,135000,USD,135000,US,100,US,M,3,11
|
||||
2020,MI,FT,Data Analyst,41000,EUR,46759,FR,50,FR,L,2,10
|
||||
2023,SE,FT,Data Analyst,208049,USD,208049,US,0,US,M,3,11
|
||||
2023,SE,FT,Data Engineer,250000,USD,250000,US,100,US,M,3,11
|
||||
2023,MI,FT,Data Analyst,150000,USD,150000,US,0,US,M,2,11
|
||||
2023,SE,FT,Data Scientist,237000,USD,237000,US,100,US,M,3,11
|
||||
2022,SE,FT,Data Analyst,105000,USD,105000,US,0,US,M,3,11
|
||||
2022,SE,FT,Data Scientist,141525,USD,141525,US,100,US,M,3,11
|
||||
2023,EN,FT,Data Engineer,58000,USD,58000,US,100,US,M,0,11
|
||||
2022,SE,FT,Data Analyst,35000,EUR,36773,ES,0,ES,M,3,12
|
||||
2022,SE,FT,Data Analyst,38000,EUR,39925,ES,0,ES,M,3,12
|
||||
|
3005
lab_6/train_data.csv
Normal file
3005
lab_6/train_data.csv
Normal file
File diff suppressed because it is too large
Load Diff
1599
lab_8/laba8.ipynb
Normal file
1599
lab_8/laba8.ipynb
Normal file
File diff suppressed because one or more lines are too long
1083
lab_9/laba9.ipynb
Normal file
1083
lab_9/laba9.ipynb
Normal file
File diff suppressed because one or more lines are too long
53944
static/csv/DiamondsPrices2022.csv
Normal file
53944
static/csv/DiamondsPrices2022.csv
Normal file
File diff suppressed because it is too large
Load Diff
2601
static/csv/Forbes Billionaires.csv
Normal file
2601
static/csv/Forbes Billionaires.csv
Normal file
File diff suppressed because it is too large
Load Diff
21614
static/csv/House.csv
Normal file
21614
static/csv/House.csv
Normal file
File diff suppressed because it is too large
Load Diff
236
static/csv/WorldPopulation.csv
Normal file
236
static/csv/WorldPopulation.csv
Normal file
@@ -0,0 +1,236 @@
|
||||
no,Country (or dependency),Population 2020,Yearly Change,Net Change,Density (P/Km²),Land Area (Km²),Migrants (net),Fert. Rate,Med. Age,Urban Pop %,World Share
|
||||
1,China,"1,439,323,776",0.39%,"5,540,090",153,"9,388,211","-348,399",1.7,38,61%,18.47%
|
||||
2,India,"1,380,004,385",0.99%,"13,586,631",464,"2,973,190","-532,687",2.2,28,35%,17.70%
|
||||
3,United States,"331,002,651",0.59%,"1,937,734",36,"9,147,420","954,806",1.8,38,83%,4.25%
|
||||
4,Indonesia,"273,523,615",1.07%,"2,898,047",151,"1,811,570","-98,955",2.3,30,56%,3.51%
|
||||
5,Pakistan,"220,892,340",2.00%,"4,327,022",287,"770,880","-233,379",3.6,23,35%,2.83%
|
||||
6,Brazil,"212,559,417",0.72%,"1,509,890",25,"8,358,140","21,200",1.7,33,88%,2.73%
|
||||
7,Nigeria,"206,139,589",2.58%,"5,175,990",226,"910,770","-60,000",5.4,18,52%,2.64%
|
||||
8,Bangladesh,"164,689,383",1.01%,"1,643,222","1,265","130,170","-369,501",2.1,28,39%,2.11%
|
||||
9,Russia,"145,934,462",0.04%,"62,206",9,"16,376,870","182,456",1.8,40,74%,1.87%
|
||||
10,Mexico,"128,932,753",1.06%,"1,357,224",66,"1,943,950","-60,000",2.1,29,84%,1.65%
|
||||
11,Japan,"126,476,461",-0.30%,"-383,840",347,"364,555","71,560",1.4,48,92%,1.62%
|
||||
12,Ethiopia,"114,963,588",2.57%,"2,884,858",115,"1,000,000","30,000",4.3,19,21%,1.47%
|
||||
13,Philippines,"109,581,078",1.35%,"1,464,463",368,"298,170","-67,152",2.6,26,47%,1.41%
|
||||
14,Egypt,"102,334,404",1.94%,"1,946,331",103,"995,450","-38,033",3.3,25,43%,1.31%
|
||||
15,Vietnam,"97,338,579",0.91%,"876,473",314,"310,070","-80,000",2.1,32,38%,1.25%
|
||||
16,DR Congo,"89,561,403",3.19%,"2,770,836",40,"2,267,050","23,861",6,17,46%,1.15%
|
||||
17,Turkey,"84,339,067",1.09%,"909,452",110,"769,630","283,922",2.1,32,76%,1.08%
|
||||
18,Iran,"83,992,949",1.30%,"1,079,043",52,"1,628,550","-55,000",2.2,32,76%,1.08%
|
||||
19,Germany,"83,783,942",0.32%,"266,897",240,"348,560","543,822",1.6,46,76%,1.07%
|
||||
20,Thailand,"69,799,978",0.25%,"174,396",137,"510,890","19,444",1.5,40,51%,0.90%
|
||||
21,United Kingdom,"67,886,011",0.53%,"355,839",281,"241,930","260,650",1.8,40,83%,0.87%
|
||||
22,France,"65,273,511",0.22%,"143,783",119,"547,557","36,527",1.9,42,82%,0.84%
|
||||
23,Italy,"60,461,826",-0.15%,"-88,249",206,"294,140","148,943",1.3,47,69%,0.78%
|
||||
24,Tanzania,"59,734,218",2.98%,"1,728,755",67,"885,800","-40,076",4.9,18,37%,0.77%
|
||||
25,South Africa,"59,308,690",1.28%,"750,420",49,"1,213,090","145,405",2.4,28,67%,0.76%
|
||||
26,Myanmar,"54,409,800",0.67%,"364,380",83,"653,290","-163,313",2.2,29,31%,0.70%
|
||||
27,Kenya,"53,771,296",2.28%,"1,197,323",94,"569,140","-10,000",3.5,20,28%,0.69%
|
||||
28,South Korea,"51,269,185",0.09%,"43,877",527,"97,230","11,731",1.1,44,82%,0.66%
|
||||
29,Colombia,"50,882,891",1.08%,"543,448",46,"1,109,500","204,796",1.8,31,80%,0.65%
|
||||
30,Spain,"46,754,778",0.04%,"18,002",94,"498,800","40,000",1.3,45,80%,0.60%
|
||||
31,Uganda,"45,741,007",3.32%,"1,471,413",229,"199,810","168,694",5,17,26%,0.59%
|
||||
32,Argentina,"45,195,774",0.93%,"415,097",17,"2,736,690","4,800",2.3,32,93%,0.58%
|
||||
33,Algeria,"43,851,044",1.85%,"797,990",18,"2,381,740","-10,000",3.1,29,73%,0.56%
|
||||
34,Sudan,"43,849,260",2.42%,"1,036,022",25,"1,765,048","-50,000",4.4,20,35%,0.56%
|
||||
35,Ukraine,"43,733,762",-0.59%,"-259,876",75,"579,320","10,000",1.4,41,69%,0.56%
|
||||
36,Iraq,"40,222,493",2.32%,"912,710",93,"434,320","7,834",3.7,21,73%,0.52%
|
||||
37,Afghanistan,"38,928,346",2.33%,"886,592",60,"652,860","-62,920",4.6,18,25%,0.50%
|
||||
38,Poland,"37,846,611",-0.11%,"-41,157",124,"306,230","-29,395",1.4,42,60%,0.49%
|
||||
39,Canada,"37,742,154",0.89%,"331,107",4,"9,093,510","242,032",1.5,41,81%,0.48%
|
||||
40,Morocco,"36,910,560",1.20%,"438,791",83,"446,300","-51,419",2.4,30,64%,0.47%
|
||||
41,Saudi Arabia,"34,813,871",1.59%,"545,343",16,"2,149,690","134,979",2.3,32,84%,0.45%
|
||||
42,Uzbekistan,"33,469,203",1.48%,"487,487",79,"425,400","-8,863",2.4,28,50%,0.43%
|
||||
43,Peru,"32,971,854",1.42%,"461,401",26,"1,280,000","99,069",2.3,31,79%,0.42%
|
||||
44,Angola,"32,866,272",3.27%,"1,040,977",26,"1,246,700","6,413",5.6,17,67%,0.42%
|
||||
45,Malaysia,"32,365,999",1.30%,"416,222",99,"328,550","50,000",2,30,78%,0.42%
|
||||
46,Mozambique,"31,255,435",2.93%,"889,399",40,"786,380","-5,000",4.9,18,38%,0.40%
|
||||
47,Ghana,"31,072,940",2.15%,"655,084",137,"227,540","-10,000",3.9,22,57%,0.40%
|
||||
48,Yemen,"29,825,964",2.28%,"664,042",56,"527,970","-30,000",3.8,20,38%,0.38%
|
||||
49,Nepal,"29,136,808",1.85%,"528,098",203,"143,350","41,710",1.9,25,21%,0.37%
|
||||
50,Venezuela,"28,435,940",-0.28%,"-79,889",32,"882,050","-653,249",2.3,30,N.A.,0.36%
|
||||
51,Madagascar,"27,691,018",2.68%,"721,711",48,"581,795","-1,500",4.1,20,39%,0.36%
|
||||
52,Cameroon,"26,545,863",2.59%,"669,483",56,"472,710","-4,800",4.6,19,56%,0.34%
|
||||
53,Côte d'Ivoire,"26,378,274",2.57%,"661,730",83,"318,000","-8,000",4.7,19,51%,0.34%
|
||||
54,North Korea,"25,778,816",0.44%,"112,655",214,"120,410","-5,403",1.9,35,63%,0.33%
|
||||
55,Australia,"25,499,884",1.18%,"296,686",3,"7,682,300","158,246",1.8,38,86%,0.33%
|
||||
56,Niger,"24,206,644",3.84%,"895,929",19,"1,266,700","4,000",7,15,17%,0.31%
|
||||
57,Taiwan,"23,816,775",0.18%,"42,899",673,"35,410","30,001",1.2,42,79%,0.31%
|
||||
58,Sri Lanka,"21,413,249",0.42%,"89,516",341,"62,710","-97,986",2.2,34,18%,0.27%
|
||||
59,Burkina Faso,"20,903,273",2.86%,"581,895",76,"273,600","-25,000",5.2,18,31%,0.27%
|
||||
60,Mali,"20,250,833",3.02%,"592,802",17,"1,220,190","-40,000",5.9,16,44%,0.26%
|
||||
61,Romania,"19,237,691",-0.66%,"-126,866",84,"230,170","-73,999",1.6,43,55%,0.25%
|
||||
62,Malawi,"19,129,952",2.69%,"501,205",203,"94,280","-16,053",4.3,18,18%,0.25%
|
||||
63,Chile,"19,116,201",0.87%,"164,163",26,"743,532","111,708",1.7,35,85%,0.25%
|
||||
64,Kazakhstan,"18,776,707",1.21%,"225,280",7,"2,699,700","-18,000",2.8,31,58%,0.24%
|
||||
65,Zambia,"18,383,955",2.93%,"522,925",25,"743,390","-8,000",4.7,18,45%,0.24%
|
||||
66,Guatemala,"17,915,568",1.90%,"334,096",167,"107,160","-9,215",2.9,23,52%,0.23%
|
||||
67,Ecuador,"17,643,054",1.55%,"269,392",71,"248,360","36,400",2.4,28,63%,0.23%
|
||||
68,Syria,"17,500,658",2.52%,"430,523",95,"183,630","-427,391",2.8,26,60%,0.22%
|
||||
69,Netherlands,"17,134,872",0.22%,"37,742",508,"33,720","16,000",1.7,43,92%,0.22%
|
||||
70,Senegal,"16,743,927",2.75%,"447,563",87,"192,530","-20,000",4.7,19,49%,0.21%
|
||||
71,Cambodia,"16,718,965",1.41%,"232,423",95,"176,520","-30,000",2.5,26,24%,0.21%
|
||||
72,Chad,"16,425,864",3.00%,"478,988",13,"1,259,200","2,000",5.8,17,23%,0.21%
|
||||
73,Somalia,"15,893,222",2.92%,"450,317",25,"627,340","-40,000",6.1,17,47%,0.20%
|
||||
74,Zimbabwe,"14,862,924",1.48%,"217,456",38,"386,850","-116,858",3.6,19,38%,0.19%
|
||||
75,Guinea,"13,132,795",2.83%,"361,549",53,"245,720","-4,000",4.7,18,39%,0.17%
|
||||
76,Rwanda,"12,952,218",2.58%,"325,268",525,"24,670","-9,000",4.1,20,18%,0.17%
|
||||
77,Benin,"12,123,200",2.73%,"322,049",108,"112,760","-2,000",4.9,19,48%,0.16%
|
||||
78,Burundi,"11,890,784",3.12%,"360,204",463,"25,680","2,001",5.5,17,14%,0.15%
|
||||
79,Tunisia,"11,818,619",1.06%,"123,900",76,"155,360","-4,000",2.2,33,70%,0.15%
|
||||
80,Bolivia,"11,673,021",1.39%,"159,921",11,"1,083,300","-9,504",2.8,26,69%,0.15%
|
||||
81,Belgium,"11,589,623",0.44%,"50,295",383,"30,280","48,000",1.7,42,98%,0.15%
|
||||
82,Haiti,"11,402,528",1.24%,"139,451",414,"27,560","-35,000",3,24,57%,0.15%
|
||||
83,Cuba,"11,326,616",-0.06%,"-6,867",106,"106,440","-14,400",1.6,42,78%,0.15%
|
||||
84,South Sudan,"11,193,725",1.19%,"131,612",18,"610,952","-174,200",4.7,19,25%,0.14%
|
||||
85,Dominican Republic,"10,847,910",1.01%,"108,952",225,"48,320","-30,000",2.4,28,85%,0.14%
|
||||
86,Czech Republic (Czechia),"10,708,981",0.18%,"19,772",139,"77,240","22,011",1.6,43,74%,0.14%
|
||||
87,Greece,"10,423,054",-0.48%,"-50,401",81,"128,900","-16,000",1.3,46,85%,0.13%
|
||||
88,Jordan,"10,203,134",1.00%,"101,440",115,"88,780","10,220",2.8,24,91%,0.13%
|
||||
89,Portugal,"10,196,709",-0.29%,"-29,478",111,"91,590","-6,000",1.3,46,66%,0.13%
|
||||
90,Azerbaijan,"10,139,177",0.91%,"91,459",123,"82,658","1,200",2.1,32,56%,0.13%
|
||||
91,Sweden,"10,099,265",0.63%,"62,886",25,"410,340","40,000",1.9,41,88%,0.13%
|
||||
92,Honduras,"9,904,607",1.63%,"158,490",89,"111,890","-6,800",2.5,24,57%,0.13%
|
||||
93,United Arab Emirates,"9,890,402",1.23%,"119,873",118,"83,600","40,000",1.4,33,86%,0.13%
|
||||
94,Hungary,"9,660,351",-0.25%,"-24,328",107,"90,530","6,000",1.5,43,72%,0.12%
|
||||
95,Tajikistan,"9,537,645",2.32%,"216,627",68,"139,960","-20,000",3.6,22,27%,0.12%
|
||||
96,Belarus,"9,449,323",-0.03%,"-3,088",47,"202,910","8,730",1.7,40,79%,0.12%
|
||||
97,Austria,"9,006,398",0.57%,"51,296",109,"82,409","65,000",1.5,43,57%,0.12%
|
||||
98,Papua New Guinea,"8,947,024",1.95%,"170,915",20,"452,860",-800,3.6,22,13%,0.11%
|
||||
99,Serbia,"8,737,371",-0.40%,"-34,864",100,"87,460","4,000",1.5,42,56%,0.11%
|
||||
100,Israel,"8,655,535",1.60%,"136,158",400,"21,640","10,000",3,30,93%,0.11%
|
||||
101,Switzerland,"8,654,622",0.74%,"63,257",219,"39,516","52,000",1.5,43,74%,0.11%
|
||||
102,Togo,"8,278,724",2.43%,"196,358",152,"54,390","-2,000",4.4,19,43%,0.11%
|
||||
103,Sierra Leone,"7,976,983",2.10%,"163,768",111,"72,180","-4,200",4.3,19,43%,0.10%
|
||||
104,Hong Kong,"7,496,981",0.82%,"60,827","7,140","1,050","29,308",1.3,45,N.A.,0.10%
|
||||
105,Laos,"7,275,560",1.48%,"106,105",32,"230,800","-14,704",2.7,24,36%,0.09%
|
||||
106,Paraguay,"7,132,538",1.25%,"87,902",18,"397,300","-16,556",2.4,26,62%,0.09%
|
||||
107,Bulgaria,"6,948,445",-0.74%,"-51,674",64,"108,560","-4,800",1.6,45,76%,0.09%
|
||||
108,Libya,"6,871,292",1.38%,"93,840",4,"1,759,540","-1,999",2.3,29,78%,0.09%
|
||||
109,Lebanon,"6,825,445",-0.44%,"-30,268",667,"10,230","-30,012",2.1,30,78%,0.09%
|
||||
110,Nicaragua,"6,624,554",1.21%,"79,052",55,"120,340","-21,272",2.4,26,57%,0.08%
|
||||
111,Kyrgyzstan,"6,524,195",1.69%,"108,345",34,"191,800","-4,000",3,26,36%,0.08%
|
||||
112,El Salvador,"6,486,205",0.51%,"32,652",313,"20,720","-40,539",2.1,28,73%,0.08%
|
||||
113,Turkmenistan,"6,031,200",1.50%,"89,111",13,"469,930","-5,000",2.8,27,53%,0.08%
|
||||
114,Singapore,"5,850,342",0.79%,"46,005","8,358",700,"27,028",1.2,42,N.A.,0.08%
|
||||
115,Denmark,"5,792,202",0.35%,"20,326",137,"42,430","15,200",1.8,42,88%,0.07%
|
||||
116,Finland,"5,540,720",0.15%,"8,564",18,"303,890","14,000",1.5,43,86%,0.07%
|
||||
117,Congo,"5,518,087",2.56%,"137,579",16,"341,500","-4,000",4.5,19,70%,0.07%
|
||||
118,Slovakia,"5,459,642",0.05%,"2,629",114,"48,088","1,485",1.5,41,54%,0.07%
|
||||
119,Norway,"5,421,241",0.79%,"42,384",15,"365,268","28,000",1.7,40,83%,0.07%
|
||||
120,Oman,"5,106,626",2.65%,"131,640",16,"309,500","87,400",2.9,31,87%,0.07%
|
||||
121,State of Palestine,"5,101,414",2.41%,"119,994",847,"6,020","-10,563",3.7,21,80%,0.07%
|
||||
122,Costa Rica,"5,094,118",0.92%,"46,557",100,"51,060","4,200",1.8,33,80%,0.07%
|
||||
123,Liberia,"5,057,681",2.44%,"120,307",53,"96,320","-5,000",4.4,19,53%,0.06%
|
||||
124,Ireland,"4,937,786",1.13%,"55,291",72,"68,890","23,604",1.8,38,63%,0.06%
|
||||
125,Central African Republic,"4,829,767",1.78%,"84,582",8,"622,980","-40,000",4.8,18,43%,0.06%
|
||||
126,New Zealand,"4,822,233",0.82%,"39,170",18,"263,310","14,881",1.9,38,87%,0.06%
|
||||
127,Mauritania,"4,649,658",2.74%,"123,962",5,"1,030,700","5,000",4.6,20,57%,0.06%
|
||||
128,Panama,"4,314,767",1.61%,"68,328",58,"74,340","11,200",2.5,30,68%,0.06%
|
||||
129,Kuwait,"4,270,571",1.51%,"63,488",240,"17,820","39,520",2.1,37,N.A.,0.05%
|
||||
130,Croatia,"4,105,267",-0.61%,"-25,037",73,"55,960","-8,001",1.4,44,58%,0.05%
|
||||
131,Moldova,"4,033,963",-0.23%,"-9,300",123,"32,850","-1,387",1.3,38,43%,0.05%
|
||||
132,Georgia,"3,989,167",-0.19%,"-7,598",57,"69,490","-10,000",2.1,38,58%,0.05%
|
||||
133,Eritrea,"3,546,421",1.41%,"49,304",35,"101,000","-39,858",4.1,19,63%,0.05%
|
||||
134,Uruguay,"3,473,730",0.35%,"11,996",20,"175,020","-3,000",2,36,96%,0.04%
|
||||
135,Bosnia and Herzegovina,"3,280,819",-0.61%,"-20,181",64,"51,000","-21,585",1.3,43,52%,0.04%
|
||||
136,Mongolia,"3,278,290",1.65%,"53,123",2,"1,553,560",-852,2.9,28,67%,0.04%
|
||||
137,Armenia,"2,963,243",0.19%,"5,512",104,"28,470","-4,998",1.8,35,63%,0.04%
|
||||
138,Jamaica,"2,961,167",0.44%,"12,888",273,"10,830","-11,332",2,31,55%,0.04%
|
||||
139,Qatar,"2,881,053",1.73%,"48,986",248,"11,610","40,000",1.9,32,96%,0.04%
|
||||
140,Albania,"2,877,797",-0.11%,"-3,120",105,"27,400","-14,000",1.6,36,63%,0.04%
|
||||
141,Puerto Rico,"2,860,853",-2.47%,"-72,555",323,"8,870","-97,986",1.2,44,N.A.,0.04%
|
||||
142,Lithuania,"2,722,289",-1.35%,"-37,338",43,"62,674","-32,780",1.7,45,71%,0.03%
|
||||
143,Namibia,"2,540,905",1.86%,"46,375",3,"823,290","-4,806",3.4,22,55%,0.03%
|
||||
144,Gambia,"2,416,668",2.94%,"68,962",239,"10,120","-3,087",5.3,18,59%,0.03%
|
||||
145,Botswana,"2,351,627",2.08%,"47,930",4,"566,730","3,000",2.9,24,73%,0.03%
|
||||
146,Gabon,"2,225,734",2.45%,"53,155",9,"257,670","3,260",4,23,87%,0.03%
|
||||
147,Lesotho,"2,142,249",0.80%,"16,981",71,"30,360","-10,047",3.2,24,31%,0.03%
|
||||
148,North Macedonia,"2,083,374",0.00%,-85,83,"25,220","-1,000",1.5,39,59%,0.03%
|
||||
149,Slovenia,"2,078,938",0.01%,284,103,"20,140","2,000",1.6,45,55%,0.03%
|
||||
150,Guinea-Bissau,"1,968,001",2.45%,"47,079",70,"28,120","-1,399",4.5,19,45%,0.03%
|
||||
151,Latvia,"1,886,198",-1.08%,"-20,545",30,"62,200","-14,837",1.7,44,69%,0.02%
|
||||
152,Bahrain,"1,701,575",3.68%,"60,403","2,239",760,"47,800",2,32,89%,0.02%
|
||||
153,Equatorial Guinea,"1,402,985",3.47%,"46,999",50,"28,050","16,000",4.6,22,73%,0.02%
|
||||
154,Trinidad and Tobago,"1,399,488",0.32%,"4,515",273,"5,130",-800,1.7,36,52%,0.02%
|
||||
155,Estonia,"1,326,535",0.07%,887,31,"42,390","3,911",1.6,42,68%,0.02%
|
||||
156,Timor-Leste,"1,318,445",1.96%,"25,326",89,"14,870","-5,385",4.1,21,33%,0.02%
|
||||
157,Mauritius,"1,271,768",0.17%,"2,100",626,"2,030",0,1.4,37,41%,0.02%
|
||||
158,Cyprus,"1,207,359",0.73%,"8,784",131,"9,240","5,000",1.3,37,67%,0.02%
|
||||
159,Eswatini,"1,160,164",1.05%,"12,034",67,"17,200","-8,353",3,21,30%,0.01%
|
||||
160,Djibouti,"988,000",1.48%,"14,440",43,"23,180",900,2.8,27,79%,0.01%
|
||||
161,Fiji,"896,445",0.73%,"6,492",49,"18,270","-6,202",2.8,28,59%,0.01%
|
||||
162,Réunion,"895,312",0.72%,"6,385",358,"2,500","-1,256",2.3,36,100%,0.01%
|
||||
163,Comoros,"869,601",2.20%,"18,715",467,"1,861","-2,000",4.2,20,29%,0.01%
|
||||
164,Guyana,"786,552",0.48%,"3,786",4,"196,850","-6,000",2.5,27,27%,0.01%
|
||||
165,Bhutan,"771,608",1.12%,"8,516",20,"38,117",320,2,28,46%,0.01%
|
||||
166,Solomon Islands,"686,884",2.55%,"17,061",25,"27,990","-1,600",4.4,20,23%,0.01%
|
||||
167,Macao,"649,335",1.39%,"8,890","21,645",30,"5,000",1.2,39,N.A.,0.01%
|
||||
168,Montenegro,"628,066",0.01%,79,47,"13,450",-480,1.8,39,68%,0.01%
|
||||
169,Luxembourg,"625,978",1.66%,"10,249",242,"2,590","9,741",1.5,40,88%,0.01%
|
||||
170,Western Sahara,"597,339",2.55%,"14,876",2,"266,000","5,582",2.4,28,87%,0.01%
|
||||
171,Suriname,"586,632",0.90%,"5,260",4,"156,000","-1,000",2.4,29,65%,0.01%
|
||||
172,Cabo Verde,"555,987",1.10%,"6,052",138,"4,030","-1,342",2.3,28,68%,0.01%
|
||||
173,Micronesia,"548,914",1.00%,"5,428",784,700,"-2,957",2.9,27,68%,0.01%
|
||||
174,Maldives,"540,544",1.81%,"9,591","1,802",300,"11,370",1.9,30,35%,0.01%
|
||||
175,Malta,"441,543",0.27%,"1,171","1,380",320,900,1.5,43,93%,0.01%
|
||||
176,Brunei,"437,479",0.97%,"4,194",83,"5,270",0,1.8,32,80%,0.01%
|
||||
177,Guadeloupe,"400,124",0.02%,68,237,"1,690","-1,440",2.2,44,N.A.,0.01%
|
||||
178,Belize,"397,628",1.86%,"7,275",17,"22,810","1,200",2.3,25,46%,0.01%
|
||||
179,Bahamas,"393,244",0.97%,"3,762",39,"10,010","1,000",1.8,32,86%,0.01%
|
||||
180,Martinique,"375,265",-0.08%,-289,354,"1,060",-960,1.9,47,92%,0.00%
|
||||
181,Iceland,"341,243",0.65%,"2,212",3,"100,250",380,1.8,37,94%,0.00%
|
||||
182,Vanuatu,"307,145",2.42%,"7,263",25,"12,190",120,3.8,21,24%,0.00%
|
||||
183,French Guiana,"298,682",2.70%,"7,850",4,"82,200","1,200",3.4,25,87%,0.00%
|
||||
184,Barbados,"287,375",0.12%,350,668,430,-79,1.6,40,31%,0.00%
|
||||
185,New Caledonia,"285,498",0.97%,"2,748",16,"18,280",502,2,34,72%,0.00%
|
||||
186,French Polynesia,"280,908",0.58%,"1,621",77,"3,660","-1,000",2,34,64%,0.00%
|
||||
187,Mayotte,"272,815",2.50%,"6,665",728,375,0,3.7,20,46%,0.00%
|
||||
188,Sao Tome & Principe,"219,159",1.91%,"4,103",228,960,"-1,680",4.4,19,74%,0.00%
|
||||
189,Samoa,"198,414",0.67%,"1,317",70,"2,830","-2,803",3.9,22,18%,0.00%
|
||||
190,Saint Lucia,"183,627",0.46%,837,301,610,0,1.4,34,19%,0.00%
|
||||
191,Channel Islands,"173,863",0.93%,"1,604",915,190,"1,351",1.5,43,30%,0.00%
|
||||
192,Guam,"168,775",0.89%,"1,481",313,540,-506,2.3,31,95%,0.00%
|
||||
193,Curaçao,"164,093",0.41%,669,370,444,515,1.8,42,89%,0.00%
|
||||
194,Kiribati,"119,449",1.57%,"1,843",147,810,-800,3.6,23,57%,0.00%
|
||||
195,Grenada,"112,523",0.46%,520,331,340,-200,2.1,32,35%,0.00%
|
||||
196,St. Vincent & Grenadines,"110,940",0.32%,351,284,390,-200,1.9,33,53%,0.00%
|
||||
197,Aruba,"106,766",0.43%,452,593,180,201,1.9,41,44%,0.00%
|
||||
198,Tonga,"105,695",1.15%,"1,201",147,720,-800,3.6,22,24%,0.00%
|
||||
199,U.S. Virgin Islands,"104,425",-0.15%,-153,298,350,-451,2,43,96%,0.00%
|
||||
200,Seychelles,"98,347",0.62%,608,214,460,-200,2.5,34,56%,0.00%
|
||||
201,Antigua and Barbuda,"97,929",0.84%,811,223,440,0,2,34,26%,0.00%
|
||||
202,Isle of Man,"85,033",0.53%,449,149,570,,N.A.,N.A.,53%,0.00%
|
||||
203,Andorra,"77,265",0.16%,123,164,470,,N.A.,N.A.,88%,0.00%
|
||||
204,Dominica,"71,986",0.25%,178,96,750,,N.A.,N.A.,74%,0.00%
|
||||
205,Cayman Islands,"65,722",1.19%,774,274,240,,N.A.,N.A.,97%,0.00%
|
||||
206,Bermuda,"62,278",-0.36%,-228,"1,246",50,,N.A.,N.A.,97%,0.00%
|
||||
207,Marshall Islands,"59,190",0.68%,399,329,180,,N.A.,N.A.,70%,0.00%
|
||||
208,Northern Mariana Islands,"57,559",0.60%,343,125,460,,N.A.,N.A.,88%,0.00%
|
||||
209,Greenland,"56,770",0.17%,98,0,"410,450",,N.A.,N.A.,87%,0.00%
|
||||
210,American Samoa,"55,191",-0.22%,-121,276,200,,N.A.,N.A.,88%,0.00%
|
||||
211,Saint Kitts & Nevis,"53,199",0.71%,376,205,260,,N.A.,N.A.,33%,0.00%
|
||||
212,Faeroe Islands,"48,863",0.38%,185,35,"1,396",,N.A.,N.A.,43%,0.00%
|
||||
213,Sint Maarten,"42,876",1.15%,488,"1,261",34,,N.A.,N.A.,96%,0.00%
|
||||
214,Monaco,"39,242",0.71%,278,"26,337",1,,N.A.,N.A.,N.A.,0.00%
|
||||
215,Turks and Caicos,"38,717",1.38%,526,41,950,,N.A.,N.A.,89%,0.00%
|
||||
216,Saint Martin,"38,666",1.75%,664,730,53,,N.A.,N.A.,0%,0.00%
|
||||
217,Liechtenstein,"38,128",0.29%,109,238,160,,N.A.,N.A.,15%,0.00%
|
||||
218,San Marino,"33,931",0.21%,71,566,60,,N.A.,N.A.,97%,0.00%
|
||||
219,Gibraltar,"33,691",-0.03%,-10,"3,369",10,,N.A.,N.A.,N.A.,0.00%
|
||||
220,British Virgin Islands,"30,231",0.67%,201,202,150,,N.A.,N.A.,52%,0.00%
|
||||
221,Caribbean Netherlands,"26,223",0.94%,244,80,328,,N.A.,N.A.,75%,0.00%
|
||||
222,Palau,"18,094",0.48%,86,39,460,,N.A.,N.A.,N.A.,0.00%
|
||||
223,Cook Islands,"17,564",0.09%,16,73,240,,N.A.,N.A.,75%,0.00%
|
||||
224,Anguilla,"15,003",0.90%,134,167,90,,N.A.,N.A.,N.A.,0.00%
|
||||
225,Tuvalu,"11,792",1.25%,146,393,30,,N.A.,N.A.,62%,0.00%
|
||||
226,Wallis & Futuna,"11,239",-1.69%,-193,80,140,,N.A.,N.A.,0%,0.00%
|
||||
227,Nauru,"10,824",0.63%,68,541,20,,N.A.,N.A.,N.A.,0.00%
|
||||
228,Saint Barthelemy,"9,877",0.30%,30,470,21,,N.A.,N.A.,0%,0.00%
|
||||
229,Saint Helena,"6,077",0.30%,18,16,390,,N.A.,N.A.,27%,0.00%
|
||||
230,Saint Pierre & Miquelon,"5,794",-0.48%,-28,25,230,,N.A.,N.A.,100%,0.00%
|
||||
231,Montserrat,"4,992",0.06%,3,50,100,,N.A.,N.A.,10%,0.00%
|
||||
232,Falkland Islands,"3,480",3.05%,103,0,"12,170",,N.A.,N.A.,66%,0.00%
|
||||
233,Niue,"1,626",0.68%,11,6,260,,N.A.,N.A.,46%,0.00%
|
||||
234,Tokelau,"1,357",1.27%,17,136,10,,N.A.,N.A.,0%,0.00%
|
||||
235,Holy See,801,0.25%,2,"2,003",0,,N.A.,N.A.,N.A.,0.00%
|
||||
|
3756
static/csv/ds_salaries.csv
Normal file
3756
static/csv/ds_salaries.csv
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user