A newspaper editor was researching immigration data trends on H1B(H-1B, H-1B1, E-3) visa application processing over the past years, trying to identify the occupations and states with the most number of approved H1B visas. She has found statistics available from the US Department of Labor and its Office of Foreign Labor Certification Performance Data. But while there are ready-made reports for 2018 and 2017, the site doesn’t have them for past years.
As a data engineer, you are asked to create a mechanism to analyze past years data, specificially calculate two metrics: Top 10 Occupations and Top 10 States for certified visa applications.
Your code should be modular and reusable for future. If the newspaper gets data for the year 2019 (with the assumption that the necessary data to calculate the metrics are available) and puts it in the input directory, running the run.sh script should produce the results in the output folder without needing to change the code.
Raw data could be found here under the Disclosure Data tab (i.e., files listed in the Disclosure File column with ".xlsx" extension). The File Structure docs can be found in the website. For convenience, the Excel files have been converted into a semicolon separated (";") format and placed them into this Google drive folder.
The program takes semicolon separated files as input, adhering to the file structure given on the website.
Few pointers on input file,
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The input has to be a single file named h1b_input.csv.
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The input file has to be utf8 encoded (please convert xlsx to csv before providing input).
My program creates 2 output files:
top_10_occupations.txt: Top 10 occupations for certified visa applicationstop_10_states.txt: Top 10 states for certified visa applications
Each line holds one record and each field on each line is separated by a semicolon (;).
Each line of the top_10_occupations.txt file contains these fields in this order:
TOP_OCCUPATIONS: The occupation name associated with an application's Standard Occupational Classification (SOC) code.NUMBER_CERTIFIED_APPLICATIONS: Number of applications that have been certified for that occupation.PERCENTAGE: % of applications that have been certified for that occupation compared to total number of certified applications regardless of occupation.
The records in the file are sorted by NUMBER_CERTIFIED_APPLICATIONS, and in case of a tie, alphabetically by TOP_OCCUPATIONS.
Each line of the top_10_states.txt file contains these fields in this order:
TOP_STATES: State where the work will take place.NUMBER_CERTIFIED_APPLICATIONS: Number of applications that have been certified for work in that state.PERCENTAGE: % of applications that have been certified in that state compared to total number of certified applications regardless of state.
The records in this file are sorted by NUMBER_CERTIFIED_APPLICATIONS field, and in case of a tie, alphabetically by TOP_STATES.
Depending on the input (e.g., see the example below), there may be fewer than 10 lines in each file. There, however, will not be more than 10 lines in each file.
Percentages are rounded off to 1 decimal place. For instance, 1.05% is rounded to 1.1% and 1.04% is rounded to 1.0%. Also, 1% is represented by 1.0%
If you are given the input file, ./input/h1b_input.csv with the following data:
;CASE_NUMBER;CASE_STATUS;CASE_SUBMITTED;DECISION_DATE;VISA_CLASS;EMPLOYMENT_START_DATE;EMPLOYMENT_END_DATE;EMPLOYER_NAME;EMPLOYER_BUSINESS_DBA;EMPLOYER_ADDRESS;EMPLOYER_CITY;EMPLOYER_STATE;EMPLOYER_POSTAL_CODE;EMPLOYER_COUNTRY;EMPLOYER_PROVINCE;EMPLOYER_PHONE;EMPLOYER_PHONE_EXT;AGENT_REPRESENTING_EMPLOYER;AGENT_ATTORNEY_NAME;AGENT_ATTORNEY_CITY;AGENT_ATTORNEY_STATE;JOB_TITLE;SOC_CODE;SOC_NAME;NAICS_CODE;TOTAL_WORKERS;NEW_EMPLOYMENT;CONTINUED_EMPLOYMENT;CHANGE_PREVIOUS_EMPLOYMENT;NEW_CONCURRENT_EMP;CHANGE_EMPLOYER;AMENDED_PETITION;FULL_TIME_POSITION;PREVAILING_WAGE;PW_UNIT_OF_PAY;PW_WAGE_LEVEL;PW_SOURCE;PW_SOURCE_YEAR;PW_SOURCE_OTHER;WAGE_RATE_OF_PAY_FROM;WAGE_RATE_OF_PAY_TO;WAGE_UNIT_OF_PAY;H1B_DEPENDENT;WILLFUL_VIOLATOR;SUPPORT_H1B;LABOR_CON_AGREE;PUBLIC_DISCLOSURE_LOCATION;WORKSITE_CITY;WORKSITE_COUNTY;WORKSITE_STATE;WORKSITE_POSTAL_CODE;ORIGINAL_CERT_DATE
0;I-200-18026-338377;CERTIFIED;2018-01-29;2018-02-02;H-1B;2018-07-28;2021-07-27;MICROSOFT CORPORATION;;1 MICROSOFT WAY;REDMOND;WA;98052;UNITED STATES OF AMERICA;;4258828080;;N;",";;;SOFTWARE ENGINEER;15-1132;"SOFTWARE DEVELOPERS, APPLICATIONS";51121.0;1;0;1;0;0;0;0;Y;112549.0;Year;Level II;OES;2017.0;OFLC ONLINE DATA CENTER;143915.0;0.0;Year;N;N;;;;REDMOND;KING;WA;98052;
1;I-200-17296-353451;CERTIFIED;2017-10-23;2017-10-27;H-1B;2017-11-06;2020-11-06;ERNST & YOUNG U.S. LLP;;200 PLAZA DRIVE;SECAUCUS;NJ;07094;UNITED STATES OF AMERICA;;2018723003;;Y;"BRADSHAW, MELANIE";TORONTO;;TAX SENIOR;13-2011;ACCOUNTANTS AND AUDITORS;541211.0;1;0;0;0;0;1;0;Y;79976.0;Year;Level II;OES;2017.0;OFLC ONLINE DATA CENTER;100000.0;0.0;Year;N;N;;;;SANTA CLARA;SAN JOSE;CA;95110;
2;I-200-18242-524477;CERTIFIED;2018-08-30;2018-09-06;H-1B;2018-09-10;2021-09-09;LOGIXHUB LLC;;320 DECKER DRIVE;IRVING;TX;75062;UNITED STATES OF AMERICA;;2145419305;;N;",";;;DATABASE ADMINISTRATOR;15-1141;DATABASE ADMINISTRATORS;541511.0;1;0;0;0;0;1;0;Y;77792.0;Year;Level II;OES;2018.0;OFLC ONLINE DATA CENTER;78240.0;0.0;Year;N;N;;;;IRVING;DALLAS;TX;75062;
3;I-200-18070-575236;CERTIFIED;;2018-03-30;H-1B;2018-09-10;2021-09-09;"HEXAWARE TECHNOLOGIES, INC.";;101 WOOD AVENUE SOUTH;ISELIN;NJ;08830;UNITED STATES OF AMERICA;;6094096950;;Y;"DUTOT, CHRISTOPHER";TROY;MI;SOFTWARE ENGINEER;15-1132;"SOFTWARE DEVELOPERS, APPLICATIONS";541511.0;5;5;0;0;0;0;0;Y;84406.0;Year;Level II;OES;2017.0;OFLC ONLINE DATA CENTER;84406.0;85000.0;Year;Y;N;Y;;;NEW CASTLE;NEW CASTLE;DE;19720;
4;I-200-18243-850522;CERTIFIED;2018-08-31;2018-09-07;H-1B;2018-09-07;2021-09-06;"ECLOUD LABS,INC.";;120 S WOOD AVENUE;ISELIN;NJ;08830;UNITED STATES OF AMERICA;;7327501323;;Y;"ALLEN, THOMAS";EDISON;NJ;MICROSOFT DYNAMICS CRM APPLICATION DEVELOPER;15-1132;"SOFTWARE DEVELOPERS, APPLICATIONS";541511.0;1;0;0;0;0;0;1;Y;87714.0;Year;Level III;OES;2018.0;OFLC ONLINE DATA CENTER;95000.0;0.0;Year;Y;N;Y;Y;;BIRMINGHAM;SHELBY;AL;35244;
5;I-200-18142-939501;CERTIFIED;2018-05-22;2018-05-29;H-1B;2018-05-29;2021-05-28;OBERON IT;;1404 W WALNUT HILL LN;IRVING;TX;75038;UNITED STATES OF AMERICA;;8666609190;;Y;"GARRITSON, JAMES";RICHARDSON;TX;SENIOR SYSTEM ARCHITECT;15-1132;"SOFTWARE DEVELOPERS, APPLICATIONS";541511.0;1;0;0;0;0;0;1;Y;71864.0;Year;Level II;Other;2017.0;OFLC ONLINE DATA CENTER;74000.0;0.0;Year;Y;N;Y;;;SUNRISE;BROWARD;FL;33323;
6;I-200-18121-552858;CERTIFIED;2018-05-01;2018-05-07;H-1B;2018-05-02;2018-10-26;ICONSOFT INC.;;101 CAMBRIDGE STREET SUITE 360;BURLINGTON;MA;01803;UNITED STATES OF AMERICA;;8882054614;1;N;",";;;SENIOR ORACLE ADF DEVELOPER;15-1132;"SOFTWARE DEVELOPERS, APPLICATIONS";541511.0;1;0;1;0;0;0;0;Y;92331.0;Year;Level III;Other;2017.0;OFLC ONLINE DATA CENTER;114000.0;0.0;Year;Y;N;Y;;;JACKSONVILLE;DUVAL COUNTY;FL;32202;
7;I-200-18215-849606;CERTIFIED;2018-08-03;2018-08-09;H-1B;2018-08-11;2021-08-11;COGNIZANT TECHNOLOGY SOLUTIONS US CORP;;211 QUALITY CIRCLE;COLLEGE STATION;TX;77845;UNITED STATES OF AMERICA;;2019661249;;N;",";;;SENIOR SYSTEMS ANALYST JC60;15-1121;COMPUTER SYSTEMS ANALYST;541512.0;1;0;1;0;0;0;0;Y;80579.0;Year;Level II;OES;2018.0;OFLC ONLINE DATA CENTER;80579.0;0.0;Year;Y;N;Y;;;OWINGS MILLS;BALTIMORE;MD;21117;
8;I-201-17339-472823;CERTIFIED;2017-12-08;2017-12-14;H-1B1 Chile;2017-12-08;2019-06-07;ISHI SYSTEMS INC;;185 HUDSON STREET;JERSEY CITY;NJ;07311;UNITED STATES OF AMERICA;;2013326900;;N;",";;;ASSOCIATE PRODUCT MANAGER(15-1199.09);15-1199;"COMPUTER OCCUPATIONS, ALL OTHER";541511.0;1;0;1;0;0;0;0;Y;88317.0;Year;Level III;OES;2017.0;OFLC ONLINE DATA CENTER;90000.0;0.0;Year;;;;;;JERSEY CITY;HUDSON;NJ;07311;
9;I-200-18233-239931;CERTIFIED;2018-08-21;2018-08-27;H-1B;2018-09-05;2021-09-04;"WB SOLUTIONS, LLC";;7320 E FLETCHER AVE;TAMPA;FL;33637;UNITED STATES OF AMERICA;;8133300099;;Y;"KIDAMBI, VAMAN";TRUMBULL;CT;SENIOR JAVA DEVELOPER;15-1132;"SOFTWARE DEVELOPERS, APPLICATIONS";541511.0;1;0;0;0;0;1;0;Y;104790.0;Year;Level III;OES;2018.0;OFLC ONLINE DATA CENTER;105000.0;0.0;Year;Y;N;Y;Y;;ALPHARETTA;FULTON;GA;30005;
then your output files would be:
./output/top_10_occupations.txt:
TOP_OCCUPATIONS;NUMBER_CERTIFIED_APPLICATIONS;PERCENTAGE
SOFTWARE DEVELOPERS, APPLICATIONS;6;60.0%
ACCOUNTANTS AND AUDITORS;1;10.0%
COMPUTER OCCUPATIONS, ALL OTHER;1;10.0%
COMPUTER SYSTEMS ANALYST;1;10.0%
DATABASE ADMINISTRATORS;1;10.0%
./output/top_10_states.txt:
TOP_STATES;NUMBER_CERTIFIED_APPLICATIONS;PERCENTAGE
FL;2;20.0%
AL;1;10.0%
CA;1;10.0%
DE;1;10.0%
GA;1;10.0%
MD;1;10.0%
NJ;1;10.0%
TX;1;10.0%
WA;1;10.0%
My steps to solve this problem were, read file contents line by line understand schema from header clean data row if necessary insert required data into dictionary sort dictionary write data to output file
At first, as part of rapid prototyping, I read the contents of the file and added the required counts to a dictionary and then wrote results to output file. I chose dictionary data structure as it perfectly suits the problem, we need counts of each occupation and state, with occupation as key(needs to be unique) and counts as value.
But later on inspection of data, I found that all the data is not clean and data parsing fails for some rows. So I cleaned the data before adding it to the respective dictionaries. I wrote a custom function to parse the data accordingly.
When looking at the data, I found that many rows have occupation names as different strings even though they belong to same soc code. This results in incorrect counts and inaccuracies. It makes more sense to choose a column like soc code(with unique value for occupations) as key of the dictionary.
For example, few rows have MARKETING MANAGER as soc name while few have MARKETING MANAGERS as soc name but all those rows have the same soc code 11-2021. Version 1 counts these rows as different rows therefore producing inaccurate counts.
So, I revised my approach by considering soc code as keys and a dictionary of soc name and its counts as value. I used this complex dictionary of dictionary to get more accurate counts on occupations. While solving the problem with this approach I figured, soc code column data for few rows is not in format, so I cleaned those rows and then inserted.
I understand that I would be increasing the code complexity, run time and space by implementing version 2 but this version gives more accurate results and much intuitive intrepretation of occupation names. The run time did not increase drastically when changing from version 1 to version 2 for this dataset.
Counting top states would remain same in both versions as the state names are clean and follow the format except for missing values.
I used python3 for solving this challenge and used only the sys standard library for command line arguments needed for my script.
Running the run.sh file in the project root directory will run my script and produce the results in the specified format in specified folder.
Please run the run_tests.sh script to run all the tests. The results are stored in results.txt in the same directory.
Feel free to contact me if you need anything.