Dataset

This dataset was created by SHAMANTH

Contents

Dataset

This dataset was created by Santhosh Kumar

Released under Apache 2.0

Contents

Dataset

This dataset was created by Rohan Kayan

Released under Other (specified in description)

Contents

This Dataset indicates average salary by position title and grade for full-time regular employees. Data excludes elected, appointed, non-merit and temporary employees. Underfilled positions are also excluded from the dataset. Update Frequency : Annually

Annual salary information including gross pay and overtime pay for all active, permanent employees of Montgomery County, MD paid in calendar year 2023. This dataset is a prime candidate for conducting analyses on salary disparities, the relationship between department/division and salary, and the distribution of salaries across gender and grade levels.

Statistical models can be applied to predict base salaries based on factors such as department, grade, and length of service. Machine learning techniques could also be employed to identify patterns and anomalies in the salary data, such as outliers or instances of significant inequity.

Some analysis to be performed with this dataset can include:

• Gender Pay Gap Analysis: An examination of salary differences between genders within similar roles, grades, and departments to identify any disparities that need to be addressed.
• Departmental Salary Analysis: Analyzing the distribution of salaries across different departments and divisions to understand how compensation varies within the organization.
• Impact of Overtime and Longevity Pay: Evaluating how overtime and longevity pay contribute to the overall compensation of employees and identifying trends or patterns in these payments. ​

The ATO (Australian Tax Office) made a dataset openly available (see links) showing all the Australian Salary and Wages (2002, 2006, 2010, 2014) by detailed occupation (around 1,000) and over 100 SA4 regions. Sole Trader sales and earnings are also provided. This open data (csv) is now packaged into a database (*.sql) with 45 sample SQL queries (backupSQL[date]_public.txt).See more description at related Figshare #datavis record. Versions:V5: Following #datascience course, I have made main data (individual salary and wages) available as csv and Jupyter Notebook. Checksum matches #dataTotals. In 209,xxx rows.Also provided Jobs, and SA4(Locations) description files as csv. More details at: Where are jobs growing/shrinking? Figshare DOI: 4056282 (linked below). Noted 1% discrepancy ($6B) in 2010 wages total - to follow up.#dataTotals - Salary and WagesYearWorkers (M)Earnings ($B) 20028.528520069.4372201010.2481201410.3584#dataTotal - Sole TradersYearWorkers (M)Sales ($B)Earnings ($B)20020.9611320061.0881920101.11122620141.19630#links See ATO request for data at ideascale link below.See original csv open data set (CC-BY) at data.gov.au link below.This database was used to create maps of change in regional employment - see Figshare link below (m9.figshare.4056282).#packageThis file package contains a database (analysing the open data) in SQL package and sample SQL text, interrogating the DB. DB name: test. There are 20 queries relating to Salary and Wages.#analysisThe database was analysed and outputs provided on Nectar(.org.au) resources at: http://118.138.240.130.(offline)This is only resourced for max 1 year, from July 2016, so will expire in June 2017. Hence the filing here. The sample home page is provided here (and pdf), but not all the supporting files, which may be packaged and added later. Until then all files are available at the Nectar URL. Nectar URL now offline - server files attached as package (html_backup[date].zip), including php scripts, html, csv, jpegs.#installIMPORT: DB SQL dump e.g. test_2016-12-20.sql (14.8Mb)1.Started MAMP on OSX.1.1 Go to PhpMyAdmin2. New Database: 3. Import: Choose file: test_2016-12-20.sql -> Go (about 15-20 seconds on MacBookPro 16Gb, 2.3 Ghz i5)4. four tables appeared: jobTitles 3,208 rows | salaryWages 209,697 rows | soleTrader 97,209 rows | stateNames 9 rowsplus views e.g. deltahair, Industrycodes, states5. Run test query under **#; Sum of Salary by SA4 e.g. 101 $4.7B, 102 $6.9B#sampleSQLselect sa4,(select sum(count) from salaryWageswhere year = '2014' and sa4 = sw.sa4) as thisYr14,(select sum(count) from salaryWageswhere year = '2010' and sa4 = sw.sa4) as thisYr10,(select sum(count) from salaryWageswhere year = '2006' and sa4 = sw.sa4) as thisYr06,(select sum(count) from salaryWageswhere year = '2002' and sa4 = sw.sa4) as thisYr02from salaryWages swgroup by sa4order by sa4

Context

In the rapidly evolving field of data science, understanding the trends and patterns in salaries is crucial for professionals and organizations alike. This dataset aims to shed light on the landscape of Data Science Salaries from 2020 to 2024. By analyzing salary data over this period, data enthusiasts, researchers, and industry professionals can gain valuable insights into salary trends, regional variations, and potential factors influencing compensation within the data science community.

Content

The dataset encompasses a comprehensive collection of data science salary information, covering a span of five years from 2020 to 2024. The data includes various aspects related to salaries, providing a multifaceted view of compensation in the field.

Dataset Structure

This dataset (data_science_salaries) covering from 2020 up to 2024 includes the following columns:

Acknowledgment

The primary dataset was retrieved from the ai-jobs.net. I sincerely thank the team for providing the core data used in this dataset.

© Image credit: Freepik

Problem Description: Develop a salary prediction system based on the given dataset.

Data supplied: You are given two data files in CSV: • train_features.csv: Each row represents the metadata for an individual job posting. The “jobId” column represents a unique identifier for the job posting. The remaining columns describe the features of the job posting. • train_salaries.csv: Each row associates a “jobId” with a “salary”. The first row of each file contains headers for the columns. Keep in that the metadata and salary data were crawled from the internet. As such, it’s possible that the data is dirty (it may contain errors).

Questions 1. What steps did you take to prepare the data for the project? Was any cleaning necessary? 2. What algorithmic method did you apply? Why? What other methods did you consider? 3. Describe how the algorithmic method that you chose works? 4. What features did you use? Why? 5. How did you train your model? During training, what issues concerned you? 6. How did you assess the accuracy of your predictions? Why did you choose that method? Would you consider any alternative approaches for assessing accuracy? 7. Which features had the most significant impact on salary? How did you identify these to be most significant? Which features had the least impact on salary? How did you identify these?

This data set represents the data analyzed and discussed in a research article for the Journal of Altmetrics (JOA) of the same title/name. However, the original survey project was commissioned by Virginia Tech Faculty Senate to assess faculty perceptions of research assessment and salary considerations at Virginia Tech. The project was overseen by the Faculty Senate Research Assessment Committee and the resulting report was submitted to Faculty Senate and presented to the Virginia Tech Board of Visitors at their June 2019 meeting. That report can be found at https://bov.vt.edu/assets/Attachment II_Constituent Reports_June 2019.pdf, pages 12-118 (includes survey questions). The data that are available in this data set represent data that were analyzed and included in the JOA research article. Certain data, including responses to questions about salaries, are not included in this data set, because they were not a part of the analysis for the JOA publication. This data set includes four files: the survey instrument (all questions, PDF format), select quantitative responses (.csv), select qualitative responses (.csv), and the codebook for the questions in the response files (.csv). All response data have been anonymized; please see each file for more details. Racial data and departmental data are eliminated to ensure anonymity. The survey was submitted to the Institutional Review Board of Virginia Tech and was determined to not be research involving human subjects as defined by HHS and FDA regulations, resulting in approval to broadly distribute the survey to Virginia Tech faculty with expectations that responses are kept anonymous and results do not claim to be generalizable knowledge (IRB reference number 19-234). The authors would like to thank Dr. Ivica Ico Bukvic, who provided us with a survey instrument used within the School of Performing Arts (SOPA) at Virginia Tech to determine the types of research and creative works SOPA faculty produce and which indicators they prefer for scholarly evaluation; this project's survey instrument was based in-part on the SOPA survey instrument.

Graduate nominal salaries for those of working age and the young population by gender and industry in 2023.

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

This file contains detailed information about data professionals, including their salaries, designations, departments, and more. The data can be used for salary prediction, trend analysis, and HR analytics.

Column Descriptors

FIRST NAME: First name of the data professional (String)

LAST NAME: Last name of the data professional (String)

SEX: Gender of the data professional (String: 'F' for Female, 'M' for Male)

DOJ (Date of Joining): The date when the data professional joined the company (Date in MM/DD/YYYY format)

CURRENT DATE: The current date or the snapshot date of the data (Date in MM/DD/YYYY format)

DESIGNATION: The job role or designation of the data professional (String: e.g., Analyst, Senior Analyst, Manager)

AGE: Age of the data professional (Integer)

SALARY: Annual salary of the data professional (Float)

UNIT: Business unit or department the data professional works in (String: e.g., IT, Finance, Marketing)

LEAVES USED: Number of leaves used by the data professional (Integer)

LEAVES REMAINING: Number of leaves remaining for the data professional (Integer)

RATINGS: Performance ratings of the data professional (Float)

PAST EXP: Past work experience in years before joining the current company (Float)

Provenance

Data Collection:

• The dataset was compiled from internal HR records of a hypothetical company.
• Each record represents a unique data professional with various attributes collected from their employment history.
• The data spans from 2009 to 2016, capturing a snapshot as of January 7, 2016.

Data Organization:

• The data has been organized chronologically by the date of joining (DOJ).
• Each row represents an individual data professional.
• Various attributes such as designation, department, and performance ratings have been included to enable comprehensive analysis.

This dataset is accessed from https://www.kaggle.com/jessemostipak/college-tuition-diversity-and-pay and was downloaded on August 4, 2021.

The following excerpt is from Kaggle regarding the sources of this dataset:

The data this week comes from many different sources but originally came from the US Department of Education.

Tuition and fees by college/university for 2018-2019, along with school type, degree length, state, in-state vs out-of-state from the Chronicle of Higher Education. Diversity by college/university for 2014, along with school type, degree length, state, in-state vs out-of-state from the Chronicle of Higher Education. Example diversity graphics from Priceonomics. Average net cost by income bracket from TuitionTracker.org. Example price trend and graduation rates from TuitionTracker.org Salary potential data comes from payscale.com.

This dataset included the following files:

1. diversity_school.csv

2. historical_tuition.csv

3. salary_potential.csv

4. tuition_cost.csv

5. tuition_income.csv

After data cleaning, the data in diversity_school.csv and tuition_cost.csv were merged and the data in salary_potential.csv and tuition_income.csv were merged. The combined datasets were then split based on the US Census Regions into West, Midwest, Northeast and South (https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf).

Median nominal and real salaries by different demographics time series 2007 - 2022(By gender, age group, and graduate type)

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Is the Gini Coefficient Enough? A Microeconomic Data Decomposition StudyIvan Skliarov, Lukasz Goczek (2023).List of data files:1. theil_raw.csv - data obtained from LISSY using the lis_theil.R script.*2. scv_raw.csv - data obtained from LISSY using the scv_theil.R script.*3. hdi.csv - Human Development Index and its components.4. gini.csv - Gini coefficient from SWIID 9.4.5. wdi.csv - World Development Indicators from the World Bank.6. wgi.csv - World Governance Indicators from the World Bank.7. govcon.csv - government consumption (% of GDP) from UNCTAD.8. theil_fin.csv - final dataset (1, 3-7 combined), which is used in lis_analysis.do.9. scv_fin.csv - final dataset (2-7 combined), which is used in lis_analysis.do.10. indexes.csv - only within and between-cohort components of the Theil index and SCV with imputed values (see lis_analysis.do) for Georgia and Lithuania, which is used in lis_plot.R. * LISSY is the remote-execution system allowing access to the Luxembourg Income Study database: https://www.lisdatacenter.org/data-access/lissy/.For questions about this research please contact:Ivan Skliarov, MA: Faculty of Economic Sciences, University of Warsaw, Poland, Długa 44/50, Warsaw 00-241, Poland, i.skliarov@student.uw.edu.pl.Lukasz Goczek, PhD: Faculty of Economic Sciences, University of Warsaw, Poland, Długa 44/50, Warsaw 00-241, Poland, lgoczek@wne.uw.edu.pl.

Organogram (organisation chart) showing all staff roles. Names and salaries are also listed for senior civil servants. Organogram data is released by all central government departments and their agencies since 2010. Snapshots for 31 March and 30 September are published by 6 June and 6 December each year. The published data is validated and released in CSV format and under Open Government Licence (OGL) for reuse. Not all staff are listed within a Senior Civil Service (SCS1) deputy director led team. These staff are listed on the final row of the transparency return. Staff may not have an allocated SCS1 team lead due to a number of reasons, which could include the deputy director post being vacant, the member of staff being new, their record being processed, and so on. Additionally, some staff have a nil salary due to being in unpaid positions or being 'free loans' from other government departments.