Dataset Card for Data Science Job Salaries

  Dataset Summary





  Content

Column Description

work_year The year the salary was paid.

experience_level The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director

employment_type The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance

job_title… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/data-science-job-salaries.

1) Data Introduction • The Data Science Salaries 2023 Dataset is a global annual salary analysis dataset that summarizes a variety of information in a tabular format, including salary, career, employment type, job, remote work rate, and company location and size for data science jobs as of 2023.

2) Data Utilization (1) Data Science Salaries 2023 Dataset has characteristics that: • Each row contains 11 key characteristics, including year, career level, employment type, job name, annual salary (local currency and USD), employee country of residence, remote work rate, company location, and company size. • Data is organized to reflect different countries, jobs, careers, and work patterns to analyze pay and work environments in data science in three dimensions. (2) Data Science Salaries 2023 Dataset can be used to: • Data Science Salary Analysis and Comparison: Analyzing salary levels and distributions by job, career, country, and company size can be used to understand industry trends and market value. • Establishing Recruitment and Career Strategies: It can be applied to recruitment strategies, career development, global talent attraction, etc. by analyzing the correlation between various working conditions and salaries such as remote work rates, employment types, and company location.

What's inside dataset

work_year

The year during which the salary was paid. There are two types of work year values: 2020 Year with a definitive amount from the past 2021e Year with an estimated amount (e.g. current year)

experience_level

The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director

employment_type

The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance

job_title

The role worked in during the year. salary The total gross salary amount paid.

salary_currency

The currency of the salary paid as an ISO 4217 currency code.

salary_in_usd

The salary in USD (FX rate divided by avg. USD rate for the respective year via fxdata.foorilla.com).

employee_residence

Employee's primary country of residence in during the work year as an ISO 3166 country code.

remote_ratio

The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote 100 Fully remote (more than 80%)

company_location

The country of the employer's main office or contracting branch as an ISO 3166 country code.

company_size

The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large)

Dataset Source - ai-jobs.net Salaries

H-1B visa sponsorship trends for Senior Data Scientist, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

Across industries, organizations are increasing their hiring efforts to build larger data science arsenals: from 2020 to 2021, the percentage of surveyed organizations that employed ** data scientists or more increased from ** percent to almost ** percent. On average, the number of data scientists employed in a organization grew from ** to **.

Dataset

This dataset was created by Manju C

Contents

Abstract

Looking for a job as Data Analyst? Maybe this dataset can help you.

About this dataset

Amidst the pandemic many people lost their jobs, with this dataset it is possible to hone the job search so that more people in need can find employment. This dataset was created by picklesueat and contains more than 2000 job listing for data analyst positions, with features such as: - Salary Estimate - Location - Company Rating - Job Description - and more.

How to use

• Find the best jobs by salary and company rating
• Explore skills required in job descriptions
• Predict salary based on industry, location, company revenue
• Your kernel can be featured here!
• Data Engineer Jobs
• Business Analyst Jobs
• Data Scientist Jobs
• More Datasets

Acknowledgements

If you use this dataset, please support the author.

License

License was not specified at the source

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Photo by Chris Liverani on Unsplash

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Dataset

This dataset was created by Shubhranshu Arya

Released under MIT

Contents

Explore the progression of average salaries for graduates in Computer Science (Big Data; Data Science) from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Computer Science (Big Data; Data Science) relative to other fields. This data is essential for students assessing the return on investment of their education in Computer Science (Big Data; Data Science), providing a clear picture of financial prospects post-graduation.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for interdisciplinary data science in the U.S.

Graph and download economic data for Employed full time: Wage and salary workers: Computer scientists and systems analysts occupations: 16 years and over: Women (LEU0254690600A) from 2000 to 2010 about analysts, computers, occupation, females, full-time, salaries, workers, 16 years +, wages, employment, and USA.

Analysis of ‘HR Analytics: Job Change of Data Scientists’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context and Content

A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. Many people signup for their training. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates. Information related to demographics, education, experience are in hands from candidates signup and enrollment.

This dataset designed to understand the factors that lead a person to leave current job for HR researches too. By model(s) that uses the current credentials,demographics,experience data you will predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision.

The whole data divided to train and test . Target isn't included in test but the test target values data file is in hands for related tasks. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target

Note: - The dataset is imbalanced. - Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. - Missing imputation can be a part of your pipeline as well.

# Features #
- enrollee_id : Unique ID for candidate

• city: City code

• city_ development _index : Developement index of the city (scaled)

• gender: Gender of candidate

• relevent_experience: Relevant experience of candidate

• enrolled_university: Type of University course enrolled if any

• education_level: Education level of candidate

• major_discipline :Education major discipline of candidate

• experience: Candidate total experience in years

• company_size: No of employees in current employer's company

• company_type : Type of current employer

• last_new_job: Difference in years between previous job and current job

• training_hours: training hours completed

• target: 0 – Not looking for job change, 1 – Looking for a job change

Inspiration

• Predict the probability of a candidate will work for the company
• Interpret model(s) such a way that illustrate which features affect candidate decision # Please refer to the following task for more details: https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015

--- Original source retains full ownership of the source dataset ---

Graph and download economic data for Employed full time: Wage and salary workers: Natural sciences managers occupations: 16 years and over (LEU0254473800A) from 2000 to 2024 about science, management, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

Explore the progression of average salaries for graduates in Data Science; Computer Science from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Data Science; Computer Science relative to other fields. This data is essential for students assessing the return on investment of their education in Data Science; Computer Science, providing a clear picture of financial prospects post-graduation.

Glassdoor Jobs Data Analysis

Jobs data from Glassdoor.com for a self learning project

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4514483%2F8936fd5d38ecdff76f7a8db6c1bba6f5%2Fwilliam-iven-gcsNOsPEXfs-unsplash.jpg?generation=1602433410514032&alt=media" alt="">

About Glassdoor

Glassdoor is a website where current and former employees anonymously review companies. Glassdoor also allows users to anonymously submit and view salaries as well as search and apply for jobs on its platform.

In 2018, the company was acquired by the Japanese firm, Recruit Holdings, for US$1.2 billion. The company is headquartered in Mill Valley, California, with additional offices in Chicago, Dublin, London, and São Paulo. wikipedia

Content

1. There are 10 columns in the data they are as follows:
2. Job Title : The title of the job which you are applying to.
3. Salary Estimate : Salary estimated of the job posting.
4. Job Description : The job description included skills, requirements, etc.
5. Rating : Rating of the companies job posting is listed.
6. Company Name : Company name.
7. Location : The location the companies job posting is listed.
8. Size : The size of the company's employment.
9. Founded : The company founded year
10. Type of ownership : Type of ownership in which the company.

11. Industry : The industry in which the company works.

12. All the data collected is about 150 job listings for Data Scientist and it's related roles. The job listings are scraped from Glassdoor.com locate in Indonesia on 10 October 2020.

GitHub's Link

Here is my GitHub link: https://github.com/Deff-ux/Scrapping-Glassdoor-Job-Posting-

AbstractThe H1B is an employment-based visa category for temporary foreign workers in the United States. Every year, the US immigration department receives over 200,000 petitions and selects 85,000 applications through a random process and the U.S. employer must submit a petition for an H1B visa to the US immigration department. This is the most common visa status applied to international students once they complete college or higher education and begin working in a full-time position. The project provides essential information on job titles, preferred regions of settlement, foreign applicants and employers' trends for H1B visa application. According to locations, employers, job titles and salary range make up most of the H1B petitions, so different visualization utilizing tools will be used in order to analyze and interpreted in relation to the trends of the H1B visa to provide a recommendation to the applicant. This report is the base of the project for Visualization of Complex Data class at the George Washington University, some examples in this project has an analysis for the different relevant variables (Case Status, Employer Name, SOC name, Job Title, Prevailing Wage, Worksite, and Latitude and Longitude information) from Kaggle and Office of Foreign Labor Certification(OFLC) in order to see the H1B visa changes in the past several decades. Keywords: H1B visa, Data Analysis, Visualization of Complex Data, HTML, JavaScript, CSS, Tableau, D3.jsDatasetThe dataset contains 10 columns and covers a total of 3 million records spanning from 2011-2016. The relevant columns in the dataset include case status, employer name, SOC name, jobe title, full time position, prevailing wage, year, worksite, and latitude and longitude information.Link to dataset: https://www.kaggle.com/nsharan/h-1b-visaLink to dataset(FY2017): https://www.foreignlaborcert.doleta.gov/performancedata.cfmRunning the codeOpen Index.htmlData ProcessingDoing some data preprocessing to transform the raw data into an understandable format.Find and combine any other external datasets to enrich the analysis such as dataset of FY2017.To make appropriated Visualizations, variables should be Developed and compiled into visualization programs.Draw a geo map and scatter plot to compare the fastest growth in fixed value and in percentages.Extract some aspects and analyze the changes in employers’ preference as well as forecasts for the future trends.VisualizationsCombo chart: this chart shows the overall volume of receipts and approvals rate.Scatter plot: scatter plot shows the beneficiary country of birth.Geo map: this map shows All States of H1B petitions filed.Line chart: this chart shows top10 states of H1B petitions filed. Pie chart: this chart shows comparison of Education level and occupations for petitions FY2011 vs FY2017.Tree map: tree map shows overall top employers who submit the greatest number of applications.Side-by-side bar chart: this chart shows overall comparison of Data Scientist and Data Analyst.Highlight table: this table shows mean wage of a Data Scientist and Data Analyst with case status certified.Bubble chart: this chart shows top10 companies for Data Scientist and Data Analyst.Related ResearchThe H-1B Visa Debate, Explained - Harvard Business Reviewhttps://hbr.org/2017/05/the-h-1b-visa-debate-explainedForeign Labor Certification Data Centerhttps://www.foreignlaborcert.doleta.govKey facts about the U.S. H-1B visa programhttp://www.pewresearch.org/fact-tank/2017/04/27/key-facts-about-the-u-s-h-1b-visa-program/H1B visa News and Updates from The Economic Timeshttps://economictimes.indiatimes.com/topic/H1B-visa/newsH-1B visa - Wikipediahttps://en.wikipedia.org/wiki/H-1B_visaKey FindingsFrom the analysis, the government is cutting down the number of approvals for H1B on 2017.In the past decade, due to the nature of demand for high-skilled workers, visa holders have clustered in STEM fields and come mostly from countries in Asia such as China and India.Technical Jobs fill up the majority of Top 10 Jobs among foreign workers such as Computer Systems Analyst and Software Developers.The employers located in the metro areas thrive to find foreign workforce who can fill the technical position that they have in their organization.States like California, New York, Washington, New Jersey, Massachusetts, Illinois, and Texas are the prime location for foreign workers and provide many job opportunities. Top Companies such Infosys, Tata, IBM India that submit most H1B Visa Applications are companies based in India associated with software and IT services.Data Scientist position has experienced an exponential growth in terms of H1B visa applications and jobs are clustered in West region with the highest number.Visualization utilizing programsHTML, JavaScript, CSS, D3.js, Google API, Python, R, and Tableau

Evaluate a natural language code generation model on real data science pedagogical notebooks! Data Science Problems (DSP) includes well-posed data science problems in Markdown along with unit tests to verify correctness and a Docker environment for reproducible execution. About 1/3 of notebooks in this benchmark also include data dependencies, so this benchmark not only can test a model's ability to chain together complex tasks, but also evaluate the solutions on real data! See our paper Training and Evaluating a Jupyter Notebook Data Science Assistant for more details about state of the art results and other properties of the dataset.

Analysis of ‘Salary’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rsadiq/salary on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This is a simple data where I tried to explain simple linear regression in a simplest way. For the beginner who wants to start their machine learning or data science can follow this simple data to understand simple linear regression.

This data consists of salary and years of experience of 35 jobholders. Where I will try to show the relationship between salary and years of experience.

--- Original source retains full ownership of the source dataset ---

Explore the progression of average salaries for graduates in Data Science Education Evaluation from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Data Science Education Evaluation relative to other fields. This data is essential for students assessing the return on investment of their education in Data Science Education Evaluation, providing a clear picture of financial prospects post-graduation.

The role of Data Science and AI for predicting the decline of professionals in the recruitment process: augmenting decision-making in human resources management Features Description: Declined: Variable to be predict, where value 0 means that the candi- date continued in the recruit- ment process until the hiring, and value 1 implies the candi- date’s declination from recruit- ment process. ValueClient: The total amount the customer plan to pay by the hired candidate. The value 0 means that client yet did not define a value to pay the candidate. Values must be greater than or equal to 0. ExtraCost: Extra cost the customer has to pay to hire the candidate. Values must be greater than or equal to 0. ValueResources: Requested value by the candidate to work. The value 0 means that the candidate did not request a salary amount yet an this value will be negotiate later. Values must be greater than or equal to 0. Net: The difference between the “ValueClient”, yearly taxes and “ValueResources”. Negative values mean that the amount the client plans to pay the candidate has not yet been defined and is still open for negotiation. DaysOnContact: Number of days that the candidate is in the “Contact” step of the recruitment process. Values must be greater than or equal to 0. DaysOnInterview: Number of days that the candidate is in the “Interview” step of the recruitment process. Values must be greater than or equal to 0. DaysOnSendCV: Number of days that the candidate is in the “Send CV” step of the recruitment process. Values must be greater than or equal to 0. DaysOnReturn: Number of days that the candidate is in the “Return” step of the recruitment process. Values must be greater than or equal to 0. DaysOnCSchedule: Number of days that the candidate is in the “C. Schedule” step of the recruitment process. Values must be greater than or equal to 0. DaysOnCRealized: Number of days that the candidate is in the “C. Realized” step of the recruitment process. Values must be greater than or equal to 0. ProcessDuration: Duration of entire recruitment process in days. Values must be greater than or equal to 0