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.

The average annual salary of a Data Architect in India was estimated to be over *********** Indian rupees per annum, the highest among other jobs in the Data Science sector in India. It was followed by data Scientist and Database Developer roles.

Dataset

This dataset was created by SRF

Contents

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.

Dataset

This dataset was created by Prithviraj

Contents

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

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

H-1B visa sponsorship trends for 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.

This dataset contains the salaries of Data Science Professionals for year 2020 and 2021.

About 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.

salaryinusd : 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

Explore the progression of average salaries for graduates in Applied 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 Applied Data Science relative to other fields. This data is essential for students assessing the return on investment of their education in Applied Data Science, providing a clear picture of financial prospects post-graduation.

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.

razvanbyrne/Data-Science-Salaries dataset hosted on Hugging Face and contributed by the HF Datasets community

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

US Adult Census data relating income to social factors such as Age, Education, race etc.

The Us Adult income dataset was extracted by Barry Becker from the 1994 US Census Database. The data set consists of anonymous information such as occupation, age, native country, race, capital gain, capital loss, education, work class and more. Each row is labelled as either having a salary greater than ">50K" or "<=50K".

This Data set is split into two CSV files, named adult-training.txt and adult-test.txt.

The goal here is to train a binary classifier on the training dataset to predict the column income_bracket which has two possible values ">50K" and "<=50K" and evaluate the accuracy of the classifier with the test dataset.

Note that the dataset is made up of categorical and continuous features. It also contains missing values The categorical columns are: workclass, education, marital_status, occupation, relationship, race, gender, native_country

The continuous columns are: age, education_num, capital_gain, capital_loss, hours_per_week

This Dataset was obtained from the UCI repository, it can be found on

https://archive.ics.uci.edu/ml/datasets/census+income, http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/

USAGE This dataset is well suited to developing and testing wide linear classifiers, deep neutral network classifiers and a combination of both. For more info on Combined Deep and Wide Model classifiers, refer to the Research Paper by Google https://arxiv.org/abs/1606.07792

Refer to this kernel for sample usage : https://www.kaggle.com/johnolafenwa/wage-prediction

Complete Tutorial is available from http://johnolafenwa.blogspot.com.ng/2017/07/machine-learning-tutorial-1-wage.html?m=1

Dataset

This dataset was created by Khalid Ameen

Released under Apache 2.0

Contents

Dataset

This dataset was created by Manju C

Contents

Dataset

This dataset was created by Lorthar Amissah Quansah

Released under Apache 2.0

Contents

Dataset

This dataset was created by Arpita Gupta

Released under Apache 2.0

Contents

Graph and download economic data for Employed full time: Wage and salary workers: Computer scientists and systems analysts occupations: 16 years and over (LEU0254477000A) from 2000 to 2010 about analysts, computers, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

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