This dataset was obtained from the Google Jobs API through serpAPI and contains information about job offers for data scientists in companies based in the United States of America (USA). The data may include details such as job title, company name, location, job description, salary range, and other relevant information. The dataset is likely to be valuable for individuals seeking to understand the job market for data scientists in the USA and for companies looking to recruit data scientists. It may also be useful for researchers who are interested in exploring trends and patterns in the job market for data scientists. The data should be used with caution, as the API source may not cover all job offers in the USA and the information provided by the companies may not always be accurate or up-to-date.

The dataset contains information about job detailed discription.The dataset is impure and anyone could use their data science skill to make prediction such as salary and job title.

Dataset

This dataset was created by Yong Kang Chia

Contents

Analysis of ‘Job Classification Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/HRAnalyticRepository/job-classification-dataset on 30 September 2021.

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

Context

This is a dataset containing some fictional job class specs information. Typically job class specs have information which characterize the job class- its features, and a label- in this case a pay grade - something to predict that the features are related to.

Content

The data is a static snapshot. The contents are ID column - a sequential number Job Family ID Job Family Description Job Class ID Job Class Description PayGrade- numeric Education Level Experience Organizational Impact Problem Solving Supervision Contact Level Financial Budget PG- Alpha label for PayGrade

Acknowledgements

This data is purely fictional

Inspiration

The intent is to use machine learning classification algorithms to predict PG from Educational level through to Financial budget information.

Typically job classification in HR is time consuming and cumbersome as a manual activity. The intent is to show how machine learning and People Analytics can be brought to bear on this task.

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

This dataset contains a curated list of exclusively 10k fake job postings, intended to assist researchers, data scientists, and analysts in studying fraudulent recruitment patterns and scam tactics. By focusing solely on fake job listings, the dataset provides an opportunity to:

1. Develop and test machine learning models to detect fraudulent job postings.
2. Understand the linguistic and structural characteristics of fake job advertisements.
3. Investigate the impact of fake job postings on job seekers and recruitment platforms.

The dataset includes attributes such as job title, company name, location, and detailed descriptions of fake postings. It is ideal for analyzing how scammers operate in the digital recruitment space and for building tools to combat online employment scams.

Note: This dataset does not include real job postings, and hence, it cannot be used for general job market analysis. It is intended solely for studying fraudulent recruitment practices.

Dataset

This dataset was created by Ahmad Dawood Akram

Released under Apache 2.0

Contents

This dataset contains a collection of job listings from different sources, covering various industries and job roles and also tells whether the job posting is fake or not. It provides information such as job titles, locations, descriptions, requirements, and other relevant details. The dataset can be useful for analyzing job market trends, studying job descriptions, or building natural language processing models for job-related tasks.

This dataset of soft skill phrases and their clusters was obtained based on the semi-automated approach using job-descriptions from Armenian job postings from 2004-2015. https://www.kaggle.com/madhab/jobposts

Analysis of ‘IT job vacancies and requirements’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/prabinraj/it-job-vacancies-and-requirements on 28 January 2022.

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

The data

This dataset contains data about jobs taken from https://jobs.prathidhwani.org/jobs on Jan 20, 2021 The selenium package in python is used to extract data from the website

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

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 ---

Analysis of ‘HR data, Predict changing jobs (competition form)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kukuroo3/hr-data-predict-change-jobscompetition-form on 28 January 2022.

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

Context This dataset was taken from link and separated into competition format. The label for the test data is provided in the form of a function.

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

Dataset

This dataset was created by Koti4878.M

Contents

Job Offers Web Scraping Search

Targeted Results to Find the Optimal Work Solution

By [source]

About this dataset

This dataset collects job offers from web scraping which are filtered according to specific keywords, locations and times. This data gives users rich and precise search capabilities to uncover the best working solution for them. With the information collected, users can explore options that match with their personal situation, skillset and preferences in terms of location and schedule. The columns provide detailed information around job titles, employer names, locations, time frames as well as other necessary parameters so you can make a smart choice for your next career opportunity

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is a great resource for those looking to find an optimal work solution based on keywords, location and time parameters. With this information, users can quickly and easily search through job offers that best fit their needs. Here are some tips on how to use this dataset to its fullest potential:

• Start by identifying what type of job offer you want to find. The keyword column will help you narrow down your search by allowing you to search for job postings that contain the word or phrase you are looking for.

• Next, consider where the job is located – the Location column tells you where in the world each posting is from so make sure it’s somewhere that suits your needs!

• Finally, consider when the position is available – look at the Time frame column which gives an indication of when each posting was made as well as if it’s a full-time/ part-time role or even if it’s a casual/temporary position from day one so make sure it meets your requirements first before applying!

• Additionally, if details such as hours per week or further schedule information are important criteria then there is also info provided under Horari and Temps Oferta columns too! Now that all three criteria have been ticked off - key words, location and time frame - then take a look at Empresa (Company Name) and Nom_Oferta (Post Name) columns too in order to get an idea of who will be employing you should you land the gig!

  All these pieces of data put together should give any motivated individual all they need in order to seek out an optimal work solution - keep hunting good luck!

Research Ideas

• Machine learning can be used to groups job offers in order to facilitate the identification of similarities and differences between them. This could allow users to specifically target their search for a work solution.
• The data can be used to compare job offerings across different areas or types of jobs, enabling users to make better informed decisions in terms of their career options and goals.
• It may also provide an insight into the local job market, enabling companies and employers to identify where there is potential for new opportunities or possible trends that simply may have previously gone unnoticed

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: web_scraping_information_offers.csv | Column name | Description | |:-----------------|:------------------------------------| | Nom_Oferta | Name of the job offer. (String) | | Empresa | Company offering the job. (String) | | Ubicació | Location of the job offer. (String) | | Temps_Oferta | Time of the job offer. (String) | | Horari | Schedule of the job offer. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Analysis of ‘NBA Players Career Duration’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sveneschlbeck/nba-players-career-duration on 28 January 2022.

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

Context

In terms of competitiveness, work ethics and training mentality, few leagues worldwide are as hard as the National Basketball Association. If a Rookie (new player) is successful or not depends on many variables - especially on his performance in the first season. Sometimes, it is possible to use statistics about such players to predict wheter they will last 5 years in the NBA or not.

Content

The tabular data contains 22 columns, all regarding a player's performance records such as e.g. the number of 3 Points made.

Analysis

Take a look at the notebook "nba-players" to get started on how to transform, analyse or visualize the data. Interesting questions to answer might be: - Statistics about NBA Rookies (Percentage of Goal types, Number of played Games, etc.) - Statistics about NBA Games/Seasons (Average Rookie Performance, etc.) - Machine Learning models predicting a Player's Career Duration of more than 5 years (binary) or the probability therefore (Proba Prediction)

Data Source

https://data.world/exercises/logistic-regression-exercise-1

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

Analysis of ‘Heart Attack Analysis & Prediction Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rashikrahmanpritom/heart-attack-analysis-prediction-dataset on 13 February 2022.

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

Hone your analytical and ML skills by participating in tasks of my other dataset's. Given below.

Data Science Job Posting on Glassdoor

Groceries dataset for Market Basket Analysis(MBA)

Dataset for Facial recognition using ML approach

Covid_w/wo_Pneumonia Chest Xray

Disney Movies 1937-2016 Gross Income

Bollywood Movie data from 2000 to 2019

17.7K English song data from 2008-2017

About this dataset

• Age : Age of the patient

• Sex : Sex of the patient

• exang: exercise induced angina (1 = yes; 0 = no)

• ca: number of major vessels (0-3)

• cp : Chest Pain type chest pain type

  • Value 1: typical angina
  • Value 2: atypical angina
  • Value 3: non-anginal pain
  • Value 4: asymptomatic

• trtbps : resting blood pressure (in mm Hg)

• chol : cholestoral in mg/dl fetched via BMI sensor

• fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)

• rest_ecg : resting electrocardiographic results

  • Value 0: normal
  • Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
  • Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria

• thalach : maximum heart rate achieved

• target : 0= less chance of heart attack 1= more chance of heart attack

n

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

The Data Science job market has been expanding rapidly over the past few years, and projections for 2025 indicate that this growth will continue at an impressive pace. This dataset contains over 7,000 job opportunities in 2025, mainly gathered from India. However, it provides valuable insights into the skills in demand globally.

This dataset offers real-world insights into the latest in-demand skills such as Python, SQL, machine learning, and AI, helping data scientists navigate the evolving job market. It highlights key job trends, market-demanded skills, and location-based opportunities.

** If you find this dataset helpful, please don't forget to upvote **

Dataset Attributes:

Job Title: The position being offered (e.g., Data Scientist, Data Analyst). Company Name: The name of the hiring company. Location: Geographical location of the job (e.g., Chennai, Bengaluru). Experience: The required years of experience (e.g., 0-1 Years, 2-5 Years). Job Description: A brief description of the job role and responsibilities. Skills: The key technical and soft skills required for the job (e.g., Python, SQL, Machine Learning). Job Post Day: The date when the job was posted.

Analysis of ‘🪜 What corporations talk about on social media?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/corporate-messaginge on 13 February 2022.

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

About this dataset

A data categorization job concerning what corporations actually talk about on social media. Contributors were asked to classify statements as information (objective statements about the company or it's activities), dialog (replies to users, etc.), or action (messages that ask for votes or ask users to click on links, etc.). Added: February 14, 2015 by CrowdFlower | Data Rows: 3118 Download Now

Source: https://www.crowdflower.com/data-for-everyone/

This dataset was created by CrowdFlower and contains around 3000 samples along with Text, Unit State, technical information and other features such as: - Id - Golden - and more.

How to use this dataset

• Analyze Category Gold in relation to Last Judgment At
• Study the influence of Screenname on Trusted Judgments
• More datasets

Acknowledgements

If you use this dataset in your research, please credit CrowdFlower

Start A New Notebook!

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

Analysis of ‘Housing in London’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/justinas/housing-in-london on 28 January 2022.

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

Update 29-04-2020: The data is now split into two files based on the variable collection frequency (monthly and yearly). Additional variables added: area size in hectares, number of jobs in the area, number of people living in the area.

Context

I have been inspired by Xavier and his work on Barcelona to explore the city of London! 🇬🇧 💂

Content

The datasets is primarily centered around the housing market of London. However, it contains a lot of additional relevant data: - Monthly average house prices - Yearly number of houses - Yearly number of houses sold - Yearly percentage of households that recycle - Yearly life satisfaction - Yearly median salary of the residents of the area - Yearly mean salary of the residents of the area - Monthly number of crimes committed - Yearly number of jobs - Yearly number of people living in the area - Area size in hectares

The data is split by areas of London called boroughs (a flag exists to identify these), but some of the variables have other geographical UK regions for reference (like England, North East, etc.). There have been no changes made to the data except for melting it into a long format from the original tables.

Acknowledgements

The data has been extracted from London Datastore. It is released under UK Open Government License v2 and v3. The underlining datasets can be found here: https://data.london.gov.uk/dataset/uk-house-price-index https://data.london.gov.uk/dataset/number-and-density-of-dwellings-by-borough https://data.london.gov.uk/dataset/subjective-personal-well-being-borough https://data.london.gov.uk/dataset/household-waste-recycling-rates-borough https://data.london.gov.uk/dataset/earnings-place-residence-borough https://data.london.gov.uk/dataset/recorded_crime_summary https://data.london.gov.uk/dataset/jobs-and-job-density-borough https://data.london.gov.uk/dataset/ons-mid-year-population-estimates-custom-age-tables

Cover photo by Frans Ruiter from Unsplash

Inspiration

The dataset lends itself for extensive exploratory data analysis. It could also be a great supervised learning regression problem to predict house price changes of different boroughs over time.

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

Dataset

This dataset was created by Mukund

Released under CC0: Public Domain

Contents

It contains the following files:

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

Splash banner

Photo by Chris Liverani on Unsplash

Splash Icon

Icon by Eucalyp available on flaticon.com