India Tech Career Intelligence [1M] is a large-scale dataset containing 1,000,000 standardized records of technology jobs and internships across India.

It is designed for Data Science, Machine Learning, Data Analytics, Career Intelligence Research, Educational Projects, and Labor Market Analysis.

Key Features: - 1,000,000 Records - 33 Structured Features - Jobs & Internships - Salary & Stipend Information - Company Classification - Geographic Distribution - Work Mode Analysis - Skills & Hiring Trends

Domains: AI, ML, Data Science, Web Development, Cybersecurity, Cloud Computing, DevOps, Software Engineering

Use Cases: Salary prediction, hiring trend analysis, dashboards, ML models, EDA, research

Data is aggregated from publicly available sources and standardized for educational and analytical use.

Founded in 2021, DataThick operates in the AI sector offering a data community for data professionals that focuses on data insight and artificial intelligence among various tools and technologies. The platform provides resources and learning opportunities in data science, analytics, business intelligence, and machine learning. It also includes practical elements such as real-time projects, interview preparation, and resume building. DataThick aims to cater to a diverse audience, welcoming experts from multiple fields including AI, robotics, and aviation. The community addresses emerging trends in IT such as big data, blockchain, and cyber security.

AI Job Market Analytics Dataset

About Dataset

The AI Job Market Analytics Dataset is a large-scale synthetic dataset containing 200,000 job records designed to represent the rapidly evolving artificial intelligence and machine learning employment landscape. The dataset combines job postings, compensation information, candidate requirements, hiring metrics, remote work opportunities, and AI-specific skill requirements to provide a comprehensive view of the modern AI workforce.

As artificial intelligence continues to transform industries worldwide, demand for skilled professionals in machine learning, data science, generative AI, natural language processing, computer vision, and MLOps has grown significantly. Organizations are actively competing for talent with specialized technical expertise, making workforce analytics an increasingly important area of research and business intelligence.

This dataset provides realistic job market data across multiple countries, industries, and company types, enabling users to explore salary trends, hiring patterns, skill demand, recruitment efficiency, and workforce planning strategies. It serves as a valuable resource for machine learning projects, labor market research, business analytics, and career-focused data science applications.

Dataset Highlights

• 200,000 AI and machine learning job records

• Global hiring and compensation information

• Salary, bonus, and employment metrics

• AI-specific skills and tool requirements

• Remote work and workforce trend indicators

• Recruitment and hiring performance metrics

• Multiple industries and job categories

• Time-series ready job posting data

Included Data Categories

Job Information

Job titles, employment types, hiring requirements, and posting dates.

Compensation Data

Salary information, bonuses, remote work ratios, and workforce compensation metrics.

Candidate Requirements

Experience levels, educational qualifications, technical skills, AI tools, and specialized expertise requirements.

Hiring Metrics

Application volumes, interview rounds, hiring difficulty scores, and time-to-hire measurements.

AI Skills and Technologies

Machine learning, large language models (LLMs), MLOps, and modern AI tooling requirements.

Geographic Information

Country-level job market information supporting regional workforce analysis.

Potential Applications

• Salary Prediction

• Workforce Analytics

• Hiring Trend Analysis

• Skill Demand Forecasting

• Career Market Research

• Recruitment Analytics

• Employee Demand Modeling

• Business Intelligence Dashboards

• Machine Learning and Data Science Projects

• Labor Market Forecasting

Target Audience

This dataset is suitable for Data Scientists, Machine Learning Engineers, HR Analysts, Business Analysts, Researchers, Students, Recruiters, Workforce Strategists, and professionals interested in understanding trends within the global AI job market.

Educational Value

The dataset can be used for exploratory data analysis, feature engineering, regression modeling, classification tasks, clustering, forecasting, dashboard development, and workforce intelligence research. It provides practical experience working with employment and compensation data while exploring real-world business and labor market challenges.

Dataset Generation

This is a synthetic dataset generated for educational, research, portfolio-building, and machine learning applications. The data was programmatically created to simulate realistic AI job postings, compensation structures, hiring processes, candidate requirements, and workforce trends observed across the technology industry and AI-driven organizations.

Context

COVID-19 pandemic impacted the whole world, overwhelming healthcare systems - unprepared for such intense and lengthy request for ICU beds, professionals, personal protection equipment and healthcare resources. Brazil recorded first COVID-19 case on February 26 and reached community transmission on March 20.

Call to action

There is urgency in obtaining accurate that to better predict and prepare healthcare systems and avoid collapse, defined by above capacity need of ICU beds (assuming human resources, PPE and professionals are available), using individual clinical data - in lieu of epidemiological and populational data. https://img.medscape.com/thumbnail_library/cdc_200313_flatten_the_curve_800x450.jpg" alt="">

Task 01

Predict admission to the ICU of confirmed COVID-19 cases. Based on the data available, is it feasible to predict which patients will need intensive care unit support? The aim is to provide tertiary and quarternary hospitals with the most accurate answer, so ICU resources can be arranged or patient transfer can be scheduled.

Task 02

Predict NOT admission to the ICU of confirmed COVID-19 cases. Based on the subsample of widely available data, is it feasible to predict which patients will need intensive care unit support? The aim is to provide local and temporary hospitals a good enough answer, so frontline physicians can safely discharge and remotely follow up with these patients.

The Data

Label output

ICU should be considered, as the first version of this dataset, the target variable.

Window Concept

We were carefull to include real life cenarios of with window of events and available data. Data was obtain and grouped - patient -- patient encounter -- aggregated by windows in chronological order | Window | Description | |----------|-------------------------------------| | 0-2 | From 0 to 2 hours of the admission | | 2-4 | From 2 to 4 hours of the admission | | 4-6 | From 4 to 6 hours of the admission | | 6-12 | From 6 to 12 hours of the admission | | Above-12 | Above 12 hours from admission |

• Beware NOT to use the data when the target variable is present, as it is unknown the order of the event (maybe the target event happened before the results were obtained). They were kept there so we can grow this dataset in other outcomes latter on.

Examples: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1591620%2Fb1bc424df771a4d2d3b3088606d083e6%2FTimeline%20Example%20Best.png?generation=1594740856017996&alt=media" alt="">

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Dataset

This dataset contains anonymized data from Hospital Sírio-Libanês, São Paulo and Brasilia. All data were anonymized following the best international practices and recommendations. Data has been cleaned and scaled by column according to Min Max Scaler to fit between -1 and 1.

Available data

1. Patient demographic information (03)
2. Patient previous grouped diseases (09)
3. Blood results (36)
4. Vital signs (06)

In total there are 54 features, expanded when pertinent to the mean, median, max, min, diff and relative diff.

1. diff = max - min
2. relative diff = diff/median

Expected submission

Submit a notebook that implements the full lifecycle of data preparation, model creation and evaluation.

Questions and information

Please use the kernels and the discussion, as Sírio-Libanês Data Intelligence Team will be replying to questions. Additional questions, corporate and clarifications: data.intelligence@hsl.org.br

Selected Interesting Kernel

Interesting solutions made public within this dataset competition will be invited to discuss and present their findings and approach to a group of researchers and experts in field along with the data science team of Sírio-Libanês.

FAQ

Data Sharing and Usage

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Tips & Tricks

Missing data

Problem: One of the major challenges of working with health care data is that the sampling rate varies across different type of measurements. For instance, vital signs are sampled more frequently (usually hourly) than blood labs (usually daily).

Tips & Tricks: It is reasonable to assume that a patient who does not have a measurement recorded in a time window is clinically stable, potentially presenting vital signs and blood labs similar to neighboring windows. Therefore, one may fill the missing values u...

snapshot-tab-pane Analytics Market Size 2024-2028The analytics market size is forecast to increase by USD 286.5 billion at a CAGR of 15.06% between 2023 and 2028.The market is experiencing significant growth due to several key factors. The increasing availability and complexity of data are driving market expansion, as organizations seek to gain insights from their data to make informed business decisions. Additionally, advances in natural language processing (NLP), machine learning, and artificial intelligence (AI) technologies are enabling more sophisticated data analysis and prediction capabilities. However, data privacy and security concerns remain a challenge, as organizations must ensure the confidentiality, integrity, and availability of their data. Overall, these trends and challenges are shaping the market and presenting opportunities for innovation and growth.What will be the Size of the Analytics Market During the Forecast Period? Request Free SampleThe market is experiencing significant modernization, driven by the adoption of advanced technologies such as AI and orchestration. Discovery tools and innovation in manufacturing, healthcare, and science are catalyzing the need for more robust analytics solutions. Governance frameworks, privacy regulations, and security best practices are becoming essential components of the analytics landscape. Manufacturing and retail industries are leveraging advanced analytics tools for quality management, lifecycle management, and pipeline optimization. In healthcare, analytics is playing a crucial role in vaccine research and patient care, while in finance, predictive modeling and financial analysis are driving business decision-making. Businesses across industries are turning to advanced analytics platforms for customer relationship management, sales intelligence, and customer segmentation.AI adoption is enabling personalized experiences, while virtualization and cloud-based analytics are streamlining operations. Governance, integrity tools, and migration to advanced analytics management platforms are critical for ensuring data accuracy and security. Advanced analytics is also being used for quality management, predictive modeling, and business process optimization in various sectors. Innovation in visualization tools, cataloging, and storytelling is making data more accessible and actionable for businesses, enabling them to gain valuable insights and make informed decisions. Overall, the market is evolving rapidly, with a focus on innovation, efficiency, and data security.How is this Analytics Industry segmented and which is the largest segment?The analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD billion" for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.Component ServicesSoftwareHardwareEnd-user BSFIManufacturingRetailHealthcareOthersGeography North America USEurope GermanyUKAPAC ChinaIndiaMiddle East and Africa South America By Component InsightsThe services segment is estimated to witness significant growth during the forecast period.In today's business landscape, analytics plays a pivotal role in driving growth and gaining a competitive edge. companies cater to diverse industry needs by providing customized analytics services, including AI, ETL, social media, meta discovery, hybrid multi-cloud, smart retail, proteins, governance, decision-making, self-service consumption, healthcare, scalability, integration, fabrics, customer experience, sensors, real-time healthcare, IoT solutions, active meta discovery, immersive gaming, analytics apps, digital experience, outbreaks, processing power, network devices, healthcare analytics capabilities, engineering, self-service consumption, AI solutions, text analytics, AI-powered BI, geospatial analysis, business intelligence, e-commerce, customer analytics, security, cross-product access management, human resource management, product promotions, social media advertising, enterprise resource planning, and geospatial analytics.Service providers enhance data security, discover new revenue streams, transform service support for better productivity, and enable informed business decisions. The increasing competition and innovation necessitate industry-specific, consumer group-specific, and region-specific data analysis, making professional services indispensable.Get a glance at the Analytics Industry report of share of various segments Request Free SampleThe Services segment was valued at USD 114.00 billion in 2018 and showed a gradual increase during the forecast period.Regional AnalysisNorth America is estimated to contribute 30% to the growth of the global market during the forecast period.Technavio's analysts have

Context

In today’s competitive job market, companies receive numerous applications for each job posting, making it challenging to efficiently screen and shortlist candidates. This dataset is designed to facilitate research and development in resume screening, job matching, and recruitment analytics. It can be used to build machine learning models for applicant-job matching, automate resume parsing, and analyze hiring trends.

Dataset Overview

This dataset contains applicant details, resumes, job descriptions, and matching labels to assess how well a candidate fits a specific job role. It can be used to explore factors affecting job selection, identify biases in hiring, and improve applicant tracking systems.

Data Sources & Collection

The dataset was compiled from synthetic and publicly available job application data. It is structured to resemble real-world hiring scenarios, making it useful for data science and HR analytics projects. The resumes and job descriptions are either anonymized, synthesized, or derived from publicly accessible recruitment data.

Columns Description

Job Applicant Name – Full name of the applicant. Age – Applicant’s age. Gender – Applicant’s gender identity. Race – Racial background of the applicant. Ethnicity – Ethnic identity of the applicant. Resume – Text content of the applicant’s resume, including skills, experience, and education. Job Roles – The job positions for which the applicant applied. Job Description – A detailed description of the job role, including required skills, responsibilities, and qualifications. Best Match – A label or score indicating how well the applicant matches the job role based on qualifications and experience.

Inspiration & Use Cases

This dataset is useful for: ✅ Building AI-powered resume-screening models to automate candidate selection. ✅ Developing job recommendation systems that suggest the best roles for applicants. ✅ Analyzing hiring trends & biases in recruitment based on age, gender, or ethnicity. ✅ Training NLP models for resume parsing and job description understanding.

Potential Applications

AI-based Applicant Tracking Systems (ATS) HR Analytics & Hiring Bias Studies Resume-Job Matching Algorithms Data-Driven Career Counseling

🚀 We encourage data scientists, recruiters, and HR tech enthusiasts to explore this dataset and build innovative solutions!

According to Cognitive Market Research, the global SME Big Data market size is USD xx million in 2024. It will expand at a compound annual growth rate (CAGR) of 4.60% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD xx million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.8% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD xx million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD xx million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.6% from 2024 to 2031.
Latin America had a market share for more than 5% of the global revenue with a market size of USD xx million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.0% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD xx million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.3% from 2024 to 2031.
The Software held the highest SME Big Data market revenue share in 2024.

Market Dynamics of SME Big Data Market

Key Drivers of SME Big Data Market

The Necessity of Data-Driven Decision Making : Small and medium-sized enterprises are increasingly implementing big data solutions to derive actionable insights. These tools enhance customer engagement, streamline operations, and bolster competitive strategies within a rapidly changing business landscape.

The Rise of Cloud-Based Analytics : Cloud computing platforms offer SMEs affordable, scalable, and adaptable big data solutions. The ease of deployment and subscription-based pricing models are propelling adoption, particularly among companies with limited IT resources.

The Growth of Digitalization in SMEs : The continuous digital transformation across various sectors is prompting SMEs to adopt big data technologies. Improved connectivity, the expansion of e-commerce, and the rise of mobile usage are generating substantial amounts of data, thereby increasing the demand for analytics.

Key Restraints in SME Big Data Market

High Costs of Implementation and Maintenance : The use of advanced big data tools frequently entails significant expenses related to setup, customization, and employee training. These financial challenges hinder adoption among small and medium-sized enterprises (SMEs) with limited budgets.

Lack of Skilled Workforce : Small and medium-sized enterprises encounter challenges in recruiting professionals who possess expertise in data science, analytics, and artificial intelligence. This shortage of talent limits the effective use of big data technologies.

Challenges in Data Security and Compliance : The management of sensitive business and customer data raises issues regarding privacy and adherence to regulatory standards. Insufficient cybersecurity measures prevent SMEs from fully capitalizing on big data platforms.

Key Trends of SME Big Data Market

Integration of AI and Machine Learning : Small and Medium Enterprises (SMEs) are progressively embracing AI-driven big data solutions for purposes such as predictive analytics, automation, and the enhancement of personalized customer experiences. These advanced technologies significantly improve efficiency and the ability to make informed decisions.

Rise of Self-Service Analytics : User-friendly self-service analytics platforms are becoming increasingly popular among SMEs. These platforms enable non-technical users to independently analyze data, thereby decreasing dependence on specialized teams.

Expansion of Industry-Specific Solutions : Vendors are providing customized big data tools tailored for specific sectors, including retail, healthcare, and manufacturing. These bespoke solutions effectively tackle unique challenges, fostering greater adoption across various industries.

Impact of Covid-19 on the SME Big Data Market

The COVID-19 pandemic significantly impacted the SME Big Data market, accelerating digital transformation as businesses sought to adapt to rapidly changing conditions. With disruptions in traditional operations and a shift towards remote work, SMEs increasingly turned to big data analytics to maintain efficiency, manage supply chains, and understand evolving customer behaviors. The pandemic undersc...

Context

The Honeywell Automation Employee Reviews dataset is a collection of valuable insights extracted from employee reviews of Honeywell Automation, a renowned Technology & Innovation company. The dataset offers a unique window into the experiences, sentiments and perspectives of individuals who have worked at Honeywell Automation.

Honeywell Automation

Sources

The dataset was curated by Web Scraping employee reviews from Ambition Box, a platform where employees share their experiences and opinions about their workplaces. The data includes reviews spanning a wide range of topics including work-life balance, career growth, company culture and more.

Inspiration

This dataset was inspired by the desire to better understand the employee experience at Honeywell Automation and to provide a resource for anyone interested in gaining insights into the company's work environment. It serves as a valuable resource for HR professionals, job seekers, researchers and anyone looking to explore the world of Honeywell Automation through the eyes of its employees.

Additionally, The motivation behind curating this dataset is to empower data enthusiasts, NLP researchers, AI developers, and culture analytics enthusiasts to explore the dynamic world of Honeywell Automation through the eyes of its employees. It serves as an invaluable resource for projects aimed at sentiment analysis, language processing and culture analytics.

We hope that this dataset will not only inform but also inspire discussions and analyses that can benefit both current and future members of the Honeywell Automation community, as well as the wider public interested in workplace insights and data-driven exploration.

Context

The Samsung India Electronics Employee Reviews dataset is a collection of valuable insights extracted from employee reviews of Samsung India Electronics, a renowned Consumer Electronics & Appliances company. The dataset offers a unique window into the experiences, sentiments and perspectives of individuals who have worked at Samsung India Electronics.

Samsung India Electronics

Sources

The dataset was curated by Web Scraping employee reviews from Ambition Box, a platform where employees share their experiences and opinions about their workplaces. The data includes reviews spanning a wide range of topics including work-life balance, career growth, company culture and more.

Inspiration

This dataset was inspired by the desire to better understand the employee experience at Samsung India Electronics and to provide a resource for anyone interested in gaining insights into the company's work environment. It serves as a valuable resource for HR professionals, job seekers, researchers and anyone looking to explore the world of Samsung India Electronics through the eyes of its employees.

Additionally, The motivation behind curating this dataset is to empower data enthusiasts, NLP researchers, AI developers, and culture analytics enthusiasts to explore the dynamic world of Samsung India Electronics through the eyes of its employees. It serves as an invaluable resource for projects aimed at sentiment analysis, language processing and culture analytics.

We hope that this dataset will not only inform but also inspire discussions and analyses that can benefit both current and future members of the Samsung India Electronics community, as well as the wider public interested in workplace insights and data-driven exploration.

The Scientific and Technical Publication Market was valued at USD 37.8 Billion in 2025 and is projected to grow to USD 50 Billion by 2035, at a CAGR of 2.8%. Scientific And Technical Publication Market Overview: Scientific and Technical Publication Market Overview: The Scientific and Technical Publication Market Size was valued at 36.7 USD Billion in 2024. The Scientific and Technical Publication Market is expected to grow from 37.8 USD Billion in 2025 to 50 USD Billion by 2035. The Scientific and Technical Publication Market CAGR (growth rate) is expected to be around 2.8% during the forecast period (2025 - 2035). Key Scientific and Technical Publication Market Trends Highlighted The Global Scientific and Technical Publication Market is experiencing significant transformation driven by advancements in digital technologies and the rise of open access publishing. The increasing demand for freely accessible research outputs is reshaping traditional publishing models, leading to more transparency and enhanced collaboration among researchers. Governments and institutions across the globe are encouraging open access initiatives, resulting in a more dynamic and inclusive dissemination of knowledge. Additionally, there is a growing trend toward interdisciplinary research, which fosters innovation and collaboration across multiple scientific fields. Key market drivers include a rising emphasis on research and development, supported by government and private investments aimed at addressing global challenges such as health crises and climate change. The need for timely and relevant scientific information to inform policy decisions further propels the growth of this market. Opportunities lie in leveraging emerging technologies, such as artificial intelligence and data analytics, to enhance the efficiency of peer review processes and improve the visibility of published research. As more researchers and institutions recognize the importance of sharing findings swiftly and broadly, they are likely to explore more dynamic publication platforms that cater to this demand.Recent trends show an increase in the acceptance of preprints and grey literature, reflecting a shift in how research is communicated. Many researchers are now opting to share their findings in a more timely manner, ensuring their work remains relevant in fast-evolving scientific landscapes. This trend indicates a potential for enhanced citation rates and broader engagement with scientific outputs, enabling a more rapid exchange of knowledge on a global scale. As the market evolves, supportive policies and frameworks will be essential in addressing challenges and fostering sustainable growth in the Global Scientific and Technical Publication Market. Source: Primary Research, Secondary Research, WGR Database and Analyst Review Scientific and Technical Publication Market Segment Insights: Scientific and Technical Publication Market Regional Insights In the Global Scientific and Technical Publication Market, North America represents a significant segment, valued at 15 USD Billion in 2024 and projected to reach 19 USD Billion in 2035, thus dominating the regional landscape. This area shows robust market growth, driven by advanced research initiatives and a strong emphasis on innovation. Europe shows steady expansion, supported by substantial investments in education and Research and Development initiatives, fostering a conducive environment for scientific publishing. The APAC region experiences moderate increase, with emerging markets enhancing their publication capabilities and digital adoption further contributing to this growth.Meanwhile, South America and MEA indicate gradual progress, reflecting increased awareness and investment in educational resources and digital infrastructure. The overall market trends highlight a collective push toward transitioning to digital platforms and open access models, presenting opportunities for growth while addressing challenges such as copyright issues and shifts in consumer behavior. Understanding the Global Scientific and Technical Publication Market statistics will be critical for stakeholders

Context

The LG Electronics Employee Reviews dataset is a collection of valuable insights extracted from employee reviews of LG Electronics, a renowned Consumer Electronics & Appliances company. The dataset offers a unique window into the experiences, sentiments and perspectives of individuals who have worked at LG Electronics.

LG Electronics

Sources

The dataset was curated by Web Scraping employee reviews from Ambition Box, a platform where employees share their experiences and opinions about their workplaces. The data includes reviews spanning a wide range of topics including work-life balance, career growth, company culture and more.

Inspiration

This dataset was inspired by the desire to better understand the employee experience at LG Electronics and to provide a resource for anyone interested in gaining insights into the company's work environment. It serves as a valuable resource for HR professionals, job seekers, researchers and anyone looking to explore the world of LG Electronics through the eyes of its employees.

Additionally, The motivation behind curating this dataset is to empower data enthusiasts, NLP researchers, AI developers, and culture analytics enthusiasts to explore the dynamic world of LG Electronics through the eyes of its employees. It serves as an invaluable resource for projects aimed at sentiment analysis, language processing and culture analytics.

We hope that this dataset will not only inform but also inspire discussions and analyses that can benefit both current and future members of the LG Electronics community, as well as the wider public interested in workplace insights and data-driven exploration.

Context

The Havells Employee Reviews dataset is a collection of valuable insights extracted from employee reviews of Havells, a renowned Fast Moving Electrical Good(FMEG) company. The dataset offers a unique window into the experiences, sentiments and perspectives of individuals who have worked at Havells.

Havells

Sources

The dataset was curated by Web Scraping employee reviews from Ambition Box, a platform where employees share their experiences and opinions about their workplaces. The data includes reviews spanning a wide range of topics including work-life balance, career growth, company culture and more.

Inspiration

This dataset was inspired by the desire to better understand the employee experience at Havells and to provide a resource for anyone interested in gaining insights into the company's work environment. It serves as a valuable resource for HR professionals, job seekers, researchers and anyone looking to explore the world of Havells through the eyes of its employees.

Additionally, The motivation behind curating this dataset is to empower data enthusiasts, NLP researchers, AI developers, and culture analytics enthusiasts to explore the dynamic world of Havells through the eyes of its employees. It serves as an invaluable resource for projects aimed at sentiment analysis, language processing and culture analytics.

We hope that this dataset will not only inform but also inspire discussions and analyses that can benefit both current and future members of the Havells community, as well as the wider public interested in workplace insights and data-driven exploration.

Context

The Whirlpool India Employee Reviews dataset is a collection of valuable insights extracted from employee reviews of Whirlpool India, a renowned Consumer Electronics & Appliances company. The dataset offers a unique window into the experiences, sentiments and perspectives of individuals who have worked at Whirlpool India.

Whirlpool India

Sources

The dataset was curated by Web Scraping employee reviews from Ambition Box, a platform where employees share their experiences and opinions about their workplaces. The data includes reviews spanning a wide range of topics including work-life balance, career growth, company culture and more.

Inspiration

This dataset was inspired by the desire to better understand the employee experience at Whirlpool India and to provide a resource for anyone interested in gaining insights into the company's work environment. It serves as a valuable resource for HR professionals, job seekers, researchers and anyone looking to explore the world of Whirlpool India through the eyes of its employees.

Additionally, The motivation behind curating this dataset is to empower data enthusiasts, NLP researchers, AI developers, and culture analytics enthusiasts to explore the dynamic world of Whirlpool India through the eyes of its employees. It serves as an invaluable resource for projects aimed at sentiment analysis, language processing and culture analytics.

We hope that this dataset will not only inform but also inspire discussions and analyses that can benefit both current and future members of the Whirlpool India community, as well as the wider public interested in workplace insights and data-driven exploration.

Context

The Maruti Suzuki Employee Reviews dataset is a collection of valuable insights extracted from employee reviews of Maruti Suzuki, an Indian multinational automotive manufacturing company. The dataset offers a unique window into the experiences, sentiments and perspectives of individuals who have worked at Maruti Suzuki.

Maruti Suzuki

Sources

The dataset was curated by Web Scraping employee reviews from Ambition Box, a platform where employees share their experiences and opinions about their workplaces. The data includes reviews spanning a wide range of topics including work-life balance, career growth, company culture and more.

Inspiration

This dataset was inspired by the desire to better understand the employee experience at Maruti Suzuki and to provide a resource for anyone interested in gaining insights into the company's work environment. It serves as a valuable resource for HR professionals, job seekers, researchers and anyone looking to explore the world of Maruti Suzuki through the eyes of its employees.

Additionally, The motivation behind curating this dataset is to empower data enthusiasts, NLP researchers, AI developers, and culture analytics enthusiasts to explore the dynamic world of Maruti Suzuki through the eyes of its employees. It serves as an invaluable resource for projects aimed at sentiment analysis, language processing and culture analytics.

We hope that this dataset will not only inform but also inspire discussions and analyses that can benefit both current and future members of the Maruti Suzuki community, as well as the wider public interested in workplace insights and data-driven exploration.

Context

The Panasonic India Employee Reviews dataset is a collection of valuable insights extracted from employee reviews of Panasonic India, a renowned Consumer Electronics & Appliances company. The dataset offers a unique window into the experiences, sentiments and perspectives of individuals who have worked at Panasonic India.

Panasonic India

Sources

The dataset was curated by Web Scraping employee reviews from Ambition Box, a platform where employees share their experiences and opinions about their workplaces. The data includes reviews spanning a wide range of topics including work-life balance, career growth, company culture and more.

Inspiration

This dataset was inspired by the desire to better understand the employee experience at Panasonic India and to provide a resource for anyone interested in gaining insights into the company's work environment. It serves as a valuable resource for HR professionals, job seekers, researchers and anyone looking to explore the world of Panasonic India through the eyes of its employees.

Additionally, The motivation behind curating this dataset is to empower data enthusiasts, NLP researchers, AI developers, and culture analytics enthusiasts to explore the dynamic world of Panasonic India through the eyes of its employees. It serves as an invaluable resource for projects aimed at sentiment analysis, language processing and culture analytics.

We hope that this dataset will not only inform but also inspire discussions and analyses that can benefit both current and future members of the Panasonic India community, as well as the wider public interested in workplace insights and data-driven exploration.

Context

The Bajaj Electricals Employee Reviews dataset is a collection of valuable insights extracted from employee reviews of Bajaj Electricals, a renowned Consumer Electronics & Appliances company. The dataset offers a unique window into the experiences, sentiments and perspectives of individuals who have worked at Bajaj Electricals.

Bajaj Electricals

Sources

The dataset was curated by Web Scraping employee reviews from Ambition Box, a platform where employees share their experiences and opinions about their workplaces. The data includes reviews spanning a wide range of topics including work-life balance, career growth, company culture and more.

Inspiration

This dataset was inspired by the desire to better understand the employee experience at Bajaj Electricals and to provide a resource for anyone interested in gaining insights into the company's work environment. It serves as a valuable resource for HR professionals, job seekers, researchers and anyone looking to explore the world of Bajaj Electricals through the eyes of its employees.

Additionally, The motivation behind curating this dataset is to empower data enthusiasts, NLP researchers, AI developers, and culture analytics enthusiasts to explore the dynamic world of Bajaj Electricals through the eyes of its employees. It serves as an invaluable resource for projects aimed at sentiment analysis, language processing and culture analytics.

We hope that this dataset will not only inform but also inspire discussions and analyses that can benefit both current and future members of the Bajaj Electricals community, as well as the wider public interested in workplace insights and data-driven exploration.