The Department of Economic Research Data Index provides information about the economy and labor market in Massachusetts. It includes links to data tools, analytic reports, data visualizations, dashboards and other resources in the following areas:

• Unemployment and Labor Force Data
• Employment Information by Industry
• Employment Information by Occupation
• Information on Massachusetts Employers
• Location Data

The report contains thirteen (13) performance metrics for City's workforce development programs. Each metric can be breakdown by three demographic types (gender, race/ethnicity, and age group) and the program target population (e.g., youth and young adults, NYCHA communities) as well.

This report is a key output of an integrated data system that collects, integrates, and generates disaggregated data by Mayor's Office for Economic Opportunity (NYC Opportunity). Currently, the report is generated by the integrated database incorporating data from 18 workforce development programs managed by 5 City agencies.

There has been no single "workforce development system" in the City of New York. Instead, many discrete public agencies directly manage or fund local partners to deliver a range of different services, sometimes tailored to specific populations. As a result, program data have historically been fragmented as well, making it challenging to develop insights based on a comprehensive picture. To overcome it, NYC Opportunity collects data from 5 City agencies and builds the integrated database, and it begins to build a complete picture of how participants move through the system onto a career pathway.

Each row represents a count of unique individuals for a specific performance metric, program target population, a specific demographic group, and a specific period. For example, if the Metric Value is 2000 with Clients Served (Metric Name), NYCHA Communities (Program Target Population), Asian (Subgroup), and 2019 (Period), you can say that "In 2019, 2,000 Asian individuals participated programs targeting NYCHA communities.

Please refer to the Workforce Data Portal for further data guidance (https://workforcedata.nyc.gov/en/data-guidance), and interactive visualizations for this report (https://workforcedata.nyc.gov/en/common-metrics).

Learning Data Visualization Tools Market Outlook

According to our latest research, the global Learning Data Visualization Tools Market size reached USD 2.8 billion in 2024, demonstrating robust growth driven by the increasing demand for data literacy and analytics skills across various sectors. The market is expected to grow at a CAGR of 13.7% from 2025 to 2033, projecting a value of USD 8.8 billion by 2033. This surge is primarily attributed to the rapid digitization of education and corporate learning environments, the proliferation of big data, and the critical need for interactive, accessible analytical tools to foster effective data comprehension and decision-making.

One of the most significant growth factors for the Learning Data Visualization Tools Market is the widespread integration of data-driven decision-making processes within organizations and educational institutions. As businesses and academic settings increasingly rely on data to guide strategies, there is a parallel surge in the demand for professionals who possess strong data visualization skills. This has led to a marked increase in the adoption of user-friendly data visualization tools such as Tableau, Power BI, and Google Data Studio in both formal education and corporate training programs. The ability of these tools to simplify complex datasets into intuitive visual representations is a key driver, enabling learners to grasp intricate concepts more efficiently and apply them in real-world scenarios.

Technological advancements and the evolution of cloud-based learning platforms have further propelled the market. The shift toward digital and remote learning, especially post-pandemic, has accelerated the adoption of cloud-based data visualization tools, which offer scalability, accessibility, and seamless integration with other e-learning resources. Cloud deployment eliminates geographical barriers, allowing learners and organizations from diverse regions to access advanced visualization tools and resources at any time. Additionally, the increasing availability of free and open-source visualization libraries such as D3.js has democratized access to these technologies, further expanding the market’s reach across different socioeconomic segments.

Another crucial growth driver is the rising emphasis on upskilling and reskilling initiatives across industries. As automation and artificial intelligence reshape job requirements, data literacy has become a fundamental skill for both students and working professionals. Enterprises are investing heavily in learning platforms that incorporate data visualization tools to train their workforce, ensuring they remain competitive in the digital economy. The trend is mirrored in higher education, where curricula are being revamped to include data visualization modules, reflecting the growing recognition of its importance in fostering analytical and critical thinking skills among learners.

From a regional perspective, North America dominates the Learning Data Visualization Tools Market, accounting for the largest revenue share in 2024. This can be attributed to the presence of leading technology providers, a mature e-learning ecosystem, and high levels of digital adoption in both educational and corporate sectors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, government initiatives to enhance digital literacy, and the increasing penetration of internet and mobile devices. Europe also contributes significantly, with a strong focus on educational innovation and enterprise training. These regional dynamics are shaping the competitive landscape and driving the global expansion of learning data visualization tools.

Tool Type Analysis

The Tool Type segment of the Learning Data Visualization Tools Market is highly diverse, encompassing established platforms like Tableau, Power BI, and Qlik, as well as newer entrants such as Google Data Studio and open-source solutions like D3.js. Tableau remains a market leader due to its intuitive drag-and-drop interface, robust analytics capabilities, and widespread adoption in both academic and corporate settings. Its ability to handle large datasets and integrate seamlessly with various data sources makes it a preferred choice for institutions aiming to provide hands-on, practical training in data visualization. Power BI, backed by Microsoft’s ecosystem, is gaining significant traction, particularly among enterpr

The U.S. Bureau of Economic Analysis’ Total Full-Time and Part-Time Employment data provides one of the most comprehensive, publicly available accountings of average annual employment. Beyond full- and part-time employment types, it includes farm employment and other sectors that aren’t always included in other sources, such as Public Administration (with more detail of federal than state and local employment in this category). It also includes and distinguishes both Wage and Salary employees from Proprietors who own their own unincorporated businesses and handle taxation chiefly as personal income. Proprietors tend to be single-person or small businesses and can include construction or repair workers, babysitters, ride-share drivers, artists, local grocers, housekeepers, various freelancers and consultants, and some attorneys and doctors.

This dataset provides unemployment rates for all countries from 1991 to 2023, based on modeled estimates from the International Labour Organization (ILO). It offers insights into global labor trends, regional employment challenges, and economic shifts across decades.

This dataset is particularly useful for economic research, labor market analysis, trend forecasting, and policy evaluation.

Data Source Source: https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS?end=2023&start=2023&view=map&year=2015 Official Website: ILOSTAT Database Time Period: 1991–2023 Geographical Coverage: All countries and regional aggregates

Dataset Features The dataset includes the following columns:

Country Name: Name of the country or region Country Code: ISO3 country code Indicator Name: Name of the labor market indicator Indicator Code: Unique code for the indicator Yearly Unemployment Data (1991–2023): Percentage of the labor force that is unemployed for each year

This dataset provides a detailed view of South Asian countries' socio-economic, environmental, and governance metrics from 2000 to 2023. It compiles key indicators like GDP, unemployment, literacy rates, energy use, governance measures, and more to facilitate a comprehensive analysis of each country’s growth, stability, and development trends over the years. The data covers Bangladesh, Bhutan, India, Pakistan, Nepal, Sri Lanka, Afghanistan, and Maldives.

Key Indicators Economic Metrics: Includes GDP (both total and per capita in USD), annual GDP growth rates, inflation, and foreign direct investment. These metrics offer insight into economic health, growth rate, and international investment trends across the region. Employment and Trade: Tracks unemployment rates as a percentage of the labor force and trade (as a percentage of GDP), helping assess workforce stability and international commerce engagement. Income and Poverty: Features the Gini index (for income inequality) and poverty headcount ratio at $2.15/day, showing income distribution and poverty levels. These indicators reveal disparities and poverty within each country. Population Statistics: Includes total population, annual population growth, and urban population percentage, capturing demographic trends and urbanization rates. Social Indicators: Covers literacy rates, school enrollment in primary education, life expectancy at birth, infant mortality rates, and access to electricity, basic water, and sanitation services. These data points help measure the population’s health, education levels, and access to essential services. Environmental and Energy Metrics: Tracks CO2 emissions, PM2.5 air pollution, renewable energy consumption, and forest area. This environmental data is crucial for analyzing air quality, sustainable energy use, and forest coverage trends. Governance Indicators: Includes metrics such as control of corruption, political stability, regulatory quality, rule of law, and voice and accountability. These indicators reflect each country’s governance quality and institutional stability. Digital and Technological Growth: Measures internet usage rates, research and development spending, and high-technology exports. These statistics indicate digital access, innovation, and technological progress. This dataset, sourced from the World Bank DataBank, provides a robust foundation for studying South Asia's socio-economic, environmental, and governance progress. By analyzing these diverse indicators, researchers and policymakers can gain a deeper understanding of the region’s development path and identify areas that need improvement.

The global Labor Market Intelligence Platform market is experiencing robust growth, driven by increasing demand for data-driven decision-making within HR and talent acquisition departments. The market, estimated at $5 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of advanced analytics and AI-powered tools is enhancing the ability of organizations to predict future labor market trends, optimize recruitment strategies, and improve workforce planning. Secondly, the increasing complexity of the global talent landscape, coupled with skill shortages in various sectors, necessitates sophisticated platforms for comprehensive labor market analysis. Finally, the growing need for compliance with labor regulations and effective diversity, equity, and inclusion (DE&I) initiatives further drives the adoption of these platforms. Leading players like LinkedIn, Lightcast, and others are actively innovating to meet these evolving demands, expanding their platform functionalities to incorporate advanced features such as predictive modeling and real-time data visualization. The market's segmentation reveals a diverse landscape of platform types, catering to specific needs across various industries and company sizes. While detailed segment-specific data isn't provided, a logical assumption considering the market's drivers would suggest robust growth in segments focused on AI-powered predictive analytics, integration with applicant tracking systems (ATS), and specialized solutions tailored to specific industries (e.g., healthcare, technology). Geographical segmentation is expected to show strong growth across North America and Europe, driven by early adoption and advanced technological infrastructure. However, emerging markets in Asia-Pacific are poised for significant expansion in the coming years, driven by increasing digitalization and economic growth. While challenges exist such as data privacy concerns and the need for robust data integration capabilities, the overall market outlook remains optimistic, promising continued growth and innovation in the years to come.

I want to understand the effect of liberalisation, privatisation and globalisation in Indian lifestyle and economy for last 26 years.

This file contains critical economic indicators (Employment, Unemployment, Labor force etc.) and some social indicators (Population, birth rate, death rate etc.) of India since the inception of liberalisation, privatisation and globalisation in 1991 till 2016.

Raw data is taken from World Bank site and used under their license. Data Cleaning is completely done by me.

Employee Data Analysis This dataset contains detailed information on employees across various departments and countries, capturing key aspects of their employment and performance metrics. It can be used for various HR analytics tasks, such as analyzing salary trends, studying the impact of leaves on productivity, or predicting employee turnover.

Dataset Features: - No: Unique identifier for each employee. - First Name: The employee's first name. - Last Name: The employee's last name. - Gender: Gender of the employee (Male/Female). - Start Date: The date when the employee started working in the company. - Years: The number of years the employee has been with the company. - Department: The department in which the employee works. - Country: The country where the employee is located. - Center: The center (region or office) where the employee is based. - Monthly Salary: The employee's monthly salary in USD. - Annual Salary: The employee's annual salary in USD. - Job Rate: A performance rating or job rate on a scale (details to be specified if available). - Sick Leaves: The number of sick leaves taken by the employee. - Unpaid Leaves: The number of unpaid leaves taken by the employee. - Overtime Hours: The total number of overtime hours worked by the employee.

Potential Use Cases: - Salary Analysis: Investigating how salaries differ across departments, countries, or gender. - Performance Insights: Analyzing job rates and correlating them with other factors like overtime hours or years of service. - Leave Management: Understanding patterns in sick and unpaid leaves and their impact on employee performance. - Employee Retention: Predictive modeling to forecast employee turnover based on historical data.

This dataset is a rich resource for anyone interested in HR analytics, data-driven decision-making in human resources, or predictive modeling related to workforce management.

India Census Dataset

This dataset provides comprehensive census data at the district level for India. It includes detailed demographic, religious, educational, and workforce-related attributes, making it a rich resource for socio-economic analysis.

1. Columns and Descriptions

A. Basic Information

District_code: A unique numeric code for each district. State_name: Name of the state to which the district belongs. District_name: Name of the district.

B. Population Data

Population: Total population of the district. Male: Total male population in the district. Female: Total female population in the district.

C. Literacy Data

Literate: Total number of literate individuals in the district.

D. Workforce Data

Workers: Total number of workers in the district. Male_Workers: Total number of male workers in the district. Female_Workers: Total number of female workers in the district. Cultivator_Workers: Number of workers engaged as cultivators. Agricultural_Workers: Number of workers engaged in agricultural labor. Household_Workers: Number of workers engaged in household industries.

E. Religion Data

Hindus: Total number of Hindus in the district. Muslims: Total number of Muslims in the district. Christians: Total number of Christians in the district. Sikhs: Total number of Sikhs in the district. Buddhists: Total number of Buddhists in the district. Jains: Total number of Jains in the district.

F. Education Data

Secondary_Education: Number of individuals with secondary education. Higher_Education: Number of individuals with higher education qualifications. Graduate_Education: Number of individuals with graduate-level education.

G. Age Group Data

Age_Group_0_29: Population in the age group 0–29 years. Age_Group_30_49: Population in the age group 30–49 years. Age_Group_50: Population aged 50 years and above.

2. Key Highlights

Number of Districts: 640 Number of Columns: 25 Non-null Values: All columns are complete with no missing data. Detailed breakdown of population by gender, age group, literacy levels, and workforce distribution. Religious composition and education statistics are also included for each district.

3. Potential Use Cases

Data Analysis and Visualization:

Explore patterns in population distribution, literacy rates, workforce composition, and religious demographics. Machine Learning Applications:

Build predictive models to classify districts or forecast demographic trends. Social Research:

Investigate correlations between education levels, workforce participation, and religion. Policy Planning:

Help policymakers target specific demographics or regions for intervention. Educational Insights:

Analyze the impact of education levels on workforce participation or literacy.

4. Dataset Overview

Total Rows: 640 Total Columns: 25 This dataset provides a unique opportunity to understand India's socio-economic and demographic composition at a granular district level.

This dataset provides a comprehensive collection of key economic indicators for India, encompassing various aspects of the economy. It includes data on Gross Domestic Product (GDP), inflation rates, employment statistics, trade balances, foreign exchange reserves, and more. The dataset is formatted as CSV files, ensuring ease of use for data analysis and visualization.

this graph was created in PowerBi,Tableau and Loocker Studio :

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ntroduction

The National Sample Survey (NSS) Multi Indicator Survey (MIS) 78th Round (2020-21) is a comprehensive dataset that provides key socio-economic insights about Kolkata and other regions of India. Conducted by the National Statistical Office (NSO), Ministry of Statistics and Programme Implementation (MoSPI), Government of India, this survey aimed to gather extensive data on multiple indicators, including education, health, employment, migration, consumption patterns, and digital access.

This document provides a detailed explanation of the Kolkata-specific findings of the NSS 78th Round, offering insights into various socio-economic dimensions of the city's population.

Objectives of the NSS 78th Round

The primary objectives of the 78th Round Multi Indicator Survey were:

To assess the education levels and literacy rates in Kolkata.

To understand household health conditions and access to healthcare facilities.

To analyze employment and labor force participation in urban settings.

To examine migration trends within and outside Kolkata.

To evaluate consumption patterns and expenditure levels.

To study digital access and usage among households.

Key Findings for Kolkata

1. Education and Literacy

The survey revealed that Kolkata maintains a high literacy rate, with a considerable percentage of its population having completed secondary and higher education.

A growing number of children are enrolled in private schools, though government schools still play a significant role.

Female literacy has shown an increasing trend, but disparities still exist in lower-income communities.

1. Health and Healthcare Access

Kolkata has a high hospital density, with most households reporting access to primary healthcare centers and hospitals.

The survey recorded a moderate prevalence of chronic diseases, including diabetes and hypertension, particularly among the elderly.

Public healthcare facilities are widely used, but there is significant reliance on private hospitals, especially for specialized treatments.

1. Employment and Labor Market

The workforce participation rate in Kolkata remains steady, with a majority engaged in the service sector, trade, and informal employment.

There has been a decline in manufacturing jobs, partly due to automation and industry shifts.

The gig economy and self-employment have seen a rise, reflecting national trends.

1. Migration Trends

Kolkata experiences both in-migration and out-migration, with many individuals moving to the city for employment and education.

The survey indicated that a large percentage of migrants come from rural West Bengal, Bihar, and Jharkhand.

Out-migration has been observed primarily among skilled professionals seeking opportunities in other metropolitan cities or abroad.

1. Consumption Patterns and Expenditure

The average household consumption expenditure in Kolkata is higher than the national average, reflecting its status as a major urban center.

Food consumption patterns indicate a preference for cereals, fish, and dairy products, with an increase in processed food consumption.

Housing and transportation form a significant portion of monthly expenses for urban residents.

1. Digital Access and Usage

The survey highlighted a strong penetration of digital connectivity, with most households having access to smartphones and the internet.

Digital literacy is improving, with increased use of online banking, e-commerce, and educational platforms.

However, a digital divide persists among lower-income groups and elderly populations.

Policy Implications

Based on the survey findings, the following policy recommendations are suggested:

Enhancing educational infrastructure to bridge the literacy gap in underprivileged areas.

Strengthening public healthcare systems to reduce dependence on private hospitals.

Promoting employment generation programs and support for informal workers.

Affordable housing initiatives to address rising living costs in Kolkata.

Expanding digital literacy programs to bridge the digital divide.

Conclusion

The Kolkata-specific insights from the NSS 78th Round (2020-21) offer valuable data for policymakers, researchers, and urban planners. These findings provide a comprehensive picture of the city's socio-economic...