This statistic shows the leading job-growth sectors in the United States between 2018 and 2028, ranked by the number of jobs forecast to be created over this period. It is estimated that around ***** million new jobs will be created in the health care and social assistance sector during this time period.

The economic growth outlook for developing Asia remains vibrant. Asian Development Outlook 2018 foresees continued momentum after growth accelerated to 6.1% in 2017. The region is expected to expand by 6.0% in 2018 and 5.9% in 2019. Excluding Asia’s high-income newly industrialized economies, the prospects for growth are even higher, at 6.5% in 2018 and 6.4% in 2019. Traction for economic expansion will come from strong external demand, which is supported by a further pickup in growth this year in the major industrial economies, and robust domestic demand. Growth in the People’s Republic of China is seen to continue its gradual moderation, to 6.6% this year and 6.4% next, as the authorities take actions to address financial risks, while India recovers from a temporary growth setback in 2017, rebounding to 7.3% in 2018 and 7.6% in 2019.

Context

For those who are actively looking for data scientist jobs in the U.S., the best news this month is the LinkedIn Workforce Report August 2018. According to the report, there is a shortage of 151,717 people with data science skills, with particularly acute shortages in New York City, San Francisco Bay Area and Los Angeles.

To help job hunters (including me) to better understand the job market, I scraped Indeed website and collected information of 7,000 data scientist jobs around the U.S. on August 3rd. The information that I collected are: Company Name, Position Name, Location, Job Description, and Number of Reviews of the Company.

Content

• alldata.csv If you want to explore the job market around the U.S., download this one because it aggregates all information and cleans the job description by removing the tags.
• all other files. If you want to explore specific city or region, you can download any of them.

Acknowledgements

Special thanks to Indeed for not blocking me : )

Inspiration

• If you have no clue of where to start, check my blog for inspiration.
• Link to my PowerPoint Slides for Presentation.
• Link to my GitHub Code.
• Reach me at sl6149@nyu.edu

Possible Questions:

1. Who gets hired? What kind of talent do employers want when they are hiring a data scientist?
2. Which location has the most opportunities?
3. What skills, tools, degrees or majors do employers want the most for data scientists?
4. What's the difference between data scientist, data engineer and data analyst?
5. Can you develop an efficient classification algorithm to differentiate the three job types above?

This statistic depicts the results of a survey about the public perception of the job market in India as of July 2018, by generation. As of this date, around **** percent of Generation Z respondents across India stated that they thought it was difficult to find a job.

The 3-year Employment Outlooks consist of a rating (very good, good, moderate, limited or very limited) of the employment prospects as well as a narrative text that provides an assessment of the main forecast indicators, recent statistics, and value-added regional observations. Employment Outlooks are developed for each detailed occupation in all provinces, territories and economic regions of Canada, where data permits. They are updated annually. The Employment Outlooks developed until the 2015-2017 period were assessed on the basis of the National Occupational Classification (NOC) 2006, and include up to 520 occupations. Beginning with the 2016-2018 Outlooks, the NOC 2011 is used for the analysis and the Outlooks include up to 500 occupations. Outlooks and trend descriptions for the latest year (currently disseminated on Job Bank) are subject to change as new information becomes available. Every effort will be made to keep the records on the Open Data Portal as up to date as possible, though delays may occur. If you have comments or questions regarding the 3-year Employment Outlooks, please contact the Labour Market Information division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca

United States Texas Service Sector Outlook: Future Employment data was reported at 28.200 % in Nov 2018. This records a decrease from the previous number of 31.900 % for Oct 2018. United States Texas Service Sector Outlook: Future Employment data is updated monthly, averaging 24.200 % from Jan 2007 (Median) to Nov 2018, with 143 observations. The data reached an all-time high of 43.700 % in Mar 2007 and a record low of -18.200 % in Nov 2008. United States Texas Service Sector Outlook: Future Employment data remains active status in CEIC and is reported by Federal Reserve Bank of Dallas. The data is categorized under Global Database’s United States – Table US.S016: Texas Service Sector Outlook Survey.

Employment Rate in the United States remained unchanged at 59.70 percent in June. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States Company's U.S. Employment: Next 6 Months: Number Change data was reported at 30.000 % in Sep 2018. This records an increase from the previous number of 29.000 % for Jun 2018. United States Company's U.S. Employment: Next 6 Months: Number Change data is updated quarterly, averaging 41.500 % from Dec 2002 (Median) to Sep 2018, with 64 observations. The data reached an all-time high of 52.000 % in Sep 2003 and a record low of 21.000 % in Mar 2009. United States Company's U.S. Employment: Next 6 Months: Number Change data remains active status in CEIC and is reported by Business Roundtable. The data is categorized under Global Database’s United States – Table US.S018: CEO Economic Outlook Survey.

Local Law 174 enacted in 2016 requires the Department of Education of the New York City School District to submit to the Council an annual report concerning Career and Technical Education programs in New York city schools. This report provides information about Career and Technical Education (CTE) programs and students in CTE programs and CTE-designated high schools, as defined in Local Law 174 as reported through the 2018-2019 STARS database. CTE Designated High Schools are those in which all students are engaged in NYC DOE-approved CTE sequences of instruction that integrate rigorous academic study with workforce skills in specific career pathways. It is important to note that schools self-report their scheduling information in STARS. The report also includes information regarding the number and ratio of certified CTE instructors. This report consists of seven tabs: 1. CTE Program Characteristics and Enrollment, including number of staff that attended a CTE PD 2. Number and percentage of students at each high school in a CTE program 3. Number and percentage of students in each Community School District in a CTE program 4. CTE application and admission - CTE designated schools only 5. Graduation rates - CTE designated schools only 6. Graduation rates - CTE designated schools only (by Community School District) 7. CTE Full-Time and Part-Time Teachers CTE Program Characteristics and Enrollment "This tab includes the following information for each high school-level CTE program: - high school name - CTE designation - name of the program - the industry for which the program prepares students - the number of industry partners associated with the program - CTE program approval status through the New York state department of education’s CTE approval process - grade levels served by such program - number of students enrolled in such program - number of school staff attending professional development events held by CTE" Number and percentage of students at each high school in a CTE program This tab includes the number and percentage of students at each high school with a CTE program, disaggregated by: student race and ethnicity; student gender; student special education status; student English Language Learner status; student economic need status (poverty); and communicty school district. Data on students with disabilities and English language learners are as of the end of the 2018-19 school year.

Career and Technical Education (CTE) programs and students in CTE programs and CTE-designated high schools, as defined in Local Law 174 as reported through the 2018-2019 STARS database. CTE Designated High Schools are those in which all students are engaged in NYC DOE-approved CTE sequences of instruction that integrate rigorous academic study with workforce skills in specific career pathways. It is important to note that schools self-report their scheduling information in STARS. The report also includes information regarding the number and ratio of certified CTE instructors.

Starting in 2021, the Community of Madrid's gross domestic product has returned to positive territory after a decline of 10.4 percent in 2020. The forecast for 2023 points to a growth of three percent, while a growth of 2.9 percent is expected for 2024. GDP per capita of Spain The GDP per capita of Spain faced a decline during the COVID-19 pandemic, when the value dropped to 29,799 U.S. dollars in 2022. Nevertheless, since then, the GDP per capita of the Iberian country has been growing and is estimated to be at 34,045 dollars in 2024. In regional terms, the Community of Madrid was the autonomous community with the highest GDP per capita, at 38,435 euros in 2022. The employment scene in Madrid Employment in the autonomous Community of Madrid has been growing every year since 2021. The Spanish region favors the service sector, having over 87 percent of its employed population inserted in this sector. The average monthly income has also increased in recent years and, in 2023, reached 2,271 euros.

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. By leveraging advanced web scraping technology, this dataset delivers access to job market trends, salary insights, and in-demand skills. A valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence, this data helps businesses stay ahead in a dynamic job market.

Key Features:

✅ 214M+ Job Postings Tracked – Data sourced from 92 company websites worldwide. ✅ 7M+ Active Job Openings – Continuously updated to reflect real hiring demand. ✅ Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅ Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅ Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅ Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes in the Dataset:

General Information: - id (UUID) – Unique identifier for the job posting. - type (constant: "job_opening") – Object type. - title (string) – Job title. - description (string) – Full job description extracted from the job listing. - url (URL) – Direct link to the job posting. - first_seen_at (ISO 8601 date-time) – When the job was first detected. - last_seen_at (ISO 8601 date-time) – When the job was last observed. - last_processed_at (ISO 8601 date-time) – When the job data was last updated.

Job Metadata:

• contract_types (array of strings) – Employment type (full-time, part-time, contract).
• categories (array of strings) – Job industry categories (engineering, marketing, finance).
• seniority (string) – Seniority level (manager, non_manager).
• status (string) – Job status (open, closed).
• language (string) – Language of the job posting.

Location Data:

• location (string) – Full location details from the job description.
• location_data (array of objects) – Structured location details: -- city (string, nullable) – City where the job is located. -- state (string, nullable) – State or region. -- zip_code (string, nullable) – Postal/ZIP code. -- country (string, nullable) – Country. -- region (string, nullable) – Broader geographical region. -- continent (string, nullable) – Continent name. -- fuzzy_match (boolean) – Indicates if the location was inferred.

Salary Data:

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Salary currency (USD, EUR, GBP).
• salary_low_usd (float, nullable) – Minimum salary converted to USD.
• salary_high_usd (float, nullable) – Maximum salary converted to USD.
• salary_time_unit (string, nullable) – Time unit (year, month, hour).

Occupational Data (ONET):

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (Computer and Mathematical).
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (Python, JavaScript, AI).

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Job Openings Docs https://docs.predictleads.com/v3/guide/job_openings_dataset

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

The Annual Social and Economic Supplement or March CPS supplement is the primary source of detailed information on income and work experience in the United States. Numerous publications based on this survey are issued each year by the Bureaus of Labor Statistics and Census. A public-use microdata file is available for private researchers, who also produce many academic and policy-related documents based on these data. The Annual Social and Economic Supplement is used to generate the annual Population Profile of the United States, reports on geographical mobility and educational attainment, and detailed analysis of money income and poverty status. The labor force and work experience data from this survey are used to profile the U.S. labor market and to make employment projections. To allow for the same type of in-depth analysis of hispanics, additional hispanic sample units are added to the basic CPS sample in March each year. Additional weighting is also performed so that estimates can be made for households and families, in addition to persons.

Job growth is often used as a measure of economic expansion and health. The city's job growth consistently exceeds local competitors. Future job growth in Henderson is predicted to be 42.1%, higher than the US average of 33.5%. Note: Most current US Census data is 2018.

United States Texas Service Sector Outlook: Part Time Employment data was reported at 2.700 % in Nov 2018. This records a decrease from the previous number of 4.200 % for Oct 2018. United States Texas Service Sector Outlook: Part Time Employment data is updated monthly, averaging 2.600 % from Jan 2007 (Median) to Nov 2018, with 143 observations. The data reached an all-time high of 16.900 % in Jun 2007 and a record low of -17.800 % in Nov 2008. United States Texas Service Sector Outlook: Part Time Employment data remains active status in CEIC and is reported by Federal Reserve Bank of Dallas. The data is categorized under Global Database’s United States – Table US.S016: Texas Service Sector Outlook Survey.

Job Vacancies in China decreased to 4380000 in the fourth quarter of 2018 from 4890000 in the third quarter of 2018. This dataset provides - China Job Vacancies - actual values, historical data, forecast, chart, statistics, economic calendar and news.