The short version for data: pick one source, finish one thing, and show it to a person. Below are the 55 free entries I would use to do exactly that, in the order I would use them.
A nonprofit research institute that translates labor-market data into insights about skills, mobility, and the future of work.
Why I recommend it: Burning Glass turns labor-market data into actionable insight about which skills are in demand and who is getting left behind. I cite their research often.
A Bronx-based nonprofit offering free tech training and career support in web development, data, design, and cybersecurity for underestimated talent in New York, Newark, and Atlanta.
Why I recommend it: I point career changers who cannot afford a bootcamp toward TKH. The program is community-rooted, employer-connected, and focused on economic mobility.
Free courses and free LinkedIn-ready credentials in AI, cybersecurity, data, and cloud computing.
Why I recommend it: One of the few places where both the training and the credential are free. Stack two or three badges in one lane instead of one badge in four lanes.
Huntr blog post ranking 101 job search sites by real usage data from over 1,003,000 saved jobs and 602,000 applications, including interview rates for the largest boards.
Why I recommend it: Use this to build a shortlist of boards for your industry instead of spraying every site. Niche boards often outperform the big names.
A roundup of 2026 hiring data — job openings, applications per role, skills-based hiring and time-to-hire — with the sources behind each figure.
Why I recommend it: Published by a university that sells degrees, so read the education-related claims with that in mind; check the linked original sources before quoting a number.
U.S. Census Bureau analysis of how many American businesses actually report using AI, broken out by industry and firm size — primary source data rather than survey hype.
Why I recommend it: When someone tells you every company is using AI now, this is the free federal data you check it against. Useful ammunition in interviews and in your own planning.
A newsletter analyzing what actually performs on LinkedIn and social platforms, based on running experiments and reporting the data rather than repeating best-practice folklore.
Why I recommend it: Most LinkedIn advice is guesswork dressed up as expertise. This one tests things and shows the numbers, which is why I read it.
Hiring guides, salary context and role descriptions for data positions, published by a data-focused staffing firm.
From the site: As data science recruiters, we love share our insights on the process. Here's a collection of our best resources and tips for recruiters.
Why I recommend it: This is written for recruiters, not candidates — read it that way. It shows you how data roles get scoped and screened before you ever see the posting.
Huntr blog post analyzing 1.7 million applications to debunk common ATS myths, including the idea that ATS auto-reject resumes or assign scores to candidates.
Why I recommend it: Stop trying to game an ATS score that does not exist. Readable formatting and clear role fit matter more than keyword stuffing.
U.S. Bureau of Labor Statistics monthly data on job openings, hires, quits and layoffs. The BLS site blocked the automated check, so this description is based on the page title.
Official Bureau of Labor Statistics release on labor productivity, output, hours worked, and unit labor costs across the US economy, updated each quarter.
Why I recommend it: This is the primary source behind most AI-and-productivity headlines. Cite the actual numbers in interviews instead of the news summary.
Research-grade tracking of what AI models can do and how that has changed over time, with the data and methods published. Free.
From the site: Our hub for benchmark results, featuring the performance of leading AI models on challenging tasks. It includes results from benchmarks administered internally by Epoch AI as well as data collected from external sources. Explore trends in AI capabilities across time, by benchmark, or by model.
Why I recommend it: For the longer view rather than this week's launch — they show their working, which most leaderboards do not.
Unified intelligence platform that turns structured and unstructured data into a governed knowledge graph for AI. Offers a free open-source graph database (FlureeDB) and a hosted Fluree AI tier that starts at $0 with a free fuel allowance; paid enterprise plans add scale, SSO and private deployments.
From the site: Fluree turns raw data into trusted, queryable knowledge graphs. GraphRAG-powered accuracy for enterprise AI.
Free harmonized microdata from the monthly U.S. Current Population Survey (CPS), covering 1962 to the present. Includes demographics, employment, program participation and supplemental topics such as food security, computer and internet use, and voter registration.
Why I recommend it: A public dataset you can use for market research, policy analysis, or building data-driven career and business arguments. Registration is instant and extracts are free.
Job market data drawn from employer career sites since 2007 — 350+ million postings used for hiring-trend research, competitive analysis, and investment research.
Why I recommend it: Not a job board — it is where the hiring trend numbers come from. Handy when you want evidence about a field instead of vibes.
The Ludwig Institute's alternative unemployment measure that counts people who are jobless, underemployed, or earning below a living wage.
In plain terms: A research institute publishes a "true rate of unemployment" that counts anyone jobless, stuck in part-time work, or earning under a living wage. The number is usually far higher than the official rate, which explains why a "strong" job market can still feel impossible.
From the site: LISEP’s mission is to help achieve shared economic prosperity for all Americans, particularly for middle- and low-income families. Our focus is fact-based economic and policy research.
Why I recommend it: When headlines say the job market is strong and your search still feels brutal, this number explains the gap. Useful language for interviews and for your own sanity.
A free, continuously updated and sourced record of the physical infrastructure behind AI: data centres, GPU clusters, power, chips, cloud prices, measured performance and company financials.
Why I recommend it: Useful grounding when you want facts rather than headlines about the AI build-out.
A free, regularly updated leaderboard benchmarking how well leading AI models actually search the web, with the methodology and benchmarks published alongside.
Why I recommend it: Check this before assuming your favorite chatbot is the best one for research. The rankings move month to month.
Labor market research institute studying degree requirements, skills-based hiring trends, and economic mobility using real job posting data.
In plain terms: This research institute analyzes employment data and hiring trends across the country. You can read free reports to learn which job skills are in demand, explore labor market forecasts, and find which credentials lead to higher pay.
Why I recommend it: Their reports tell you which employers actually dropped degree requirements versus which just said they did.
Current openings in data engineering, analytics and data science posted by Dataspace, a staffing firm that specialises in data roles.
From the site: Browse open data science contract jobs, including data science, engineering, and analytics roles at industry-leading companies nationwide.
Why I recommend it: A niche board rather than a big one. Worth a look precisely because far fewer people are applying through it than through the giant sites.
Watchdog database tracking corporate subsidies, violations, and job quality commitments by employer and location.
In plain terms: A nonprofit watchdog with free searchable databases on corporate subsidies, tax breaks, and company violation records. Use it to research an employer or a city before you take a job or move for one.
From the site: Good Jobs First promotes corporate and government accountability in economic development, especially around the use of public subsidies.
Why I recommend it: Look up a company before you accept an offer or a relocation. Subsidy and violation records tell you how an employer treats the places it operates in.
Sep 29, 2026 – Sep 30, 2026New Jersey State Data Center
Registration form for the New Jersey State Data Center's Annual Network Meeting on 29 and 30 September 2026. The State Data Center is New Jersey's official partner in the Census Bureau's programme for making population, housing and economic data usable locally; the network meeting is where affiliates and data users are brought up to date.
Details: Worth knowing about if you use public data for job targeting, grant writing or a business plan — these are the people who make state figures usable. Be aware of what the link is: a bare registration form asking name, organization, email and phone. It states no fee, no agenda and no venue or joining details, so ask the State Data Center directly before you plan travel around it.
