66 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
RudderStack's free library of explainer articles on data engineering, data infrastructure and machine learning, covering topics such as generalization, deep learning and supplying customer data to LLM applications. RudderStack sells a customer data platform.
A State of AI page by Nathan Benaich of Air Street Capital, reviewed October 31, 2026, comparing separate estimates of AI adoption, spending and financial returns, and explaining who each source measured, when, and what it counted as adoption.
Developer documentation for Olostep's /v1/batches endpoint, which submits large lists of URLs for scraping in one job and returns page content such as Markdown or HTML, with request parameters, status polling and retrieval examples. Olostep is a paid web-data API; reading the documentation is free.
Ramp's data index tracking business AI adoption over time.
Resume Now's report comparing Bureau of Labor Statistics projected employment growth for 2025–2035 across 70 U.S. occupational groups with employee reviewers' outlook on their employers' futures.
Gallup's findings on the continued decline in global employee engagement.
NYCEDC's monthly economic snapshot for October 2026, tracking the strength of New York City's economy. It notes private-sector employment fell by 23,000 in September, and the inaugural Spotlight of the Month covers the immigrant workforce ahead of the Statue of Liberty's 140th anniversary.
An interactive tool that maps 1,920 degrees to 811 occupations across the 50 states and DC, comparing salary data, hiring volume and growth paths using BLS, O*NET and other public sources.
Pew fact sheet on which social media platforms U.S. adults use, with demographic breakdowns and trends; YouTube and Facebook are the most widely used.
CompTIA's monthly tech jobs report: September 2026 active tech postings above 625,000, rising AI-skill postings and roughly flat tech occupation employment near 7 million.
Findings from JetBrains' Developer Ecosystem Survey 2026 of more than 15,000 professional developers: 90% used AI coding agents at work at least weekly as of mid-2026, and 68% used them daily. The report tracks how agents are carving out a place in developers' toolkits.
Nonprofit building open-source tools that let people get insights from data they cannot directly access or share.
Organization working on people's control over their data and digital lives, with research, newsletters and the Frequency protocol for a more open internet.
Stanford Digital Economy Lab dashboard tracking employment for workers by age and AI exposure, updated as new payroll data arrives.
Free simulation platform that introduces quantitative finance research; users in approved countries can become paid research consultants.
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.
The Governance Lab's research, publications and programs on how institutions can govern more effectively and legitimately through technology and data.
No-cost, self-paced and live courses, workshops and coaching on data, digital, AI and innovation skills for people working in government.
Stanford HAI's annual flagship report tracking AI progress across research, the economy, education, policy, and public opinion. The 2025 edition compiles data on model capability, training costs, industry investment, and workforce effects, with downloadable charts and datasets.
Why I recommend it: Free to download from Stanford HAI. It is a self-published report from a university institute; figures are sourced within the report, but some industry-investment and capability numbers rely on data supplied by the companies being measured. Treat headline rankings as the report's own synthesis, not neutral fact.
Bespoke Labs' open-source toolkit for local typed decisions, contrastive data curation and model evaluation.
Why I recommend it: Free and open source, from a company that also sells services — the repo is usable on its own.
NYCEDC's monthly data briefing on the city's economy as of September 24, 2026: private-sector employment down 12,900 in August, unemployment at 4.8% after six straight monthly declines, labor force participation at 62.1%, median asking rent near $4,000, plus office visitation, tourism, and transit numbers.
Why I recommend it: The one-page-per-topic format makes this the fastest way to sound current on the NYC economy in an interview or a client meeting. Read the jobs and small-business pages; skip the real-estate detail unless it touches your field. It is a city agency's own read of its own economy, so pair it with the raw BLS numbers if you need the unspun version.
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.
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.
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.
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.
Open project documenting notable people and the data behind who gets recorded as notable.
Why I recommend it: Interesting for what it reveals about whose lives get written down. Coverage is patchy, so treat gaps as gaps in the record rather than in reality.
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.
An open-source tool that converts PDFs, Word files and scans into clean structured text for AI use, running locally. MIT licensed.
From the site: Get your documents ready for gen AI. Contribute to docling-project/docling development by creating an account on GitHub.
Why I recommend it: The unglamorous step that makes everything else work: getting your PDFs into text without a paid converter.
Labcorp's global internship listings across laboratory science, data, IT and corporate functions, searchable by location and field.
From the site: Internships
Why I recommend it: A useful one if you want lab or health-data experience without a research-university connection.
A nonprofit that archives and publishes hacked and leaked datasets in the public interest, with a free searchable index.
From the site: A 501(c)(3) dedicated to archiving and publishing hacked and leaked data.
Why I recommend it: A primary-source archive journalists and researchers actually use — handle what's in it carefully and read their guidance first.
Free research reports on how people now search through AI answer engines instead of traditional search, with data by industry.
From the site: Explore Profound research on AI search, answer engines, and how people discover brands and information.
Why I recommend it: Relevant if you market anything online — how AI answer engines pick sources is changing how people find businesses.
Engineering and policy writing from Palantir on data platforms, government deployments, defence technology and how the company approaches privacy controls.
Why I recommend it: Read it critically — it is a company blog on a contested subject, which makes it useful primary material for understanding the industry's own arguments.
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.
A running database of strikes, petitions and campaigns by workers responding to AI in their workplaces.
Why I recommend it: Good evidence base if you are writing or speaking about AI and jobs.
A research and publishing project on digital colonialism — who owns infrastructure, data and platforms, and who is extracted from.
Why I recommend it: Shifts the ethics conversation from bias in models to ownership of infrastructure.
A campaign toolkit on health data contracts, written for people organizing locally rather than for policy specialists.
Why I recommend it: A rare example of a plain-language toolkit about a data contract.
Faine Greenwood's independent newsletter on drones, data and technological anxiety, including her global map of drone attacks on civilians.
Why I recommend it: Follow this if you want a specialist voice showing how to build authority in a niche.
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.
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.
Free annual research on wellbeing from the University of Oxford's Wellbeing Research Centre with Gallup and the UN Sustainable Development Solutions Network, covering how work, community and trust shape how people feel about their lives.
Why I recommend it: Useful evidence when you are weighing a job on more than salary.
Free workplace data showing that as AI use rises, more time is going into troubleshooting AI output than into productivity gains.
Why I recommend it: Good counterweight to AI hype in job interviews and internal pitches.
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.
Explore discussion dynamics, ranking patterns and engagement stats across Hacker News stories.
Why I recommend it: A live analytics view of how Hacker News stories perform and spread.
Statista chart showing how people are using AI tools for education and learning purposes.
Why I recommend it: Useful data point for anyone building or explaining AI-powered learning tools.
Tiffany Teasley explains retrieval-augmented generation without the jargon: how an AI model looks things up in your own documents before answering, and why that matters for accuracy.
Why I recommend it: If you can explain RAG in one sentence in an interview, you already sound more current than most candidates.
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.
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 Brookings analysis of how traditional labor market data is being challenged and reshaped by the frontier economy.
Why I recommend it: Labor market data is shifting fast. This Brookings piece helps you understand what is really happening beneath the headlines.
Personal site and learning resources from Fawad Qureshi on AI, data, and career upskilling.
Why I recommend it: A curated learning page if you are trying to navigate AI upskilling without getting lost in the noise.
Rights x Tech event on data, technology, and power, held Wednesday, September 23, 2026.
Details: Small, discussion-heavy events like this are where you actually meet people. Go with one question you want answered.
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.
Ramp's data report using anonymized corporate spending and hiring signals to track where AI is actually changing headcount and job functions.
Why I recommend it: Real transaction data instead of survey guesses. Useful if you want evidence about which roles are shifting rather than opinions.
Huntr analyzed nearly 2 million applications and three years of activity data. Activity peaked in September, and October led interviews.
From the site: Huntr analyzed nearly 2 million applications and three years of activity data. Activity peaked in September, and October led interviews.
Why I recommend it: Concrete seasonal patterns to time your job search; don't let a slow month discourage you—use it to prepare.
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.
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.
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.
Former Chief AI, Data, and Analytics Officer at Estée Lauder, Sony Music, and Royal Caribbean; speaks on real-world operational AI adoption.
Interviews and practical guidance on breaking into and growing a data career — analytics, engineering, and adjacent roles.
Why I recommend it: Good listening if you are eyeing a data role. Pay attention to what skills guests actually used to get hired.
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.
Short free tutorials in Python, pandas, SQL, machine learning, and data visualization.
Why I recommend it: The best part is what comes after the lessons: public datasets and notebooks you can turn into portfolio work.
Data study showing a decline in Reddit citations inside ChatGPT answers and what that means for content visibility.
Why I recommend it: A concrete lesson in platform dependency: the traffic source you optimized for can quietly disappear from AI answers.
AI advisor and former Google Chief Decision Scientist (2018-2023), now working independently on decision science and applied AI.
Why I recommend it: Explains AI and decision-making in plain language without either hype or fear — rare in this space.
Report on how the datasets powering major AI systems depend on mass invasions of privacy by design.
Why I recommend it: Read this before you paste sensitive personal or client data into an AI tool.
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.
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.
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.
