Resource Hub
3505 hand-picked resources for students, job seekers, career changers, founders, and AI learners — 100% free to browse with no account required.
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35 resources
A DeepLearning.AI course built with Prior Labs, about 1.5 hours at the intermediate level, on using tabular foundation models such as TabPFN for prediction on spreadsheet-style data and connecting them to data science agents. Enrollment requires a free account.
A KDnuggets guide by Vinod Chugani that goes round by round through current data science hiring processes, including AI-assisted coding rounds and generative AI evaluation questions, and lists ways candidates can prepare.
Newsletter teaching data science fundamentals to self-taught learners, written by David Langer. Free to read.
Research areas and projects from Columbia University's Data Science Institute, including AI, health and society.
Why I recommend it: University research overview — follow links to individual projects for readable summaries.
Jason Brownlee's long-running tutorial site: hundreds of free, step-by-step machine learning walkthroughs in Python, organised into 'start here' guides by topic — getting set up, understanding algorithms, your first complete project, your first neural network, time series forecasting. Each tutorial is written to get you to a working result rather than a theory exam.
Why I recommend it: The tutorials and the 'start here' guides are free to read with no account. The site's business is paid ebooks, and the free ebook offer costs you an email address and an ongoing email course, so expect the marketing. A fair criticism to know going in: the tutorials are recipe-shaped, which gets you running code fast but can leave the why thin — pair them with something that explains the ideas.
Long-running free publication on data science, machine learning and AI, mixing daily tutorials with roundups of tools and techniques. Free to read, supported by ads and affiliate links.
Why I recommend it: Strongest on hands-on tutorials, weakest on product roundups — several posts carry affiliate links to courses, so treat recommendations as suggestions rather than independent verdicts.
Free programs for undergraduate students pursuing tech careers, with a current focus on AI, data science and machine learning pathways. Includes fellowships and work-based learning run with university and industry partners.
Why I recommend it: No cost to students, but programs are application-based and tied to specific universities and cohort dates. Check the programs page for who is eligible before you plan around it.
A platform for data science competitions, public datasets, and collaborative notebooks to practice machine learning.
Why I recommend it: Competitions, public datasets, and notebooks are free; heavy compute or private datasets need a paid plan.
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.
The standard free Python distribution for data and AI work — package management, notebooks and thousands of libraries in one install.
From the site: Anaconda is the trusted foundation for AI-native development. Secure, orchestrate, and accelerate data and AI at scale, from first experiment to production.
Why I recommend it: Free for individual use. If you are learning Python for data work, this saves you a week of setup pain.
A large free archive of practitioner-written tutorials and explainers on machine learning, statistics, data engineering and AI, from beginner to advanced.
Why I recommend it: Quality varies by author, but the beginner explainers are among the easiest free routes into data work.
Research commentary from the UK's national institute for data science and AI, covering AI safety, public-sector deployment, health data and the social impact of automated systems.
Why I recommend it: Solid, evidence-based writing on AI policy — a useful counterweight to vendor blogs.
The open-source Python library for static, animated and interactive charts, with free plot-type galleries, tutorials, cheat sheets and a full API reference.
Why I recommend it: If you are learning data work, start here: the example gallery lets you copy a chart that already looks like what you need and adapt it.
Portfolio builder made for data professionals: show projects, embed dashboards, add a skills timeline and share one public portfolio link. The free plan covers up to five projects with no card required.
A free four-month, hands-on machine learning engineering course covering Python, regression and classification, XGBoost, deep learning with TensorFlow, Docker, Kubernetes and cloud deployment, with homework, projects and a Slack community. The 2026 cohort started September 14, 2026, and you can still start now.
Why I recommend it: One of the strongest free routes into machine learning work, because you finish with deployed projects, not just notes.
Barclays' data and analytics internship track, covering the work interns do with data science, modelling and analytics teams and how to apply.
Why I recommend it: A good bridge if you have analytics coursework but no finance background — they train on the domain, not the tooling. Pair it with a portfolio project you can talk through.
IBM's internship hub, describing paid summer and co-op internships across software engineering, data science, consulting, design, sales and research, plus how and when to apply.
Why I recommend it: Start here rather than a general job board if you want a large-company internship on your resume. Applications open early, so check it in the autumn for the following summer.
Free ebook on building, monitoring, and troubleshooting AI agents in production: what to instrument, where agents fail, and the practices teams use to keep them reliable.
Why I recommend it: Useful even if you never build an agent yourself — it shows what serious teams actually worry about, which is good language to have in an interview about AI work.
A technical learning platform with 60,000+ books, 30,000+ hours of video, live online events, and interactive labs and sandboxes from O'Reilly and nearly 200 other publishers.
Why I recommend it: Check your public library card and any school or employer account before paying — many library systems give full O'Reilly access for free, and some live events are open to anyone.
The open-access preprint server for physics, mathematics, computer science, and related fields — a primary source for cutting-edge AI and machine-learning research papers.
Why I recommend it: The best place to read AI research before it hits journals or the press; search by tag or author to follow a specific line of work.
A Duke Coursera course covering the Python tooling MLOps roles rely on — virtual environments, package management, linting, testing, and deploying models as reproducible pipelines.
Why I recommend it: Useful if you are targeting ML engineering or data-science roles and need to show you can ship models, not just train them.
A hands-on Coursera project course from IBM that walks through building and deploying a simple AI web application with Python and Flask, including REST API integration and packaging for production.
Why I recommend it: Good next step after you have basic Python and want to see how an AI feature actually ships in a small web app. Audit for free; certificate available.
Job board dedicated specifically to AI, machine learning, and big data roles — ML engineering, data science, NLP, computer vision, AI research — aggregated from companies worldwide.
Why I recommend it: If you are specifically targeting an AI/ML role rather than "tech in general," this is more signal, less noise than a general tech board.
A free 12-page playbook from Tiffany Teasley (Data Sistah) with six portfolio projects built on real business problems, a free browser-based coding setup, a GitHub README template, a resume rewrite prompt, and the exact referral messages that turn a 15-minute chat into an introduction.
Why I recommend it: I like this one because it refuses to let you hide behind another certificate. Pick one project this week, finish it, then use the referral scripts at the back - that pairing is what moves people from studying to hired.
Created by Tiffany Teasley - Data Sistah
