DataCamp

Half price. Everything included. 🎉 Every course. Every track. Every certification path—all of it, at 50% off. Our July promo is live: 50% off DataCamp Premium and Teams, no exceptions on what's included. If there's a skill you've been meaning to pick up—Python, SQL, AI, or anything in between—this is the plan that gets you there. 👉 Lock in your plan: ow.ly/7yTQ50ZmNsN #DataCamp #LearnDataScience #Python #SQL #AI #Upskilling

Librarians: your community just got free access to 500+ data and AI courses! DataCamp Classrooms is now available to librarians, and we think this changes things for communities everywhere. Libraries are where people go to learn. Now they can go there to learn Python, AI, and data science too, completely free. Apply today: ow.ly/onC450ZmxqA #DataCampClassrooms #LibraryLife #DataLiteracy #AILiteracy #FreeEducation

The way to turn down a marriage proposal in #Python! #DataCamp #Memes #PythonFun (Thanks to analytic_ace for the meme!)

Free webinar: Create Claude Skills for Marketing. July 13, 11 AM ET with DataCamp's Rhys Phillips. Learn to build Claude Skills from scratch for real marketing workflows. Register: ow.ly/uTBJ50ZlWU6 #Claude

Choosing between MySQL and MongoDB depends on how your data behaves. This guide breaks down the key differences in: – Data modeling – Query language – Performance and scalability – Security and compliance – Real-world use cases Learn when to use structured relational tables, and when flexible document models are a better fit. 🔗 ow.ly/BkwC50WhuAl

"Certification," "certificate," and "career track" get thrown around like synonyms. They're not. → Certification = you passed an exam (CompTIA Data+, CAP) → Certificate = you completed a guided program (Google, IBM, Meta) → Career track = you built skills and a portfolio, with validation on top (DataCamp) That distinction matters more than most lists let on, because it changes what you're actually walking away with. We compared 7 data analyst credentials for 2026 across price, level, and who each one is really built for: DataCamp Data Analyst Certification—career track, entry-level, best if you want hands-on coding practice plus a credential Google Data Analytics Certificate—~$35/mo, strongest beginner brand recognition IBM Data Analyst Certificate—~$35/mo, Python-first alternative to Google CompTIA Data+—$225 exam, vendor-neutral and portable across employers Microsoft DP-600 (Power BI Data Analyst)—$165 exam, built for Power BI/Fabric shops Meta Data Analyst—guided certificate, business/marketing analytics focus CAP-Essentials (INFORMS)—$195–275 exam, vendor-neutral, aimed at formalizing foundational skills Full pricing, format, and a suggested 3-stage learning path here: ow.ly/ynLx50ZleJ7

Most people think you need to be "good at math" to become a data analyst. That's not actually the bar. Operations research analyst roles are projected to grow 21% from 2024 to 2034—market research analysts 7%—both well ahead of average job growth. And according to PayScale, data analysts report 3.9 out of 5 stars in job satisfaction. It's not just in-demand, people who do it actually like it. Here's the honest trade-off between the three ways in. 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗱𝗲𝗴𝗿𝗲𝗲 Comprehensive curriculum, a recognized credential, real networking. The cost: 2 to 4 years and $30,000 to $200,000, plus coursework that has nothing to do with data. 𝗕𝗼𝗼𝘁𝗰𝗮𝗺𝗽 3 to 6 months, a focused curriculum, a fraction of the price. The trade-off: less weight on a resume than a degree, and a pace that doesn't leave much room to breathe. 𝗦𝗲𝗹𝗳-𝘀𝘁𝘂𝗱𝘆 The cheapest and most flexible path by far. It only works if you have the discipline to actually finish what you start without anyone checking in on you. None of these three routes requires a math background. What they all require: SQL, a second language (Python or R), and—new as of 2026—AI-assisted workflows. Employers increasingly expect analysts to draft a first-pass query with an LLM, then know exactly when to trust the output and when not to. 𝗧𝗵𝗲 𝗽𝗮𝘆𝗼𝗳𝗳: Glassdoor's June 2026 data puts average data analyst pay around $76,000, with senior analysts near $110,000. The bar isn't a specific degree. It's a portfolio that proves you can turn raw data into a decision someone else can act on.

Claude Code isn't the only agent in town anymore. We compared 7 alternatives across model flexibility, pricing, and workflow. Quick breakdown: → Cursor—visual IDE, multi-model (Claude, GPT, Gemini), built-in Composer model at roughly a tenth the cost on its standard tier → Codex—OpenAI's delegate-and-review model, uses about 4x fewer tokens per task than Claude Code in practice → GitHub Copilot—cheapest entry point at $10/month, native ow.ly/zOtn50Zk4zm integration → Antigravity—Google's take, spins up parallel subagents instead of working sequentially → OpenCode—free, open source, runs local models if you can't send code to the cloud → Aider—git-native, every edit lands as its own atomic commit → Cline—VS Code extension, asks for approval before it touches anything The closest calls: Cursor vs. OpenCode (polish vs. control), and Copilot vs. Codex (native GitHub vs. token efficiency). Full comparison: ow.ly/lwca50Zk4zQ

Quick Python distinction that's easy to skip past: sets, frozensets, and tuples all hold multiple values—but they communicate very different intentions. A set is mutable. Great for collecting values, filtering data, or tracking state that changes over time. But that flexibility comes at a cost: mutable objects can't be hashed, so a set can't be a dictionary key or live inside another set. A frozenset is the immutable version. Once it's created, its contents are locked. That guarantee is exactly what makes it hashable—and exactly why it can do things a regular set can't. config = frozenset(["dark_mode", "beta_features"]) settings = {config: "user_123"} → works fine regular_set = {"dark_mode", "beta_features"} settings2 = {regular_set: "user_123"} → TypeError: unhashable type: 'set' Why reach for it over a tuple, then? Order. A tuple carries positional meaning—(x, y) isn't the same as (y, x). A frozenset doesn't care about order at all, only membership. So if you're modeling a fixed group where sequence is irrelevant—permissions, tags, a set of valid states—frozenset says that more precisely than a tuple does. There's also a quieter benefit: in larger codebases, accidental mutation is a common source of bugs. Making something immutable isn't just a technical constraint—it's a signal to whoever reads the code next that this value isn't supposed to change. Not a everyday tool, but a good one to have on hand when the situation calls for it.

Two years ago, "AI engineer" mostly meant strong Python, solid ML fundamentals, and comfort with TensorFlow or PyTorch. Those still matter. But the day-to-day of the role looks different in 2026. Prompt engineering and agentic AI have moved from "nice to have" to core skills. Teams are building systems where models don't just answer questions—they reason, plan, and use tools on their own. Frameworks for orchestrating these multi-agent workflows have become as standard as the ML libraries that came before them. A few other things worth knowing if you're mapping out where to invest your learning time: → 𝗥𝗔𝗚 𝗶𝘀 𝗻𝗼𝘄 𝘁𝗮𝗯𝗹𝗲 𝘀𝘁𝗮𝗸𝗲𝘀. Grounding model outputs in real, private data to cut down on hallucinations isn't an edge case anymore—it's expected in production systems. → 𝗠𝗟𝗢𝗽𝘀 𝗵𝗮𝘀 𝗮 𝘀𝗶𝗯𝗹𝗶𝗻𝗴. LLMOps. Keeping a model reliable in production used to mean watching for drift. Now it also means managing cost, latency, and guardrails for LLM-based systems specifically. → 𝗩𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗷𝗼𝗶𝗻𝗲𝗱 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝘁𝗼𝗼𝗹𝗸𝗶𝘁. If you're building anything that retrieves and reasons over custom data, storing and querying embeddings efficiently is part of the job now. → 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗟 𝘀𝗸𝗶𝗹𝗹𝘀 𝗵𝗮𝘃𝗲𝗻'𝘁 𝗱𝗶𝘀𝗮𝗽𝗽𝗲𝗮𝗿𝗲𝗱—𝘁𝗵𝗲𝘆'𝘃𝗲 𝗯𝗲𝗲𝗻 𝗿𝗲𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲𝗱. Reinforcement learning is still essential for robotics and game AI, but for most AI engineers, generative AI and agentic workflows are where the highest-leverage learning is right now. The throughline: The technical foundation (data engineering, programming, model evaluation) is unchanged. What's evolved is what you build with that foundation. Where are you focusing your learning this year—agentic systems, RAG, or something else entirely? 🖇️ ow.ly/pFZN50Zjy4B

@dair_ai Agentic research pipelines like this are becoming the norm, not the exception. If you want to go from reading papers like this to building agent pipelines yourself, our LangChain track is a solid on-ramp: datacamp.com/tracks/ai-engi…

NEW paper worth reading. (bookmark it) Autonomous research systems usually prove themselves on cherry-picked wins, human-framed topics, or a handful of preset tasks. FARS runs the full loop at scale instead. Stage-specific agents handle ideation, planning, experimentation, and writing over a shared workspace that records proposals, code, logs, results, and manuscripts. Its first public deployment produced 166 complete papers across 67 fine-grained AI/ML topics, and it kept the failures in the corpus rather than curating a highlight reel. Why it matters. 282 volunteer reviews over 140 papers give an honest read. FARS can produce review-worthy artifacts, while the same reviews expose recurring failure modes in narrow scope, methodology, and integrity. Paper: arxiv.org/abs/2606.31651 Learn to build effective AI agents in our academy: academy.dair.ai

@bindureddy Right-sizing the tool matters as much for people as it does for models, most day-to-day work doesn't need the Ferrari. Our AI for Work course is built around that exact pragmatic-use mindset: datacamp.com/courses/introd…

GPT-6 may be generally available as soon as next week Fable is great but it’s way overkill for everyday use - kinda like driving a Ferrari to the coffee shop next door! 5.6 will be absolutely perfect for pragmatic use! Can’t wait 🔥

@emollick This is exactly why "developer" increasingly means directing agents, not just writing every line yourself. We built a whole track around that shift: datacamp.com/tracks/associa…

*Cracks knuckles*

@cyrilXBT Love a free curriculum drop. If you want something more guided with hands-on practice (not just reading), we mapped out a full ML learning path here: datacamp.com/blog/how-to-le…

MIT just quietly dropped a free AI curriculum that puts $50,000 university courses to shame. 12 books. Zero tuition. From the same institution that produced the people building the models everyone is talking about. FOUNDATIONS 1. Foundations of Machine Learning — lnkd.in/gytjT5HC 2. Understanding Deep Learning — lnkd.in/dgcB68Qt 3. Machine Learning Systems — lnkd.in/dkiGZisg ADVANCED TECHNIQUES 4. Algorithms for ML — algorithmsbook.com 5. Deep Learning — lnkd.in/g2efT6DK REINFORCEMENT LEARNING 6. RL Basics (Sutton & Barto) — lnkd.in/guxqxcZZ 7. Distributional RL — lnkd.in/d4eNP-pe 8. Multi-Agent Systems — marl-book.com 9. Long Game AI — lnkd.in/g-WtzvwX ETHICS & PROBABILITY 10. Fairness in ML — fairmlbook.org 11. Probabilistic ML Part 1 — lnkd.in/g-isbdjj 12. Probabilistic ML Part 2 — lnkd.in/gJE9fy4w This is a complete MIT-level AI education. Not a YouTube playlist. Not a Twitter thread full of fluff. Textbooks written by the researchers who built the field. The people who actually study this will not just understand AI better than their peers. They will understand it better than most people currently getting paid to work in it. Most people will bookmark this and never open it. The ones who open it tonight are the ones who show up in 12 months having built something nobody around them understands yet. Bookmark this. Open the first one tonight.

@gudanglifehack Great list. Window functions + CTEs trip up most candidates, we rounded up 99 more you'll actually get asked in interviews: datacamp.com/blog/top-sql-i…

🗄️ Advanced SQL interview questions are designed to test more than your ability to write queries—they evaluate how you think, solve problems, and optimize data retrieval in real-world scenarios. If you’re preparing for interviews at top companies like TCS, Accenture, Deloitte, Infosys, Cognizant, Capgemini, IBM, Oracle, Amazon, Microsoft, Google, or any data-driven organization, you should be comfortable solving business problems using SQL rather than simply memorizing syntax. This collection of advanced SQL questions covers practical topics that frequently appear in technical interviews, including window functions, ranking, subqueries, Common Table Expressions (CTEs), recursive queries, self joins, pivoting data, duplicate detection, running totals, employee hierarchy analysis, consecutive login tracking, and advanced aggregation techniques. These are the kinds of SQL challenges you’ll encounter when working with enterprise databases containing millions of records. Employers want candidates who can analyze complex datasets, optimize queries for performance, and explain the reasoning behind their solutions. As you progress in your SQL journey, you’ll realize that writing a correct query is only the beginning. The best SQL developers also focus on: Writing clean and readable queries. Choosing the most efficient approach for large datasets. Understanding query execution plans and indexing. Reducing unnecessary joins and subqueries. Improving performance without sacrificing readability. Explaining business logic clearly during technical interviews. Remember, SQL is one of the few skills used almost every day in Data Analytics, Business Intelligence, Data Engineering, Backend Development, Finance, Marketing Analytics, Product Analytics, and Machine Learning. The stronger your SQL foundation, the more valuable you’ll become in any data-driven role.

@MrCharlesky Real talk: the jump from Analyst→Scientist is basically Python + ML on top of what you already know. We break down exactly what separates the two (and how to bridge the gap): datacamp.com/blog/data-anal…

Data Entry = Excel Junior Data Analyst = SQL + Excel Data Analyst = SQL + Excel + PowerBI Data Scientist = SQL + Excel + Python + ML Data Engineering = SQL + Python + AWS + Spark + ETL The bare minimum.

@aquijanoruiz @DataCampDonates @LaboratorioLIDE Hi! Forwarding this to our team!

@DataCamp Hi @DataCamp, we are your @DataCampDonates partner @LaboratorioLIDE. We have been trying to communicate with you regarding our continuation with the program, but we have not received a response in several months. Please contact us as soon as possible. Thanks.

The team behind Booth #310 at Gartner Data & Analytics Summit APAC 🇦🇺 Sydney was buzzing, and these are the faces who spent it talking data and AI upskilling with everyone who stopped by. From live demos to hallway conversations about closing the skills gap, our team showed up ready to dig into the questions enterprise teams are actually asking right now. Grateful for the conversations, the coffee, and everyone who made time to chat 🙌

You're already debugging in Python and thinking in SQL. That's plenty of new languages for one day. Good news: Your #DataCamp courses don't have to add another one! If English isn't your first language, you can switch your DataCamp language preference in 2 seconds—and get courses, assessments, and practice all in the language you actually think in. Less time translating, more time coding. → Update your language preference now:: ow.ly/zfx950ZjxwC

@nzvbe Pretty much. The front door has a bouncer checking for a CS degree. The side door just wants to see your GitHub.

@DataCamp You mean it's difficult to get in through the front door??

Most people think breaking into AI requires years of study and a computer science degree. The data tells a different story. According to the World Economic Forum, it takes about 30 hours to reach beginner-level AI proficiency. Advanced proficiency? Around 137 hours. And right now there are 3.4 open AI roles for every qualified candidate—demand is outpacing supply by a significant margin. The opportunity is real. But the path matters. Here's what actually works in 2026, depending on where you're starting from. 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘂𝘀𝗲 𝗔𝗜 𝗮𝘁 𝘄𝗼𝗿𝗸 You don't need code. You need workflow awareness—knowing which parts of your week are repetitive enough to delegate, and how to direct AI tools clearly enough to get useful output. ChatGPT, Claude, and Microsoft Copilot can handle first-draft writing, document summarization, research synthesis, and exploratory analysis. The learning curve is days, not months. 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗮 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘄𝗮𝗻𝘁𝗶𝗻𝗴 𝘁𝗼 𝗺𝗼𝘃𝗲 𝗶𝗻𝘁𝗼 𝗔𝗜 You already have the hardest part: production engineering. What you're missing is LLM and ML fundamentals—how foundation models work, how to build with APIs, how retrieval and agents fit together. That's modeling intuition, not years of theory. Most people make this jump in 6 to 12 months of focused work. 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗮 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝘄𝗮𝗻𝘁𝗶𝗻𝗴 𝘁𝗼 𝗺𝗼𝘃𝗲 𝗶𝗻𝘁𝗼 𝗔𝗜 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 You need the reverse: software rigor, deployment, and MLOps practices. The modeling knowledge is already there. It's the production side that needs building out. 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵 𝗮𝗻𝗱 𝘄𝗮𝗻𝘁 𝘆𝗼𝘂𝗿 𝗳𝗶𝗿𝘀𝘁 𝗔𝗜 𝗷𝗼𝗯 71% of AI and ML roles are filled by people whose title wasn't "AI" or "ML"—backend engineers, data analysts, infrastructure engineers who built the skills and moved across. The side door is wider than the front door. 𝗪𝗵𝗮𝘁 𝗵𝗶𝗿𝗶𝗻𝗴 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝗲𝗲: 3 to 5 deployed projects on GitHub, not notebooks that only run on your laptop. The portfolio projects that signal real ability are a working RAG application, an agent that completes a multi-step task, and at least one project involving fine-tuning or evaluation. Kaggle competitions add credibility. Certificates help less than shipped work. 𝗧𝗵𝗲 𝘄𝗮𝗴𝗲 𝗽𝗿𝗲𝗺𝗶𝘂𝗺 𝗳𝗼𝗿 𝗔𝗜 𝘀𝗸𝗶𝗹𝗹𝘀 𝗶𝘀 𝘀𝗶𝘁𝘁𝗶𝗻𝗴 𝗮𝘁 𝟲𝟮% 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄, up from 25% just two years ago. The gap between people who've started and people who haven't is widening fast. The 30 hours to beginner level start the moment you do.