DataCamp

Learn data and AI skills from scratch with 620+ courses. Master Python, ChatGPT, SQL, Power BI, and more.

[DataCamp @DataCamp]

If you've been putting off @Snowflake certification, this is the window.
Our Snowflake courses are free until June 29—and if you complete the Essentials for Snowflake SnowPro® Core Certification track, you get a $50 voucher off the SnowPro® Core Certification exam.*

That's a cert that validates your foundational knowledge of Snowflake—architecture, data modeling, governance, security—and helps position you for the next step in your career.

The exam normally costs $175 ($100 in India). The prep track is free right now and the voucher cuts the cost by nearly 30% (50% in India).*

After June 29, our Snowflake curriculum is not free anymore.

What the track covers:

🏗️ Snowflake's three-layer architecture
🧑‍💻 SQL transformation and data modeling
🔐 RBAC, governance, and security

🤖 Working with AI and cloud-native tools in Snowflake
6 days left 👇
https://www.https://t.co/2HxWY2daIJ

*View full terms on website.

[DataCamp @DataCamp]

@LangChain @BraceSproul @jakebroekhuizen Sandboxes are becoming table stakes for safe agent deployment, great breakdown! If you're building LangChain agents and want to go deeper on the full stack - chains, tool use, production deployment - our AI Engineering with LangChain track has you covered: https://t.co/A6uJauDkE4

[DataCamp @DataCamp]

@EricNewcomer Great piece. One practical implication: knowing how to fine-tune and deploy open-source models locally becomes a real competitive advantage when API costs are unpredictable.

[DataCamp @DataCamp]

@svpino The backlash was inevitable. Vibe coding accelerates output - it doesn't replace the judgment needed for production-ready code. Testing, modularity, maintainability still matter.

[DataCamp @DataCamp]

@TivadarDanka Love this breakdown! Gradient descent really is the foundation everything is built on. For anyone who wants to see the math come to life in Python, DataCamp has a tutorial walking through the intuition, types (batch/SGD/mini-batch), and implementation: https://t.co/qvnJ1urqm4

[DataCamp @DataCamp]

Your team uses Google Cloud. Do they know how to get the most out of it?
We've got you covered, with courses on everything from Gemini and Gen AI strategy to Cloud infrastructure and Kubernetes.

→ https://t.co/vF4GCUT6iM

#GoogleCloud #DataCamp #GenAI

[DataCamp @DataCamp]

@swyx @TomasReimers @cursor_ai Git for agent workloads is a new category entirely - the dev tooling stack is being rebuilt from scratch for AI. Developers who want to build real skills for this era (not just follow the releases): https://t.co/bb7j8ZKNhV

[DataCamp @DataCamp]

@rasbt Small model capability keeps surprising everyone. The gap between "using AI" and "building AI applications" is now within reach for data scientists. We built a whole track around this transition: https://t.co/EFaym0aSkH

[DataCamp @DataCamp]

@AdamRLucek Catching real production failures that static benchmarks miss, FTW. Latency tradeoff is real but worth it once you're shipping. LangSmith makes setup much easier: https://t.co/8FD31kBjqO

[DataCamp @DataCamp]

@LangChain @jpmorgan @Chime The shift from "can we build it" to "can we trust it in prod" is the real milestone. Governance + evals are becoming non-negotiable. For anyone mapping the full LangChain stack (including LangSmith's role here): https://t.co/8FD31kBjqO

[DataCamp @DataCamp]

@TivadarDanka Gradient descent always looks like magic until you trace the math, and then it makes total sense. For anyone wanting to code it from scratch in Python: https://t.co/CL06fpOfpE

[DataCamp @DataCamp]

Exactly! Each team has an Elo rating - a single number that updates after every match based on who they beat and how strong that opponent was. The simulation uses those ratings to calculate the probability of each team winning any given match, then plays out the full tournament 10,000 times and counts who lifts the trophy most often. So it's less "testing the ranking" and more "using the ranking to simulate thousands of possible tournaments." The full methodology is in the blog if you want to go deeper.

[DataCamp @DataCamp]

We asked Python who wins the World Cup. It ran 10,000 simulations.
Spain 17.6%. Argentina 16.7%. France, England, Brazil behind.

Our social lead is Croatian. The Croatia stat was non-negotiable.

https://t.co/hUJngMEFCN
#WorldCup2026 #Python #DataScience

[DataCamp @DataCamp]

@bigdatasumit 8K learners - that's a movement!! For anyone who finishes and wants to keep leveling up with hands-on practice against real datasets, our SQL tutorial for data scientists is a solid next step: https://t.co/H1ByvKQP0u

[DataCamp @DataCamp]

@LangChain Open source is eating the agentic stack fast. If you want hands-on practice building real LLM apps, we built a whole track for exactly this moment: https://t.co/A6uJauDkE4

[DataCamp @DataCamp]

@randomrecruiter The gap between demand and supply is massive right now. For devs who want to make the jump, we built an end-to-end track that takes you from LLM basics to production-ready agents: https://t.co/Fdp7Rtjz8Z

[DataCamp @DataCamp]

@McKinsey_MGI Question isn't "will AI take my job" but "am I using AI effectively?" For non-technical teams looking to get ahead of this, we built a course specifically for using AI at work: https://t.co/ZMqmIUQZHz

[DataCamp @DataCamp]

@chidihedge If you hit any gaps or want extra exercises alongside your challenge, this expert Python learning guide might help: https://t.co/HWvfxDdGxM Keep going!

[DataCamp @DataCamp]

@DataLemurHQ SQL is genuinely the best first step - you see results instantly and it maps to real job skills from day one. If you want a structured path from zero to job-ready, our SQL roadmap lays it all out: https://t.co/FuHL8d8hkl

[DataCamp @DataCamp]

We ranked 16 AI newsletters by signal-to-noise, editorial depth, and audience fit.

Most newsletters just repackage the same press releases. These 16 don't:

🥇 The Median (ours)—weekly, free, AI + data news with learning context
📰 The Rundown AI—2M+ subs, highest open rates in the space
⚙️ TLDR AI—the engineer's daily. fastest triage out there.
🎓 The Batch—Andrew Ng's weekly. research framing done right.
🔬 Import AI—Jack Clark, Anthropic co-founder, writing since 2016. tech + policy.
🧠 Interconnects—Nathan Lambert on open models and post-training
🛠️ Ben's Bites—for builders shipping AI products
🏗️ Latent Space—the AI engineering bible
📖 Ahead of AI—Raschka's long-form technical deep dives
💼 Superhuman AI—1M+ subs, AI at work for non-builders
😄 The Neuron—best approachable daily, plain language
🎓 One Useful Thing—Ethan Mollick on AI adoption in real orgs
🌏 ChinAI—literally the only newsletter translating Chinese AI source docs
📋 Last Week in AI—one weekly pass if you skip dailies
📰 The Algorithm—MIT Tech Review journalism
🌐 Exponential View—big picture: AI + energy + economy

Full breakdown → https://t.co/NCa4MNmp3G