เพิ่มความรู้ด้านข้อมูลและ AI ด้วยข้อมูลเชิงลึกจากผู้เชี่ยวชาญสำหรับผู้เรียนและทีมงาน
กำลังพัฒนาทักษะทีมขนาดเล็ก?เริ่มต้นวันนี้
เผยแพร่ล่าสุด
Richie and Ben explore why hiring is a broken two-sided market, why jobs are bundles of tasks (not skills), building universal taxonomies from billions of job postings, which data careers resist AI, when traditional NLP beats LLMs, and much more.
Tom Farnschläder, Data Science Editor at DataCamp, shows you how to design and build Claude Skills tailored to data tasks.
Industry experts discuss what it takes to successfully introduce tools like Claude Code into enterprise environments.
Calen Bedford, Security Architect at Insight Global, will walk through how to build an HR-focused AI agent in Claude Code using security-first design patterns.
Casius Sibanda Lee shows you how to build a memory-powered research agent using Claude Code and Oracle AI Database as the persistent memory core.
Maarten Van den Broeck, Learning Solutions Architect at DataCamp, shows you how to use Claude Cowork to build a competitor research bot for real productivity gains.
Richie and Rami explore time series foundation models and the case for scaling, feature engineering in the business world, communicating forecast uncertainty to stakeholders, the evolving role of data scientists as architects, and much more.
Nate Amidon, President at Form100 Consulting, will explore how leaders can drive successful AI automation initiatives.
Richie and Shireesh explore how AI agents are reshaping data stacks, why unified platforms like Fabric matter, how semantic models and ontologies reduce confusion in metrics, SQL and NoSQL choices on Azure, Postgres to Cosmos DB with guidance for builders, and much more.
Industry experts share what it really takes to land a data science role today.
Maria Eugenia Inzaugarat guides you step by step through analyzing patient insurance metrics and behaviour, and training a clustering model using common Python analytics libraries.
Aaren Stubberfield guides you step by step through analyzing Chicago neighborhood data using common Python packages for data manipulation and spatial analysis.
Richie and Jamie explore decision intelligence beyond BI, entity resolution across siloed data, building context graphs for fraud, AML, graph-RAG for LLMs to cut hallucinations, human-in-the-loop workflows, and ways to start today, and much more.
No stupid questions.
We live in exciting times. What happens next?
What should you learn to get hired or promoted?
Act like a boss (of an AI-ready team).
Run AI training that makes a difference.