big data
6 stories filed under big data on Beyond Market Intelligence. The newest of them: “Building a resilient data science career in an AI-native world”, “Discover how PySpark window functions transform data analysis beyond groupBy”, and “Discover how better data unlocks AI's real potential in healthcare.”. Data science careers are being reshaped by the very tools they helped create. Grouping data in PySpark gets you partway there, but it leaves you staring at a wall when you need rankings, running totals, or lagged values. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every big data story on Beyond Market Intelligence, newest first.
Building a resilient data science career in an AI-native world
Data science careers are being reshaped by the very tools they helped create. If you are entering this field now, the old playbook of static skills won't cut it. The key is to focus on how you adapt, not just what you already know. For those feeling the pressure to keep up, our piece on navigating AI/ML job requirements offers a practical look at how the expectations are shifting. This is about building a resilient skill set for the long haul.
Discover how PySpark window functions transform data analysis beyond groupBy
Grouping data in PySpark gets you partway there, but it leaves you staring at a wall when you need rankings, running totals, or lagged values. That's where window functions step in, and this practical guide shows you exactly why the trusty `groupBy` falls short. It's a clear, hands-on read for anyone ready to move past basic aggregations. If you're also curious how structured thinking applies elsewhere, our piece on paragraph structure in LLMs pairs nicely with this mindset.
Discover how better data unlocks AI's real potential in healthcare.
If AI is going to move the needle on cancer, it won't be through clever algorithms alone. This startup argues the real bottleneck is data, plain and simple. That's a refreshingly grounded take in a field prone to hype. We're not talking about magic; we're talking about feeding models the right, clean information. It's a messier problem than most want to admit, but it's the one that matters.
How AI Transformed a Data Scientist's Daily Workflow
The morning no longer starts with a chaotic scramble through CSV files. By 2026, the data scientist's workflow has shifted from manual cleanup to strategic oversight, where AI handles the repetitive grind. It's a transformation that feels less like automation and more like partnership. We still ask the hard questions, but now we have a thinking partner that accelerates discovery.
From Photonics to AI: Transforming Your PhD into an ML Career
A Ph.D. in quantum optics who wins coding competitions and has already applied ML to grating design and qubit control is not a stretch candidate. That is a portfolio. The question is not whether the transition is reasonable, but how quickly you can reframe your existing work as ML engineering. It is clearly reasonable. Your background is more aligned than you might think. For those seeking direction, our guide on distributed algorithms offers a practical next step to round out your skillset.
Palantir's billion-dollar quarter challenges AI's trust deficit
Palantir just posted $1 billion in profit, and Alex Karp responded by calling the AI industry "Marxist." That's a bold word from a CEO whose own success depends on the same frontier labs he's warning enterprises about. His point deserves attention: if these tools can't be trusted, why rush them into critical workflows? We're not here to defend big tech's habits, but we'd rather see scrutiny than blind adoption.