Data science

Data science

Instead of treating each prompt as a one-off request, the new agent remembers what was asked earlier, including datasets, filters, time ranges, and assumptions, and uses that context when answering follow-up questions. This lets users refine an analysis progressively rather than starting from scratch each time," Satapathy added. Satapathy pointed out that this eases the pressure on developers to prebuild dashboards or predefined business logic for every possible question that a data analyst or business user could ask.

With the introduction of Live Query for BigQuery and Alteryx One: Google Edition, users no longer need to move data to run workflows. Companies that standardize cloud platforms for analytics and AI often see a gap between where data is stored and how it is prepared and used. Alteryx wants to change that by bringing analytics workflows directly to BigQuery. The promise: from data to insight to action, without compromising on security or scalability.

From a meteorological perspective, the winter storm sweeping across the country this weekend is a supply chain disruption in its own right: A high-pressure system from the north is smashing into a low-pressure system from the south, belting large swaths of the US with heavy snow, sleet, and freezing rain. While the snarl in the upper atmosphere could trickle down to the real supply chain on the ground, some retailers are taking steps to anticipate the impact of the storm and position their products accordingly.

Our work is not about producing a list of tables with numbers in rows and columns,

You can always make it better. You can improve things. But it does give you a good taste of what can be done in vibe coding. Those are things that I made maybe in 15 minutes, half an hour. It is quite simple to get those first steps and say, "Oh, this works." Maybe you want to do some improvements, and you refine the code and what you're expecting.

I'm thrilled to announce that I'm stepping up as Probabl 's CSO (Chief Science Officer) to supercharge scikit-learn and its ecosystem, pursuing my dreams of tools that help go from data to impact. Scikit-learn, a central tool Scikit-learn is central to data-scientists' work: it is the most used machine-learning package. It has grown over more than a decade, supported by volunteers' time, donations, and grant funding, with a central role of Inria.

The more attributes you add to your metrics, the more complex and valuable questions you can answer. Every additional attribute provides a new dimension for analysis and troubleshooting. For instance, adding an infrastructure attribute, such as region can help you determine if a performance issue is isolated to a specific geographic area or is widespread. Similarly, adding business context, like a store location attribute for an e-commerce platform, allows you to understand if an issue is specific to a particular set of stores

"When I first started this job, the main push back I always got was that synthetic data will take over and you just will not need human feedback two to three years from now," said Fitzpatrick, who joined the startup last year. "From first principles, that actually doesn't make very much sense." Synthetic data refers to data that is artificially created.

Upper is based on W3C standards such as RDF for conceptual graph representation and SHACL for validation, and it enables the principle of "model once, represent everywhere" across the data ecosystem.Upper organizes concepts through keyed entities, their attributes, and their relationships across domain boundaries. The modeling grammar and validation structure are designed to maintain consistency as definitions evolve. Keyed concepts can be extended monotonically, allowing new attributes or relationships without modifying existing definitions allowing domains to expand over time without breaking existing models.

The pandas DataFrame is a structure that contains two-dimensional data and its corresponding labels. DataFrames are widely used in data science, machine learning, scientific computing, and many other data-intensive fields. DataFrames are similar to SQL tables or the spreadsheets that you work with in Excel or Calc.

Speaking to investment analysts, he said that while MongoDB had all the elements needed to be the right foundational platform for AI workloads, it was too early to say what might be the platform of choice. However, he said MongoDB had been winning work from AI-native companies, citing a customer that recently "switched from PostgreSQL to MongoDB because PostgreSQL could not just scale."

Snowflake has signed an agreement to acquire Select Star. This company's technology will expand Snowflake Horizon Catalog by integrating with databases, BI tools, and data pipelines. This will increase the context for AI agents such as Snowflake Intelligence. The full context of data assets is often scattered across upstream and downstream systems. This fragmentation makes it difficult to find the right data and understand the full context. In the AI era, this limited context poses a problem for both humans and agents.

Data and analytics jobs really stand out, though. This sector had a Jobs Posting Index of 60, the lowest of all sectors Indeed tracked as of the end of October. That means there are 40% fewer data and analytics job openings than before the pandemic. Even worse: There is still a rising number of applications per job in this sector, according to Indeed.

Why Python's deepcopy Can Be So Slow copy.deepcopy() creates a fully independent clone of an object, traversing every nested element of the object graph." That can be expensive. Learn what it is doing and how you can sometimes avoid the cost.

Our industry is rushing headlong toward an AI-powered future. The promise is captivating: intelligent systems that can predict market shifts, personalize customer experiences and drive unprecedented growth. Yet in that race, many organizations are short-changing or even skipping a critical first step. They are building sophisticated engines but trying to run them on unrefined fuel. The result is a quiet crisis of confidence, where powerful technology underwhelms because the marketers don't trust the data it relies on.

It is clean and complete. It captures almost everything I have watched over the last decade, with the exception of a couple of hours of viewing on flights or in hotel rooms. Normally, the algorithm serves up a menu of options that includes something that will satisfy me. And that's the thing about algorithms: They are tuned to normality. They make predictions based on statistical likelihoods, past behavior, and expectations about the continuation of trends.

Aadeesh Shastry is a New York-based professional known for his analytical thinking, structured approach, and calm leadership style. He builds his work around the principles of focus, discipline, and long-term strategy - habits he began developing long before his career started. Raised in Fremont, California, Aadeesh combined academics with athletics. He was a hurdler on his school's track team, played competitive basketball, and studied chess theory in his free time.

🚀 DATATRONiQ is a deep-tech 💪 startup in Germany, driving the future of Industrial IoT and Edge AI. Our platform provides a one-stop solution for companies to monitor, analyze, and optimize their assets and processes. Through advanced machine learning and real-time analytics, we transform high-quality industrial data into meaningful insights that drive smarter decisions. We believe in an open and collaborative environment to foster the best ideas from the most creative people - if you are excited about applying AI and data science to real industrial challenges, we'd love to talk with you!