Image by Author | Ideogram   We’ve all spent the last couple of years or so building applications with large language models. From chatbots that actually understand context to code generation tools that don't just autocomplete but build something useful, we've all seen the progress. Now, as agentic AI is becoming mainstream, you’re likely hearing…

Image by Author | ChatGPT   The Data Quality Bottleneck Every Data Scientist Knows   You've just received a new dataset. Before diving into analysis, you need to understand what you're working with: How many missing values? Which columns are problematic? What's the overall data quality score? Most data scientists spend 15-30 minutes manually exploring each…

Image by Author | Canva   Everyone and their dogs are trying to enter the tech industry, whether by learning to program, entering product management, or some other direction. I am pretty new to the tech industry, with only 5 years of experience, but as I speak to more individuals, some are worried about getting…

Sponsored Content       Recommender systems rely on data, but access to truly representative data has long been a challenge for researchers. Most academic datasets pale in comparison to the complexity and volume of user interactions in real-world environments, where data is typically locked away inside companies due to privacy concerns and commercial…

Image by Author | Canva   When it comes to error handling, the first thing we usually learn is how to use try-except blocks. But is that really enough as our codebase grows more complex? I believe not. Relying solely on try-except can lead to repetitive, cluttered, and hard-to-maintain code. In this article, I’ll…

Image by Editor | Ideogram   An organization's data teams often encounter complex projects with a variety of resources and structures scattered around. As the number of projects and team members increases, the information becomes more tangled and increasingly complex to manage. This is why we need to consolidate the information in a single platform.…

Image by Editor   Machine learning (ML) algorithms are key to building intelligent models that learn from data to solve a particular task, namely making predictions, classifications, detecting anomalies, and more. Optimizing ML models entails adjusting the data and the algorithms that lead to building such models, to achieve more accurate and efficient results, and…

Image by Author   In a previous article, I explained how AI is the skill of the future, with roles that command salaries up to $375,000 annually. Large Language Models (LLMs) have become a central focus in AI, and almost every data-centric role now requires some foundational understanding of these algorithms. Whether you’re a developer…

Image by freestockcenter on Freepik   With the emergence of large language models, prompt engineering has become an essential skill. Put simply, prompting involves how humans interact with machines. Engineering the prompt suggests an effective way to communicate the requirement so that the machines’ responses are contextual, relevant, and accurate.   The Framework   The…