Bayesian Thinking for Data Science in Microsoft Fabric
In this edition, we’re exploring Bayesian modeling and how to think about uncertainty in a more realistic, practical way using PyMC3. By the time you’re done, you’ll have a clear intuition for what Bayesian thinking really means, why it’s so useful in day-to-day data work, and how it changes the way you interpret results. We’ll also explore how PyMC3 supports this mindset in a structured but approachable way.Choosing the Right Way to Run Python in Microsoft Fabric
Fabric gives us several ways to run Python, and at first they can look overlapping. In this post, I share the practical decision model I use to choose the right option based on execution mode, compute engine, and data access path. If you are code-first and want fewer wrong turns when moving from exploration to production, this guide is for you.Causal Inference for Data Science in Microsoft Fabric
In this edition, we’re exploring into causal inference and why it matters once you move beyond basic reporting and prediction. By the time you’re done, you’ll have a clear understanding of how causal thinking differs from traditional analytics and how to reframe everyday business questions around cause and effect instead of simple correlation. And because insights only matter if they’re understood, we’ll look at how to communicate causal findings clearly and responsibly so decision-makers know what they can trust and act on.Ground Truth Before Go-Live: Building Better Fabric Data Agents with Automated Evaluation Datasets
How do you know if your Fabric data agent is actually ready for production? A few successful test questions aren't enough. In this post, I'll share why ground truth datasets are essential for evaluating Fabric data agents and how a reusable notebook can help you generate them automatically from any Fabric or Power BI semantic model.Level Up Your Forecasting with Temporal Fusion Transformers for Data Science in Microsoft Fabric
In this edition, we’re exploring Temporal Fusion Transformers in a way that actually makes sense in the real world. You’ll also get a clear walkthrough of the key ideas inside the architecture, like variable selection, gating, and attention, and how they work together to make sense of messy, real-life data. And more importantly, you’ll walk away understanding how TFTs can support you with complexity every day, giving you both clarity and confidence in your forecasting work.Exploring Text Intelligence through TF-IDF for Data Science in Microsoft Fabric
In this edition, we’re exploring how TF-IDF helps you discover meaning from language. You’ll see how this technique balances frequency and rarity to spotlight the words that truly matter, instead of the ones that just appear most often. By the time you’re done, you’ll have a solid understanding of how TF-IDF bridges the gap between unstructured text and structured analytics and why they are still relevant in the rise of Large Language Models (LLMs).Mastering Advanced Regression for Data Science in Microsoft Fabric
In this edition, we’re exploring two regression techniques that every data professional eventually bumps into when the simple models stop telling the full story. You’ll get a clear sense of what quantile regression actually solves, especially when your data behaves in unpredictable or uneven ways. By the time you’re done, you’ll feel more confident choosing the regression approach that truly fits the question you’re trying to answer, instead of defaulting to whatever is familiar.Data Wrangler in Microsoft Fabric: A No Code Approach to Faster Data Prep
Data preparation is one of the most time-consuming steps in any data analytics workflow. Fortunately, Data Wrangler in Microsoft Fabric simplifies this process by offering a graphical, no-code interface for exploring and transforming data directly inside notebooks. In this article, we will explore how Data Wrangler helps clean and transform data efficiently without writing complex code.Mastering Advanced Regex Techniques for Data Science in Microsoft Fabric
In this edition, we’re exploring how advanced regex can help you make sense of unpredictable text fields that show up in real projects. By the time you get through it, you’ll have a clearer way of spotting patterns that other people miss, expressing those patterns in a structured way, and shaping unstructured data into something that finally behaves. You’ll also get a feel for how this kind of thinking changes the way you approach cleaning work overall, because once regex clicks, you start seeing text differently.
