Data analysis starts with clean data. NULL values, those pesky gaps in your dataset, can trip you up if you handle them poorly. Dropping them might seem like a quick fix, but it’s not always the best move. This article explores what NULL values are, why they matter, and how to handle them wisely using Power BI, Pandas, and SQL. Let’s dive in with clear examples to make your data shine.

SQL queries can feel like magic when you need specific answers from your data. Subqueries, those queries nested inside others, are a powerful trick to pull out exactly what you want. They help you filter rows, calculate values, or update records with precision. This article breaks down what subqueries are, how they work, and when to use them, using a coffee shop’s sales data to keep things clear. Let’s get started.

Data analysis often uses two tools: linear regression and logistic regression. They sound alike, but they answer different questions. This article breaks down their differences with clear examples and the math behind them, so you can choose the right one for your data.

I’ve always loved coding. As a data nerd, I spend my days tinkering with Python scripts and digging into datasets. So when I got the chance to run a six-week coding boot camp for beginners in early 2022, I was thrilled. I wanted to share my excitement for programming and, honestly, learn a bit myself. Teaching 25 students, most of whom had never written a line of code, showed me new angles on what it means to program. Here’s what stuck with me.