Level Up Log

Lately, I've found myself in situations that felt a bit out of my comfort zone, and it got me thinking about how I interact with people and present myself, even in small, everyday moments like speaking up in a discussion, introducing myself to someone new, or walking into a networking event where I don't know anyone. I recently came across the idea of “channelling someone” again while listening to The Diary of a CEO podcast with Vinh Giang. It wasn’t completely new to me. When I was working as a musician, I would often "channel" a particular musician while playing. Someone whose tone or energy matched what I needed in that moment. But I had never thought of applying the same idea outside of music. On the podcast, Vinh explains how we can borrow confidence from someone we admire. Not in a "fake it till you make it" way, but more like using someone else’s energy as a prompt to shift how you carry yourself. You’re not pretending to be them. You're simply ...

I started Data Camp's Associate Data Analyst in SQL career track at the end of March and expected it to take about 39 hours, since that’s what the estimate said. But in the end, it was closer to 45 hours and honestly, it was worth it. Not just because I finished the course or got the certification, but because of how much more clear SQL feels now, especially compared to when I was learning it for the first time during the Google Data Analytics course. Learning the Order of Execution One of the most helpful parts of this course was the way it explained concepts in a logical order. In particular, understanding the order of execution helped me finally get the difference between WHERE and HAVING. During the Google course, I couldn’t figure out why one worked and the other didn’t. It was never clearly explained that the filtering happens at different stages: WHERE filters rows before the grouping, and HAVING filters after. Once I understood the order of execution, it just clicked! M...

In a previous blog post about the Stanford Introduction to Statistics course, I mentioned that I’d be revisiting a few topics that took me a little longer to wrap my head around. This post is exactly that. A way for me to break down topics, not just to clear things up for myself, but also to create something I can revisit whenever I need a quick refresher. So here are the topics I'm going to cover: Law of Large Numbers vs Central Limit Theorem Significance Level vs. P-value Confidence Level vs Confidence Interval Standard Deviation vs Standard Error Z-test vs T-test 1. Law of Large Numbers (LLN) vs Central Limit Theorem (CLT) Both are foundational ideas in statistics and often mentioned together, but they’re doing two very different jobs. LLN says that as your sample size increases, your sample mean gets closer to the true population mean. CLT explains that regardless of your population’s distribution, the sampling distribution of the sample mean becomes approximately normal a...

When I first got into data, I assumed that the most important part was the data itself. Finding the right numbers, doing the right analysis and picking the right chart. But after reading Storytelling with Data by Cole Nussbaumer Knaflic, I realized there’s a whole other layer to good data work: telling a story that actually connects with people. This book changed the way I think about presenting data. I picked it up expecting a technical guide, and it is that, but it also dives deep into how we communicate, persuade, and get people to care. Here are a few of the big takeaways that stood out to me whilst reading. Start with the Who and the Why Before you even touch a chart or a slide, you have to ask: who is this for? And why does it matter to them? The book makes a big distinction between exploratory and explanatory analysis. Exploratory is the part we often focus on—trying out different things, testing hypotheses, figuring out what’s interesting. But when it's time to p...