Here’s why work cannot be an endless “up” escalator

Growing up in ’80s Singapore, the frenzy of “upgrading” was rife in society. The country was rapidly modernizing and industrializing. First-generation white-collar families, including the parents of my classmates and I, were rapidly upwardly mobile. Through 10 years of primary and secondary school, many of my friends’ families were able to move from 3-room to

Doing the work of several people? 5 Survival Tips

Your team is doing fine… and then, someone leaves. Someone else goes on maternity. Another coworker gets married, and cashes in on their carefully-accrued leave quota to take a month-long honeymoon break. If you’re extra unlucky, yet another co-worker has a car accident and gets laid up for a month. All of a sudden, the

Pandas for Productivity Vlog Ep.8: 4 Key SQL Concepts For Business People

Why this topic? SQL isn’t only for data engineers and analysts. In a world where businesses need to be nimble and respond quickly to changes, everyone needs to be up-to-date with key metrics. Therefore, as business managers, we still need a working knowledge of how to manipulate SQL queries for quick insights. What it covers:

Pandas for Productivity Vlog Ep. 7: The necessity of making SQL queries scalable

Why this topic? Back in 2016, the size of my performance marketing portfolio tripled overnight. I was working 16-hour days with at least 2 hours per day spent updating Excel spreadsheets. However, this was all needless! What it covers: I give tips on how to automate daily repetitive SQL data pulls, to help you save

Pandas for Productivity Vlog Ep 6: Overcoming my mental block with Google BigQuery

Why this topic? The first time I encountered Google BigQuery in 2016, I was thrown off by some of its unique syntax. Previously, I had used mainly Hive and Teradata. Therefore, I’d like to share the most unfamiliar aspects to help those of you making this transition too. What it covers: I walk through the

Pandas for Productivity Vlog Ep 5: Moving averages in Pandas

Why this topic? This topic doesn’t have much documentation, yet is potentially very useful. Calculating moving averages in Excel is time-consuming. Furthermore, when you use a window function in SQL you must structure the query carefully. Why not do it in Pandas instead? What it covers: First, I demonstrate how to use .rolling() to get

Pandas for Productivity Vlog Ep 4: How to identify and fix missing data

Why this topic? This is a lead-in to next week’s episode, where I will talk about calculating moving averages in Pandas. Subsequently, I will show how nulls in the data set will affect how you want to structure your code. Therefore, I need to get everyone on the same page in understanding how nulls work.