In an earlier post I talked about the Python in Excel initialization environment: I likened it to the starting lineup of packages that load every time you open a workbook. It’s what gives you access to pandas, seaborn, and all the other core tools right inside Excel, along with ...

As an Excel analyst, you’ve probably had moments where your data seemed to show clear trends,profits rising, sales dropping, or costs climbing steadily. But sometimes, these apparent patterns might actually be random,tricking you into thinking there’s more control and predictability than there really is. That’s ...

Numbers don’t spread out evenly across digits. In many real datasets, the leading digit “1” shows up about 30% of the time while “9” hardly appears. That’s Benford’s Law. If your numbers don’t follow this pattern when they should, it could mean something odd or even fraudulent is going ...

Understanding who your most influential customers are—those who consistently recommend your business—is essential for sustainable, cost-effective growth. This approach, called social influencer analysis, identifies customers who act as connectors and referral hubs within your customer base. Historically, performing this type of analysis required complex Excel formulas or specialized ...

ARIMA (short for AutoRegressive Integrated Moving Average) is a classic statistical model for time series forecasting. It works by combining three elements: autoregression (using past values to predict future ones), integration (removing trends to make data stationary), and moving averages (capturing patterns in forecast errors). For analysts, it’s valuable ...

Feature engineering involves transforming raw data into meaningful variables to enhance machine learning models. It includes creating new features, encoding categorical data, and scaling or normalizing values—tasks that significantly boost model accuracy and insights. This practice naturally overlaps with data cleaning, as both involve handling missing values, inconsistencies, and ...

Decision trees are intuitive analytical tools that help us explore and predict outcomes by asking simple, branching questions about our data. They’re popular because they’re visual, easy to interpret, and closely mimic human decision-making processes. However, Excel users historically faced an uphill battle using decision trees. There’s ...

As analysts, we constantly report on KPIs and metrics critical to our businesses. That’s essential… but the numbers we present aren’t always as black-and-white as they seem. Every metric comes with uncertainty, errors, and limitations. Bootstrapping is a simple yet powerful statistical method that helps you quantify how ...

Hierarchical clustering is a technique that groups similar data points into clusters based on their attributes, forming a hierarchy or tree-like structure. Imagine organizing customers based on their purchasing behaviors or demographics to discover distinct segments you can target differently. For business users who rely on Excel, hierarchical clustering is ...