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 ...

As a data analyst, spotting intriguing patterns and insights in data is a thrilling part of your role. But how do you determine whether those patterns actually mean something or are simply random noise? That’s exactly where inferential statistics come into play. These techniques help you understand if the ...

Visualizing seasonality is a critical analytical skill in business because many essential metrics like sales, revenue, or demand often follow predictable seasonal patterns. Understanding these patterns helps businesses anticipate periods of peak activity, manage inventory, allocate resources effectively, and create accurate forecasts. Python in Excel makes seasonal analysis especially straightforward. ...

When working with complex datasets, especially panel data common in finance, managing multiple levels of indexing can quickly become overwhelming in traditional Excel. This is where Pandas’ MultiIndex feature shines. A MultiIndex in Pandas lets you efficiently handle data indexed across multiple dimensions, such as financial metrics tracked over time ...