Time series analysis lets analysts identify patterns, trends, and cyclic fluctuations over time. These insights are essential for accurate forecasting, strategic planning, and informed decision-making. Despite its significance, working with dates and times in a sophisticated manner is often hindered by Excel, which has become the butt of many internet ...

Excel has long been a go-to tool for data analysts to crunch numbers and perform data analysis. Traditionally, its ability to handle text data, particularly for advanced analysis tasks like sentiment analysis, was seen as limited. Analysts seeking meaningful insights from text often resorted to more specialized tools or cumbersome ...

When Copilot for Excel was first introduced, it primarily served as a tool to automate basic Excel tasks like sorting, filtering, conditional formatting, and creating formulas. However, as the AI has evolved and more resources have been invested in the product, it now supports more advanced functionalities, including sophisticated data ...

Advanced Analysis with Python in Copilot for Excel can help with many data analysis tasks that would typically be challenging to execute in Excel. From building predictive models using techniques like random forests to uncovering patterns and relationships through clustering and principal component analysis, and even analyzing text data for ...

In a previous blog post I showed how to get over limitations of creating dummy datasets directly in Microsoft 365 Copilot by asking for a Python script instead, particularly the faker package. Technology moves quickly, and since publishing that post, the Faker package has become available directly within the Python in ...

As a trainer and content creator, I regularly generate fake datasets for exercises. This task can be more challenging than it sounds. Creating variables that follow a specific pattern or distribution was difficult with earlier random data distribution tools. Additionally, crafting a dataset that “feels” real for a given industry, ...

Whether you’re a seasoned financial analyst or a data-proficient professional, understanding the calculation of investment returns can help assess the profitability and risks of various assets. This post explores three core methods for calculating investment returns: normalized returns, daily returns, and logarithmic daily returns. The integration of Python within ...

Upsampling time series data involves increasing the frequency of your data points by filling in gaps to transform a lower-frequency dataset into a higher-frequency one. This process is often necessary when aligning datasets with different frequencies or when a more granular view of the data is required. For instance, in ...

Data analysts should be proactive about missing values in their data, as these can significantly impact analysis outcomes. First of all, many algorithms such as linear and logistic regression as well as decision trees require complete datasets and cannot inherently process missing data. But beyond statistical modeling and machine learning, ...

Recently, I encountered a task in Excel where I needed to identify the top N values within each group. Generally, I’ve found that working with groups in Excel can be less than user-friendly for many tasks, and I was hopeful that some of the newer dynamic array functions might ...