Data Analysis Tutorials

Learn how to use Excel’s built-in features, formulas, and even Power Query to tackle duplicates, along with best practices and troubleshooting tips for handling complex datasets.

Explore how you can identify duplicate entries in Excel using Conditional Formatting, the COUNTIF() formula, and Power Query. Learn best practices and troubleshoot issues.

This tutorial walks you through Microsoft Fabric, showing you how to set up, connect data, build pipelines, and collaborate—all in one practical guide!

See how to use VLOOKUP() to pull data from another sheet in your Excel workbook. Build confidence with practical examples and troubleshooting techniques.

Enhance your visualizations with Matplotlib colormaps. Learn to pick the right colormap, adjust color classes, and troubleshoot common visualization issues.

Create standout bar charts using Matplotlib, Seaborn, Plotly, Plotnine, and Pandas. Explore bar chart types, from simple vertical and horizontal bars to more complex grouped and stacked layouts.

Understand how to derive the characteristic equation of a matrix and explore its core properties. Discover how eigenvalues and eigenvectors reveal patterns in data science applications. Build a solid foundation in linear algebra for machine learning.

Discover the power of the geometric mean in finance, biology, and data science. Learn how to calculate it, when to use it, and why it's useful for analyzing growth rates.

Learn how to find p-value in Excel using Excel functions like T.TEST() and Z.TEST(), by using the Data Analysis Toolpak, or through manual calculations. Equip yourself to make confident, data-backed decisions in Excel.

Explore the different methods for filtering out and permanently removing duplicate rows using SQL. Learn the practical applications of how to remove duplicates in SQL Server, MySQL, and PostgreSQL.

Explore the arithmetic mean's role in data analysis. Learn its formula, applications, and how it compares to other kinds of means and other statistical measures, and understand when each is most useful.

Uncover the impact of multicollinearity on regression models. Discover techniques to detect multicollinearity and maintain model reliability. Learn how to address multicollinearity with practical solutions.