Data Analysis with Excel
A Practical Introduction to Analyzing Data in Excel
In this video series you will learn how to use Excel as a powerful data analysis tool. Excel remains one of the most widely used applications in business and research, and knowing how to move beyond basic spreadsheet work — into real data exploration, summarization, and visualization — is a skill that pays dividends in virtually any field.
These tutorials are built around practical, hands-on examples. Rather than abstract explanations, you'll work with real datasets and see exactly how Excel's built-in tools — sorting, filtering, pivot tables, charts, and the Data Analysis Toolpak — can be used to quickly extract meaning from raw data. The goal is to get you comfortable analyzing data on your own, not just following steps.
Excel is well suited to exploratory analysis: getting a first look at a dataset, identifying patterns, summarizing groups, and producing clear visualizations. One of the first steps in any analytics project is understanding the shape and distribution of your data. Tools like descriptive statistics and pivot tables let you do this quickly without writing a single line of code.
No programming experience is required. A basic familiarity with Excel — navigating cells, entering formulas — is all you need to get started.
In This Series
Part 1: Getting Started with Data Analysis in Excel
Importing, sorting, filtering, and exploring a dataset
This first video sets up the foundation. You'll learn how to bring data into Excel, clean up common formatting issues, and use sorting and filtering to begin exploring what a dataset contains. These are the essential first steps before any deeper analysis — understanding the structure, spotting missing values, and getting a feel for the range and distribution of your variables.
You'll also see how to use Excel's built-in descriptive statistics tools to quickly generate summary tables — counts, means, standard deviations, min/max — that give you a high-level snapshot of your data before diving into more specific questions.
Part 2: Summarizing Data with Pivot Tables
Aggregating and slicing data to answer specific questions
Pivot tables are one of Excel's most powerful and underused features. In this video, you'll learn how to build a pivot table from scratch — dragging fields into rows, columns, and values — to quickly aggregate data by category. Want total sales by region? Average score by department? Pivot tables make these questions trivially easy to answer and even easier to update.
You'll also learn how to use slicers and filters within pivot tables to drill into specific subsets of your data interactively. By the end of this video, you'll be able to answer a wide range of analytical questions about any structured dataset in just a few clicks.
Part 3: Visualizing and Interpreting Results
Turning numbers into charts and communicating findings clearly
Numbers alone rarely tell the full story. In this final video, you'll learn how to create effective charts directly from your data and pivot table summaries — bar charts, line charts, scatter plots — and how to format them so they communicate clearly to an audience. You'll see which chart types are appropriate for different kinds of data and how to avoid common visualization mistakes.
You'll also get an introduction to the Data Analysis Toolpak, Excel's built-in add-in for more advanced statistical analysis. Once enabled, it provides tools for regression, correlation, histograms, and more — all accessible without any coding.
By the end of this series you'll have a solid, practical toolkit for data analysis in Excel that you can apply immediately to your own datasets and projects.
Additional Resources
For more Excel and data analysis tutorials, visit the Resources page. Related tutorials: Excel Pivot Tables, Descriptive Statistics with the Toolpak, Linear Regression in Excel.