Foundational Statistics for Data Analysis with Excel 2022

Many teams make decisions from spreadsheets nobody can interpret with confidence. Large, messy workbooks get averaged by hand, filtered inconsistently, or read as proof of a difference that is not statistically real. This course gives IT teams and their business partners a shared, plain-English foundation for turning raw data into defensible information using a tool they already have: Excel.

Role fit: This course is for IT Practitioners, analysts, and non-technical staff who support reporting. The goal is not to produce statisticians. The course builds working fluency in Excel functions, sorting and filtering, descriptive statistics like central tendency, variance, and standard deviation, and inferential methods such as confidence intervals, hypothesis testing, and A/A and A/B tests. It also covers when Excel is the right tool and when a programming language or analytics suite is the better choice.

Time investment: The course runs about 4.9 hours across five sequential skills. Each skill builds on the previous one, so Team Leads should assign the skills in order.

Change-management note: The Analysis ToolPak requires the desktop version of Excel, not Excel online. Confirm that learners have desktop licenses before assigning the course.

How the course supports results: Lessons include downloadable workbooks with start and solution worksheets, giving learners structured practice and a way to compare their work against a completed version. Later skills apply the statistics concepts to hypothesis testing and A/A and A/B testing, helping teams connect Excel mechanics to more defensible analysis.

IT teams are often asked to turn large, confusing spreadsheets into answers leadership can act on. This course gives your team a repeatable, Excel-based workflow for doing that, from cleaning raw data through testing whether a change actually made a difference.

The training builds toward a hands-on mini project: your team forms a hypothesis, collects the data, and analyzes it with confidence intervals, a t-test, and A/A and A/B tests to evaluate whether the hypothesis holds. The course example walks through building that analysis in Excel, giving teams a practical model for separating evidence-based findings from noise.

• Faster, more reliable reporting: Practitioners replace error-prone manual formulas with numeric, text, and date functions. They also use relative, absolute, and mixed cell references correctly, which cuts down on broken ranges and typos in shared workbooks.
• Better tool decisions: Team Leads gain a clear view of when Excel fits an analysis and when to move to a programming language or analytics suite. Teams also learn the online-versus-desktop differences that affect access to the Analysis ToolPak.
• Clearer summaries for stakeholders: Staff apply descriptive-statistics concepts, percentages, sorting, filtering, and PivotTables to summarize datasets in plain language.
• Defensible recommendations: Analysts use hypothesis testing and A/B testing to check whether a change is significant before the organization commits resources to it.

Each skill ends with a challenge, and the downloadable workbooks support the regular practice the trainer recommends for long-term retention.

Knowledge

• When Excel is the right tool for data analysis, and when a programming language or analytics suite is a better fit
• Differences between Excel online and the desktop version, including why the Analysis ToolPak works only on desktop
• Core Excel data types, plus how relative, absolute, and mixed cell references behave when formulas are copied or filled
• What statistics is in plain terms, including samples versus populations
• Descriptive statistics for summarizing distributions, including measures of central tendency and spread
• Inferential statistics concepts: confidence intervals, hypothesis testing, A/A tests, and A/B tests

Ability

• Set up Excel for analysis and install the Analysis ToolPak for exploratory data analysis
• Clean and manipulate data with numeric, text, and date functions, and avoid broken ranges when using the fill handle
• Sort and filter large worksheets to isolate the records that matter
• Calculate variance and standard deviation to summarize how data is spread
• Summarize test data with PivotTables and explore confidence intervals in Excel
• Run an A/A test and an A/B test with a t-test to decide whether a difference is statistically significant