Excel for Project Management: Skills Every Manager Needs

Organize project data with Excel tables, learn to create and name tables, add data dynamically, and use slicers to filter by assigned to for live, reusable project views.

Track key project metadata in Excel tables—capture project IDs, dates, assigned-to, and comments, and use calculated columns like days between start and end dates.

Apply conditional formatting to monitor project trends and risks by scoring each project on end date, status, and budget delta, and visualize outcomes with data bars and icon sets.

Create high level project summaries in Excel using pivot tables to consolidate descriptions and assignments, summarize values, and filter with slicers by status for quick insights.

Learn to derive high-level descriptive statistics from project data with Excel pivot tables. Create a table, compute budget delta and project length, and summarize status and duration.

Explore designing in-cell bar charts and sparklines to visualize progress and record-level data using conditional formatting, data bars, and custom min/max for clear KPIs.

Learn to use Excel to calculate CPI and SPI from earned value and planned value, and build a dashboard tracking safety, quality, and stakeholder satisfaction.

Learn how Excel builds a gantt chart to visualize a project schedule from start to finish with horizontal bars, a scale, and task listings, using colors and icons for milestones.

Learn to build a Gantt chart in Excel by linking start and end dates, visualizing project duration, and using formulas such as sequence, date, and xlookup with conditional formatting.

Master scenario analysis with a Gantt chart in Excel, using a dropdown to switch between original and scenario data and visualize date-driven project placement with CPI and SPI adjustments.

Apply earned value management to project performance by using plan value, earned value, and actual cost to assess schedule and cost variances, and build an Excel EVM calculator.

Build an awesome dashboard by organizing data into clearly named Excel tables for safety incidents and project data, format dates to short date, and prepare for KPI calculations.

Calculate SPI (schedule performance index) and CPI (cost performance index) from earned value, planned value, and actual cost; use iferror and count safety incidents to build a project dashboard.

Design the layout and structure of an Excel dashboard by building a Gantt chart and a pivot table, linking project data, dates, and status for clear project visibility.

Perform scenario and performance analysis in Excel to build interactive dashboards from project management data, using data validation, pivot tables, slicers, and timelines.

Compute mean, median, and mode from a sample of shipment transit days in Excel, using A2:A12, and learn how sorting and ties affect the median and the mode.

Identify the minimum and maximum values and compute quartiles in Excel using min, max, and quartile on a sorted data range, illustrating exclusive quartiles and data spread.

Learn to calculate variance and standard deviation in Excel from a sample data set (A4:A13) using the mean in C1, square errors, and apply var.s and stdev.s for efficient analysis.

Explore the central limit theorem and normal distribution as sample size grows, with a mean of 100 and standard deviation of 20, illustrating 68%, 95%, and 99.7% in Excel.

Analyze data from samples to estimate population metrics, compute standard deviation and standard error, and set the margin of error at a chosen confidence level with z scores in Excel.

Identify and minimize sources of error in data analysis by avoiding nonrandom samples, learning from the Literary Digest 1936 poll bias, guarding against investigator bias, and using random, up-to-date data.

Learn to organize data with histograms in Excel, adjusting bin width and number of bins to reveal distribution and key intervals.

Learn to create an xy scatter chart in Excel to explore relationships between two numerical data sets, such as distance traveled and amount spent, and interpret the upward trend.

Learn to display data with a logarithmic scale in Excel charts, using base ten, to make extremely large values in astronomy and biology readable.

Examine data visually by adding a trend line to a chart to reveal the relationship between distance traveled and amount spent, using a linear trend line and formatting options.

Learn to forecast outcomes in Excel using the fill handle to extend a linear trend and the forecast linear function, and heed predicting beyond your data range.

Learn to compute running totals, running averages, and three-month running averages in Excel using absolute and changing ranges with monthly data.

Define and test a data-driven hypothesis to evaluate how a variable relates to outcomes, distinguishing null and alternative hypotheses, which can be directional or non-directional.

Interpret advertising test results using standard error and z-scores to assess significance, determine if campaigns meet the 70 per week goal with 95% confidence.

Recognize the boundaries of hypothesis testing by understanding uncertainty, data quality, and the limits of the 95% standard, using correlation as a starting point for project management insights.

Apply the normal distribution in data analysis with Excel to compute probabilities and percentiles, using a mean of 100 and standard deviation of 20, covering 68%, 95%, and 99.7% ranges.

Learn how to apply the uniform distribution in Excel, generating random integers 1-6 with randbetween and calculating equal probabilities like one-sixth.

Learn how the exponential distribution uses the average time (lambda) to model task durations and calculate probabilities for specific times or ranges in Excel.

Explore the Poisson distribution in business analytics, learn about lambda and gaps between events, and compute exact and cumulative probabilities in Excel using the poisson.dist function.

Explore the binomial distribution for yes/no trials, compute exact and cumulative signup probabilities with Excel's binom.d function, and interpret the most likely signup range.