Trend Analysis in Power BI
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Learn to chart time series in Power BI, calculate rolling averages and year-over-year change, and find key drivers with decomposition trees.
Trend analysis in Power BI begins with dates. You pull day of week or time differences out of a timestamp, plot a metric over time, and smooth short-term noise so the underlying direction shows. The work then turns to why a number moves, using two built-in visuals that rank which variables have the strongest effect on a target outcome. Airbnb listings and Glassdoor employee reviews supply the data for the exercises.
What You Will Achieve
- Pull day-of-week and time-difference fields out of date columns to compare activity across the week
- Calculate year-over-year change and add a rolling average to steady a noisy line chart
- Tell cyclical movement apart from seasonal patterns and point out unusual values in a time series
- Explain what drives an outcome such as attrition or average listing price by drilling into it with Power BI visuals
Program Overview
Time Series Exploration and Date Variables
Opens with the traits of time-based data and how to derive extra fields from timestamps in a set of Airbnb listings.
- Characteristics of time series data
- Day-of-week and time-difference variables
- Weekday trends in Airbnb data
- Year-over-year change
Run Charts, Rolling Averages and Anomalies
Moves on to charting a metric over time with Glassdoor review data and reading the patterns the chart brings out.
- Time series run charts
- Cyclical versus seasonal variation
- Glassdoor reviews over time
- Smoothing with rolling averages and spotting anomalies
Decomposition Trees and Key Influencers
Closes with two native Power BI visuals that show which variables influence a target outcome most, and how to build and read each one.
- Building and interpreting decomposition trees
- Breaking down attrition rates
- Key influencers for listing price and super hosts
- Comparing the two visuals and reading segments
Certificate Awarded (Paid)
Job Outlook
Trend and driver analysis in Power BI supports reporting and business intelligence work, where analysts track metrics over time and explain changes in figures such as staff attrition, prices or review ratings.
Good for: Report builders who want to track metrics over time in Power BI and explain which factors move a result such as attrition or price.
Keep in mind: The lessons apply rolling averages and anomaly checks visually inside Power BI and leave out the mathematical derivations a statistics course would teach.
Bottom line: It pairs basic time series charting with Power BI’s two driver-analysis visuals, practiced on Airbnb listings and Glassdoor review data.
Frequently Asked Questions
What data do the Power BI exercises work with?
The exercises draw on Airbnb listings from several cities, including New York, Paris, Rome and Sydney, and on a synthetic set of Glassdoor employee reviews that holds ratings and professional history.
How do decomposition trees differ from key influencers?
A decomposition tree lets you split a metric by one variable after another and drill down step by step. Key influencers ranks the variables that most strongly affect an outcome. Both are native Power BI visuals that need no code, and one lesson compares them directly.
What questions does the key influencers chapter work on?
The exercises look at what affects the average listing price and what makes a host a super host, and they show how to read the segments of observations that shift the target outcome.
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