Afwijkingen in tijdreeksgegevens vinden

Wereldwijde trends in de tijd bekijken is ideaal om terugkerende patronen én afwijkingen te spotten. Door verder in te zoomen kun je achterhalen waarom deze patronen ontstaan.

In deze oefening onderzoek je een opvallende daling in nieuwe AirBnB-hosts. Dat doe je eerst met de anomaliedetectie van Power BI en daarna ga je er handmatig dieper op in.

Als je voortgang kwijt bent, sluit dan alle open rapporten en laad 2_3_anomalies.pbix uit de map Workbooks op het bureaublad.

Deze oefening maakt deel uit van de cursus

Trendanalyse in Power BI

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Praktische interactieve oefening

Zet theorie om in actie met een van onze interactieve oefeningen.

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Deze oefening maakt deel uit van de cursus

Trendanalyse in Power BI

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In this chapter, you’ll get more familiar with time-based variables and the multiple ways to extract further variables using EDA for analysis—like day of week and time difference. You’ll get hands-on with Power BI as you build line charts to calculate new metrics and uncover trends hiding in your data—including period-over-period change and rolling averages.

Exercise 1: Starting to explore time series data Exercise 2: Statements about time series analysis Exercise 3: Exploring AirBnB time series Exercise 4: Generating a new date variable Exercise 5: Analyzing trends by day of week Exercise 6: Calculating year-over-year change

Exercise 1: Run chart voor tijdreeksen Exercise 2: Cyclisch vs. seizoensgebonden Exercise 3: Glassdoor-beoordelingen in de tijd analyseren Exercise 4: Je eerste tijdreeks grafiek maken Exercise 5: Voortschrijdende gemiddelden berekenen Exercise 6: Afwijkingen in tijdreeksgegevens vinden

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One of the most powerful functions of EDA in Power BI is being able to identify which variables have the most influence on your target outcome. A native Power BI visualization tool enabling that is Decomposition Trees. You'll learn about Decomposition Trees, how to construct, then interpret in order to explain a target outcome by other variables.

Exercise 1: Introduction to decomposition trees Exercise 2: Statements about decomposition trees Exercise 3: Using decomposition trees in Power BI Exercise 4: Getting started with decomposition trees Exercise 5: Diving deeper into decomposition trees Exercise 6: Decomposing attrition rates

In this chapter you'll build another native Power BI tool, Key Influencers visual. It helps you to understand how much a target outcome changes based on specific variables and segments of observations.

Exercise 1: Introduction to key influencers Exercise 2: Decomposition trees vs. key influencers Exercise 3: Using key influencers in Power BI Exercise 4: Key influencers for average listing price Exercise 5: Segments from key influencers Exercise 6: Key influencers for super hosts Exercise 7: Congratulations!