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.

Starting to explore time series data

Statements about time series analysis

Exploring AirBnB time series

Generating a new date variable

Analyzing trends by day of week

Calculating year-over-year change

Exploring Time Series Data

Run chart deret waktu

Opsi

Siklus

Musiman

Siklus vs. Musiman

Menganalisis ulasan Glassdoor dari waktu ke waktu

Membuat bagan deret waktu pertama Anda

Menghitung rata-rata bergerak

Menemukan anomali dalam data deret waktu

Analyzing Time Series in Power BI

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. 

Introduction to decomposition trees

Statements about decomposition trees

Using decomposition trees in Power BI

Getting started with decomposition trees

Diving deeper into decomposition trees

Decomposing attrition rates

Decomposition Trees

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.

Introduction to key influencers

Decomposition trees vs. key influencers

Using key influencers in Power BI

Key influencers for average listing price

Segments from key influencers

Key influencers for super hosts

Congratulations!

Key Influencers

Exercises and Datasets

DataCamp vs. Local Experience

Dalam kursus ini, Anda akan mempelajari cara menganalisis deret waktu, memvisualisasikan data, dan menemukan tren. Anda akan membangun variabel tanggal baru, mempelajari run chart, dan menghitung rata-rata bergulir. Terakhir, Anda akan mengetahui cara mengidentifikasi variabel mana yang paling memengaruhi variabel target menggunakan decomposition tree dan key influencers di Power BI.

<h2>Bukan data deret waktu</h2>

  

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Exploratory Data Analysis in Power BI

Tingkatkan laporan Anda dengan analisis tren seperti deret waktu, pohon dekomposisi, dan pengaruh utama.

Analisis Tren di Power BI

Analis Data di Power BI

Pola berulang dalam kurun kurang dari satu tahun

Siklus vs. Musiman

Analisis Tren di Power BI

Latihan interaktif praktis