Section 1
Data Preparation
Preview
Preview: Prepare the data
- 4 of 51 lesson topics
- 1 overview segment
- 3 core concepts
- 2 exam tips
Lesson Topics
- Connecting to diverse data sources
- Privacy levels for data sources
- Power Query data profiling
- Power Query data cleaning
Overview
The exam tests data preparation within Power BI, a critical step before modeling and visualization. It covers connecting to various data sources, cleaning and transforming data using Power Query, and shaping it into a structure suitable for analysis. Effective data preparation ensures data quality, consistency, and performance within your Power BI reports and dashboards.
Core Concepts
- **Data Source Connectivity:** Power BI supports a wide range of data sources, each requiring a specific connection method. Understanding these differences (e.g., connecting to a flat file vs. a database) is crucial for efficient data import.
- **Privacy Levels:** Power Query privacy levels control how data from different sources can be combined. 'Public', 'Private', and 'Organizational' levels dictate data sharing and prevent unintended data exposure. Always assess data sensitivity before combining sources.
- **Data Profiling:** Power Query's data profiling tools provide insights into data quality, including null counts, data distribution, and column shapes. Use profiling to identify potential issues before transformation.
Exam Tips
- Prioritize data cleaning and transformation *before* modeling. A clean and well-structured dataset is essential for accurate analysis.
- Pay close attention to data types and ensure they are consistent across sources. Incorrect data types can lead to errors and inaccurate results.