Data Preparation in Power BI

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Free Online Course: Data Preparation in Power BI provided by DataCamp is a comprehensive online course, which lasts for 3 hours worth of material. The course is taught in English and is free of charge. Upon completion of the course, you can receive an e-certificate from DataCamp. Data Preparation in Power BI is taught by Maarten Van den Broeck and Khaled Choucri.

Overview
  • In this interactive Power BI course, you’ll learn how to use Power Query Editor to transform and shape your data to be ready for analysis.

    In this interactive Power BI course, you’ll learn how to use Power Query Editor to transform and shape your data to be ready for analysis. You’ll also get to grips with advanced Power BI topics, including how to use M language and the Advanced Editor, to help you be even more efficient in data preparation.

Syllabus
  • Profiling your Data and Introduction to Power Query
    -Data preparation is key to becoming a successful data analyst. You’ll learn how to do essential data preparation steps such as filtering and renaming columns and how to use data preview in Power BI to identify common errors that appear in datasets.

    Data Preview features in Power Query
    -Data preparation is key to becoming a successful data analyst. You’ll learn how to do essential data preparation steps such as filtering and renaming columns and how to use data preview in Power BI to identify common errors that appear in datasets.

    Data Manipulation
    -This chapter covers the most common numerical and text transformations you’ll use in Power Query. Through interactive exercises, you’ll learn how to split and merge text columns, apply logarithmic and square root transformations on numerical columns, and extract month and week names from date columns.

    Numerical transformations in Power Query
    -This chapter covers the most common numerical and text transformations you’ll use in Power Query. Through interactive exercises, you’ll learn how to split and merge text columns, apply logarithmic and square root transformations on numerical columns, and extract month and week names from date columns.