This comprehensive course provides a deep dive into Excel Power Query, an invaluable tool for data transformation and analysis. It covers an extensive range of topics, starting from the introduction of Power Query, data transformation fundamentals, advanced techniques, working with the Power Query M language, to practical applications through real-world scenarios and case studies. By the end of the course, learners will gain a robust understanding of Power Query, along with the ability to apply these skills in real-world projects, enhancing their data analysis capabilities and their ability to drive business success.
The Experts Guide to Excel-Power-Query
Power Query is a data transformation and analysis tool that allows users to extract, transform and load data from multiple sources. It provides a simple and user-friendly interface for data transformation and analysis and can help users to save time and effort in data preparation and analysis tasks.
Power Query is a data transformation and analysis tool that allows users to extract, transform and load data from multiple sources. It provides a simple and user-friendly interface for data transformation and analysis and can help users to save time and effort in data preparation and analysis tasks.
Power Query is available in different versions and is compatible with various Microsoft Office and Windows versions. Users can check for compatibility with their current software version and download the appropriate version.
Power Query has a user-friendly interface that consists of several tabs and sections. Users can navigate the interface to perform various data transformation and analysis tasks, such as filtering, sorting, merging, and pivoting data.
This involves connecting to different data sources and loading the data into Power Query. Power Query supports a wide range of data sources, such as Excel workbooks, CSV files, databases, and web APIs.
This technique allows you to selectively remove or keep rows and columns based on specific criteria. Power Query offers several functions for filtering data, such as Filter Rows and Remove Columns.
Sorting data: This involves ordering the data in a specific way based on one or more columns. Power Query offers several functions for sorting data, such as Sort Ascending and Sort Descending.
Splitting and merging columns: This technique involves dividing a column into multiple columns or combining multiple columns into a single column. Power Query offers several functions for splitting
and merging columns, such as Split Columns and Merge Columns.
Changing data types and formatting: This involves converting the data type of a column, such as changing a text column to a number column and formatting the data, such as changing the date format. Power Query offers several functions for changing data types and formatting data, such as Change Type and Transform.
Applying conditional logic: This technique involves applying rules or conditions to the data, such as filtering based on a specific value or calculating a new column based on a condition. Power Query offers several functions for applying conditional logic, such as Filter Rows and Conditional columns.
Error handling and null values: This involves handling errors and null values in the data, such as replacing null values with a default value or removing rows with errors. Power Query offers several functions for error handling and null values, such as Replace Errors and Replace Values.
Un-pivoting and pivoting data: This technique involves transforming data from a wide format to a long format (un-pivoting) or from a long format to a wide format (pivoting). Power Query offers several functions for un-pivoting and pivoting data, such as Un-pivot Columns and Pivot Columns.
This technique involves transforming data from a wide format to a
long format (un-pivoting) or from a long format to a wide format (pivoting). Power Query offers
several functions for un-pivoting and pivoting data, such as Un-pivot Columns and Pivot Columns.
This involves grouping rows based on a common value in a specific column and then applying an aggregate function such as sum, average, or count to the grouped data. This is useful when you want to summarize data and analyze it at a higher level. Power Query offers several functions for grouping and aggregating data, such as Group By and Aggregate.
This technique allows you to combine data from different sources. You can merge queries based on a common column or append queries to combine rows. Power Query offers several functions for merging and appending queries, such as Merge and Append Queries.
This involves adding calculated columns to your data using formulas or functions. Power Query allows you to create custom columns based on a variety of data types and operations, such as text manipulation, date calculations, and mathematical operations. You can also rename and delete custom columns as needed.
This is a more advanced aspect of data modeling in Excel. It involves creating a bridge table to connect two tables that have a many-to-many relationship. Power Query offers several functions for handling many-to-many relationships, such as Expand and Un-pivot.
This allows you to create dynamic reports and visualizations based on your data. You can refresh the data and the report will update automatically. Power Query allows you to load data directly into Excel tables, which can then be used
in PivotTables for analysis and reporting. You can also create relationships between tables in the Excel Data Model and use them to create more complex PivotTable reports.
The M language is the underlying code that Power Query uses to perform data transformations. In this section, you will learn the basics of the M language and its syntax.
Power Query allows you to create custom functions that you can use repeatedly in your data transformations. This section will cover how to create custom functions and how to manage parameters within them.
This section covers best practices for structuring your data transformations and data modeling. Following these best practices will help ensure that your data is organized and easy to work with.
Power Query can sometimes be slow when working with large data sets. This section covers tips and tricks for optimizing your data transformations to improve performance.
Finally, this section covers common issues that you may encounter while working with Power Query, such as missing data, formatting errors, and more. You will learn how to troubleshoot these issues and find solutions to common problems.
The hands-on examples in this module showcase the applications of Power Query in industries such as finance, healthcare, marketing, and sales.
In finance, Power Query can be used to extract financial data from multiple sources and consolidate them into a single report. In healthcare, Power Query can be used to analyze patient data and improve patient outcomes. In marketing, Power Query can be used to analyze customer data and improve marketing campaigns. In sales, Power Query can be used to track sales data and identify trends.
The tips and tricks covered in this module include how to optimize data sources, how to handle errors, and
how to manage data refreshes. These tips can help data analysts and business users save time and improve
the quality of their analyses.
The resources for further learning include links to online resources such as blogs, videos, and forums where learners can continue to expand their knowledge of Power Query.
About the teacher
Allah Ditta
Allah is a seasoned online course creator and designer, renowned in the world of e-learning through the Udemy platform. Over the past six years, Allah has excelled in the development of impactful educational content, authoring 32 best-selling online courses on Udemy. His work reflects a deep commitment to sharing knowledge and facilitating learning for thousands of students worldwide.
