In this video, Excel PRO TIP: Data Modeling 101 by Udemy Instructor, Chris Dutton, you’ll get familiar with Microsoft Excel’s powerful data modeling and business intelligence tools.
Take the full course on Udemy with the following link: https://www.udemy.com/microsoft-excel-pro-tips-for-power-users/
This course introduces Microsoft Excel’s powerful data modeling and business intelligence tools: Power Query, Power Pivot, and Data Analysis Expressions (DAX).
If you’re looking to become a power Excel user and absolutely supercharge your analytics, this course is the A-Z guide that you’re looking for.
We’ll kick things off by introducing the “Power Excel” landscape, and explore what these tools are all about and why they are changing the world of self-service business intelligence.
Using sample data from a fictional supermarket chain, we’ll get hands-on with Power Query; a tool to extract, transform, and load data from flat files, folders, databases, API services and more. We’ll practice shaping, blending and exploring our project files, and create completely automated loading procedures with only a few clicks.
From there we’ll dive into Data Modeling 101, and cover the fundamentals of database design and normalization (including table relationships, cardinality, hierarchies and more). We’ll take a tour through Excel’s data model interface, introduce some best practices and pro tips, and then create our own relational database to analyze throughout the course.
Next, we’ll use Power Pivot and DAX to explore and analyze our data model. Unlike traditional pivots, Power Pivot allows you to analyze hundreds of millions of rows across multiple data tables, and create supercharged calculated fields using a formula language called Data Analysis Expressions (or “DAX” for short). We’ll cover basic DAX syntax, then introduce some of the most powerful and commonly-used functions — CALCULATE, FILTER, SUMX and more.
We’ll wrap up the course with a final project, providing an opportunity to practice and apply the tools and techniques covered in the course to a brand new dataset.
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