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Analyse data on a whole new level with Power Pivot and Data Analysis Expressions (DAX)
With Power Pivot and DAX, you can model data, visualize big data, and easily and efficiently create analyses to gain important insights. Data Analysis Expressions (DAX) is a collection of formulas used in Power Pivot, Power BI, and Azure Analysis Services, among others. If you work with data management and analytics, you will surely greatly benefit from this Excel add-in.
- Complete the course at your own pace
- Repeat as much as you want
- Practical exercises and quizzes
- Personal & digitally shareable diploma
- Save lessons as favorites
- The course takes about 4 hours to complete.
- Access to the course material for 12 months.
- Learn a tool for data analysis & data modeling
Some of the things you will learn in this course:
Power Pivot basics
Gain a basic knowledge base in Power Pivot by first downloading and creating a shortcut to Power Pivot as an add-in for Excel. Retrieving data from external sources correctly is the most important step for the program, and in this course you will learn to do just that.
Relationships and columns
Relationships are an essential building block of working in Power Pivot, where many different kinds of relationships are created between separate databases. You can connect columns from one database to another to create completely new information as well as new ways of looking at the data you’re working with.
Measures
Measures are calculations that are applied to large amounts of data for data analysis. This course gives you an introduction to the concept and how to use it to provide new insight into your data.
Data Analysis Expression (DAX) formulas
DAX formulas are special measures that can take in multiple columns as arguments and can process big data in a way that Excel cannot. Learn the most common and useful DAX formulas as well as how these measures work.
More about the course
Power Pivot is a powerful add on for Excel and DAX (Data Analysis Expressions) includes additional functions to use when you work with relational data and dynamic aggregation. With Power Pivot & DAX you can create data models, data analysis and import data from different various sources.
- Approximately 15 lessons divided into 4 chapters.
- Total video time: 37 minutes.
- Estimated time to take the course including quizzes: 2.5 hours.
- Average duration per lesson: 3 minutes.
- Personal shareable certificate after completing the course.
A lesson from the course
In this lesson from the course Power Pivot & DAX, you’ll learn about relationships. This is one of the lessons in the chapter Relations & columns. What differentiates Power Pivot from regular pivot tables is that we can retrieve external data from multiple places and perform extensive pivot table analysis. Where we would normally have to write a lot of VLOOKUP formulas to find related data among different databases, we can make the job easy with the help of relationships. A relation is a connection between two tables of data, based on one or more columns in each table. Power Pivot allows you to take multiple data sources and combine the data tables into a coherent database called a relational database. Power Pivot & DAX is a basic course in Power Pivot and DAX, which requires some knowledge in Excel. Power Pivot is an Excel add-in that can be used for data analysis and for creating data models. Power Pivot uses the DAX formula language that allows the user to create new information using calculations.
Meet your teachers
Emil
Emil is an important part of our team of course creators. He has been creating courses on Learnesy since 2017 and has an education in computer science. Many of the popular courses that can be found in Learnesy’s course range have been developed by Emil and he has a broad competence in Excel, data analysis and programming.
Niklas
Niklas creates new courses and manages existing courses on the Learnesy platform. He has studied statistics and has a background in programming and data visualization. In addition to good knowledge of Excel, he has a burning interest in data analysis, and possesses good knowledge of the subject and tools for the field.
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