MSPTDA 15: Comprehensive Introduction to Excel Power Pivot DAX Formulas and DAX Functions

Course Overview

What does this course tell?
(Please note that the following overview content is from the original platform)


) Introduction & Overview of Topics in Two Hour Video.
) Standard PivotTable or Data Model PivotTable?.
) Excel Power Pivot & Power BI Desktop?.
) Power Query to Extract, Transform and Load Data to Data Model – Get data from Text Files, Relational Database and Excel File..
) Build Relationships.
) Introduction to DAX Formulas: Measures & Calculated Columns.
) DAX Calculated Column using the DAX Functions, RELATED and ROUND.
) Row Context: How DAX Calculated Columns are Calculated: Row Context.
) We do not want to use Calculated Column results in PivotTable using Implicit Measures.
) DAX Measure to add results from Calculated Column, using DAX SUM Function..
) Number Formatting for DAX Measures.
) Data Model PivotTable.
) Explicit DAX Formulas rather than Implicit DAX Formulas.
) Show Implicit Measures.
) Filter Context (First Look) How DAX Measures are Calculated.
) Drag Columns from Fact Table or Dimension Table?.
) Hiding Columns and Tables from Client Tool.
) Use Power Query to Refine Data Model.
) SUMX Function (Iterator Function). DAX Measure for Revenue using the SUMX Function to simulate Calculated Columns in DAX Measures.
) Compare and Contrast Calculated Columns & Measures.
) Why We Need a Date Table. Why we do NOT use the Automatic Grouping Feature for a Data Model PivotTable.
) Build an Automatic Date Table in Excel Power Pivot. And then build Relationship..
) Introduction to Time Intelligence DAX Functions. See TOTALYTD DAX Function.
) Context Transition and the Hidden CALCULATE on all Measures..
) DAX Formula Benefits..
) Example of DAX Formula that is easier to author than if we tried to do it with a Standard Pivot Table or Array Formulas.
) AVERAGEX Function (Iterator Function) to calculate Average Daily Revenue..
) Filter Context (Second Look) AVERAGEX Iterator Function.
) Build Dashboard. Create multiple DAX Formulas. Create Multiple Data Model PivotTables and a Data Model Chart..
) Create Measures for Gross Profit and Gross Profit %.
) Continue making more Data Model PivotTables..
) Make Data Model Pivot Chart..
) Conditional Formatting for Data Model PivotTable..
) DAX Text Formula for title of Dashboard.
) CUBE Function to Convert Data Model PivotTable to Excel Spreadsheet Formulas..
) Adding New Data and Refreshing. .
) Update Excel Power Pivot Automatic Date (Calendar) Table. Clue is the blank in the Dimension Table Filter..
) How to Double Check that a DAX Formula is yielding the correct answer?.
) DAX Table Functions. See CALCULATETABLE DAX Function..
) DAX Studio to visualize DAX Table Functions, and to efficiently create DAX Formulas.
) Existing Connections to import data from Data Model into an Excel Sheet.
) Summary.


We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
This course provides a comprehensive introduction to Excel Power Pivot DAX Formulas and DAX Functions. It covers topics such as standard PivotTable or Data Model PivotTable, Power Query to extract, transform and load data to Data Model, building relationships, introduction to DAX Formulas, DAX Calculated Columns, DAX Measures, number formatting, Data Model PivotTable, explicit DAX Formulas, time intelligence DAX Functions, building an automatic date table, CUBE Function, adding new data and refreshing, and DAX Table Functions.
Possible Development Paths include becoming a data analyst, data scientist, or business intelligence analyst. Learners can also pursue a degree in data science, computer science, or business analytics.
Learning Suggestions for learners include taking courses in data analysis, data visualization, and machine learning. Learners should also become familiar with SQL and other programming languages. Additionally, learners should practice using Excel Power Pivot and Power BI Desktop to gain hands-on experience.

[Applications]
Suggestions for applying the knowledge gained from MSPTDA 15: Comprehensive Introduction to Excel Power Pivot DAX Formulas and DAX Functions include using Power Query to extract, transform, and load data to the data model, building relationships, creating DAX formulas, and using DAX Studio to visualize DAX table functions. Additionally, learners should be able to create multiple data model PivotTables and charts, use the CUBE function to convert data model PivotTables to Excel spreadsheet formulas, and use existing connections to import data from the data model into an Excel sheet.

[Career Paths]
1. Business Intelligence Analyst: Business Intelligence Analysts are responsible for analyzing data and developing strategies to help organizations make better decisions. They use data analysis tools such as Excel Power Pivot, Power BI Desktop, and Power Query to extract, transform, and load data into data models. They also use DAX formulas and functions to create measures and calculated columns, and to create dashboards and visualizations. As data becomes increasingly important to organizations, the demand for Business Intelligence Analysts is expected to grow.

2. Data Scientist: Data Scientists use data analysis tools such as Excel Power Pivot, Power BI Desktop, and Power Query to extract, transform, and load data into data models. They also use DAX formulas and functions to create measures and calculated columns, and to create dashboards and visualizations. They use machine learning algorithms to identify patterns and trends in data, and to develop predictive models. As organizations become increasingly reliant on data-driven decision making, the demand for Data Scientists is expected to grow.

3. Data Analyst: Data Analysts use data analysis tools such as Excel Power Pivot, Power BI Desktop, and Power Query to extract, transform, and load data into data models. They also use DAX formulas and functions to create measures and calculated columns, and to create dashboards and visualizations. They use statistical methods to analyze data and to identify patterns and trends. As organizations become increasingly reliant on data-driven decision making, the demand for Data Analysts is expected to grow.

4. Data Engineer: Data Engineers use data analysis tools such as Excel Power Pivot, Power BI Desktop, and Power Query to extract, transform, and load data into data models. They also use DAX formulas and functions to create measures and calculated columns, and to create dashboards and visualizations. They design and develop data pipelines and data warehouses to store and process large amounts of data. As organizations become increasingly reliant on data-driven decision making, the demand for Data Engineers is expected to grow.