Benchmark - Build Analysis Solution Using PowerBI
The purpose of Step 1 is to build, visualize, and interpret data models using Power BI.
Review the Canyon Sales dimensional model you built in the Topic 1 assignment "Design a Relational Schema" and perform the following Power BI:
1. Load the Canyon Sales database into Power BI. 4 dimensions and a fact should be loaded.
2. Use bar chart to visualize units sold by product name. Take a screenshot of the bar chart to be used in Step 3.
3. Use bar chart to visualize units sold by year. Take a screenshot of the bar chart to be used in Step 3.
4. Use bar char to visualize units sold by store region name. Take a screenshot of the bar chart to be used in Step 3.
5. Use bar chart to visualize units sold by store region name and product vendor name. Take a screenshot of the bar chart to be used in Step 3.
Step 2:
In this step, the student will learn how to perform feature extractions using DAX in Power BI.
In Power BI, create a table named Calendar with the following columns:
• Date column populated with the most recently completed 2 years' worth of data from January 1st to December 31st.
• Month column to display the name of the month.
• Year column to display the year.
• Month number to display the number of the month (e.g., 1 for January).
• Week number to display the number of the week (e.g., Jan 1 to Jan 7 is week number 1).
• Weekday to display the name of the day (Jan 01 has a weekday name of Sunday).
• Weekday number to display the day number of the weekday (ex: Sunday has a weekday number 7).
• Workday: display 1 if it is a workday, otherwise 0.
Sample Solution
In Power BI, feature extraction is the process of extracting data from a source and transforming it into meaningful information. This can be done through DAX (Data Analysis Expression). DAX is a formula language used in Microsoft Office applications to perform calculations, queries, and create reports. Feature extractions allow for the manipulation of data elements with mathematical operations such as summations, multiplication, division and more.
For example, using feature extraction with Power BI one could calculate sales per day by taking the total sales for each product over a given time period and dividing this figure by the number of days in that period. This would then give an average daily sales figure which can be used to compare performance against other products or across different periods. Furthermore using feature extraction one could also generate year on year growth comparison figures which take into account seasonal fluctuations as well as overall trends in demand or desire for certain products within different regions or markets.
Feature extractions are incredibly powerful tools that allow businesses to better understand their markets and customers so they can make informed decisions when developing strategies around pricing, promotions and product placement going forward. By utilizing these features companies can identify opportunities within their own data-sets to maximize efficiency while minimizing risk associated with potential investments or new ventures.