Differentiate Between Data and Information

Prompt(s)

1. Review Lab 3-1 Descriptive Analytics: Filter and Reduce Data - Slainte. Lab 3-1 uses the Slainte dataset, a fictional sales database for a brewery. This lab introduces the students to the data reduction technique of fuzzy lookup. The dataset presents employee and vendor records for the students to identify potential fictionalized vendors.
   As part of practicing the Microsoft track: Using Power BI, connect to the Excel file and import the Employee_Listing and Suppliers tables, then transform the data in Power Query. In Power Query, the students will merge the queries with a fuzzy match. The students will adjust the fuzzy match similarity threshold to 0.7 to see fewer, more likely matches. Share your results, experiences and how you think of applying this skill to your work/study. Lab Data @ Lab_3-1_Slainte_Dataset
2. Review Lab 3-2 Diagnostic Analytics: Identify Data Clusters – Lending Club. This lab uses the Lending Club dataset. Lending Club is a real-life peer-to-peer marketplace where borrowers and investors are matched together, and the dataset provides data on loans that have been approved and rejected since 2007, including the assigned interest rate and type of loan. In this lab, students will create a cluster analysis that will let them explore groups of loans with similar characteristics.
   As part of Practicing the Microsoft track: Using Power BI, connect to the Lending Club data and load it into Power BI. Create a Scatter Plot to show the groupings of interest rate by average loan amount and average debt-to-income ratio. Use the Power BI tools to automatically find clusters and label the axes and title. Share your results, experiences and how you think of applying this skill to your work/study. Lab Data @ Lab_3-2_Lending_Club_Transform_Microsoft