The ETL Process: Advanced Data Transformations

Description

This session develops more advanced techniques for restructuring, aggregating, joining, and deriving information from complex datasets. These techniques are important because many accounting questions require analysts to move beyond cleaning individual variables to constructing new measures and analytical datasets from multiple observations and sources.

Motivation

How do we restructure relational data?

Review

The prior session introduced transformations that make raw relational data usable. This session extends those techniques to more complex settings in which the analyst must determine how observations should be combined, summarized, or reorganized to address a specific question.

Class plan

• We will work with SQL using three EYARC cases that will complement the EYARC Live SQL Training
• We will have time in class to prepare an individual submission responding to Q10 in either or both Cases 3 and/or 4

Team format

Discussion Teams

Materials

• Case

  Analytics mindset case studies OhCanada2

  View Online: Analytics mindset case studies OhCanada2PDF: Analytics mindset case studies OhCanada2Word: Analytics mindset case studies OhCanada2

• Case

  Analytics mindset case studies OhCanada3

  View Online: Analytics mindset case studies OhCanada3PDF: Analytics mindset case studies OhCanada3Word: Analytics mindset case studies OhCanada3

• Case

  Analytics mindset case studies OhCanada4

  View Online: Analytics mindset case studies OhCanada4PDF: Analytics mindset case studies OhCanada4Word: Analytics mindset case studies OhCanada4

• Slides

  Class 06 Slides

  View slides: Class 06 Slides

Tools

• SQlite extension in vscode

Concepts

• Structured Query Language (SQL)
• Relational Databases