Course Description
Introduction to technologies and disciplines used to collect, integrate, clean, and otherwise prepare data into a well-structured, controlled, documented, and understood analytic-ready dataset.
Learning Objectives and Outcomes
- Develop an advanced level of proficiency with all the activities associated with managing data with an emphasis on data preparation for analytics.
- Primary focus areas of the class are developing proficiency in the use of Structured Query Language (SQL), data modeling, data warehousing, big data and cloud data engineering, and preparing and structuring data for subsequent analytical modeling.
Text Books
The material in the course is primarily drawn from the following texts:
- Beaulieu, Learning SQL: Generate, Manipulate, and Retrieve Data, 3rd Edition, O’Reilly, 2020, (LSQL)
- Kimball & Ross, The Data Warehouse Toolkit, Wiley, 2013 (DWT)
- Ponniah, Data Modeling Fundamentals, Wiley, 2007 (DMF)
- Adamson, Star Schema, The Complete Reference, Mc-Graw-Hill, 2010 (SS)
- Svolba, Data Preparation for Analytics Using SAS, SAS Press Series, 2006 (DPA)
- Kimball, The Data Warehouse ETL Toolkit, Wiley, 2004, (DWETL)
- Additional resources to be announced.