DAT 229 - Tabular and Linked Data Analysis

 
Description
In this course, students explore relational databases which underlie many modern data systems. Students acquire foundational knowledge of table schema design, normalization and Structured Query Language (SQL) needed to interact with data sets from many knowledge domains. In contrast to production database systems, course emphasis lies on analytics-focused database skills, which enable students to integrate relational databases in data pipelines ending in analytic and presentation platforms–rather than transactional or logistics applications. Python is used throughout the course to build database schemas, insert data from flat files and third-party repositories and then extract the data for analytic and decision-support endeavors.

1. Design a multi-table, normalized database schema with strategically typed fields to house data for supporting decision making in a specific domain, such as transportation, environmental management or political science.
2. Implement a database schema using data definition commands.
3. Employ Structured Query Language (SQL) and Python or R to read data into a relational database to extract appropriate fields for analysis using primary-foriegn key relationships, table joins and row filtering.
4. Configure a relational database system using UNIX shell commands with appropriate user privileges for data management and connection to databases on a remote computer.
5. Present the investigation of an inquiry question backed by data in a relational database requiring the use of analytic tools in a Python or R environment to an audience of knowledgeable non-experts.
6. Compare the design goals and features of relational databases to those of NoSQL databases in context of a particular analytic application of a chosen data domain.

1. Relational database management systems
2. Primary-foreign key relationships
3. Normalized database schemas
4. Structured Query Language (SQL)
5. Bourne Again Shell (BASH)
6. Remote database connectivity tools (e.g. SSH Tunneling)
7. Python’s cursor and resultset objects
8. NoSQL databases and file databases
9. Primary key Indexing
10. Database views

• Information Literacy
• Quantitative & Scientific Reasoning

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