Learning goals:

• Perform analysis on data stored in relational and non-relational database systems to power strategic decision-making.
• Learn to determine, create, and execute SQL and NoSQL queries that manipulate and dissect large scale datasets.
• Leveraging the power of SQL commands, functions, and data cleaning methodologies to join, aggregate, and clean tables, as well as complete performance tune analysis to provide strategic business recommendations.
• Apply relational database management techniques to normalize data schemas in order to build the supporting data structures for a social news aggregator.

Learn how to execute core SQL commands to define, select, manipulate, control access, aggregate and join data and data tables. Understand when and how to use subqueries, several window functions, as well as partitions to complete complex tasks. Clean data, optimize SQL queries, and write select advanced JOINs to enhance analysis performance. Explain which cases you would want to use particular SQL commands, and apply the results from queries to address business problems.

Project: DEFORESTATION EXPLORATION

Databases need to be structured properly to enable efficient and effective querying and analysis of data. Build normalized, consistent, and performant relational data models. Use SQL Database Definition Language (DDL) to create the data schemas designed in Postgres and apply SQL Database Manipulation Language (DML) to migrate data from a denormalized schema to a normalized one. Understand the tradeoffs between relational databases and their non-relational counterparts, and justify which one is best for different scenarios. With a radical shift of paradigms, learn about MongoDB and Redis to get an understanding of the differences in behaviors and requirements for non-relational databases.

Project: UDIDDIT, A SOCIAL NEWS AGGREGATOR