This repository contains some my work from the SQL Udacity Nanodegree.
When it comes to extracting insights from stored data, SQL is one of the most versatile tools available. 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.
Basic SQL, SQL Joins, SQL Aggregations, SQL Subqueries & Temporary Tables, SQL Data Cleanning, SQL Window Functions, SQL Advanced Joins and Performance Tuning
Course Project: Deforestation Exploration
SQL is most commonly used to manipulate and analyze data to inform decision making. In this project, you will act as a data analyst for an organization on a mission to reduce deforestation around the world and to raise awareness about this important environmental topic. First, you’ll clean any erroneous values in a table, join that table to another lookup table to bring in a new categorical and quantitative variable, and return a new view of all categories greater than a reference value. Then, you will create and execute SQL queries to perform calculations using variables from those disparate data sets to answer questions for stakeholders. Your analysis will help you better understand which countries and regions around the world seem to have forests that have been shrinking in size, and also which countries and regions have the most significant forest area. Lastly, you will compile your answers and summarize your analysis into a report that can be shared to a leadership team.
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 anunderstanding of the differences in behaviors and requirements for non-relational databases.
Normalizing Data, DDL, DML, Consistency with Constraints, Performance with Indexes, Intro to Non-Relational Databases
Course Project: Uddidit, A Social News Aggregator
Many of today’s most popular web applications have supporting database structures that allow them to customize and aggregate information within seconds. In this project, you will build the supporting data structures for Uddidit, a social media news aggregator site. First, you’ll investigate the provided data model for potential errors such as lack of normalization, consistency rules, and proper indexing. Then, you will create a new, normalized database using DDL based on the denormalized one that is provided. Lastly, you will write DML queries to migrate the data from the denormalized schema to their normalized schema.