Real-world data rarely comes clean. Using Python and its libraries, you will gather data from a variety of sources and in a variety of formats, assess its quality and tidiness, then clean it. This is called data wrangling.
You need to be able to work in a Jupyter Notebook on your computer. Please revisit our Jupyter Notebook and Anaconda tutorials earlier in the Nanodegree program for installation instructions.
Begin by 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. Finally, apply relational database management techniques to normalize data schemas in order to build the supporting data structures for a social news aggregator.
Ziad is a seasoned software developer who loves mentoring and teaching. Currently working as an independent contractor, he previously co-founded and taught full-stack web development at DecodeMTL, Montreal's first web development bootcamp.
This project is connected to the Data Wrangling course from Udacity. You have the choice between two databases for this project: SQL and MongoDB. I opted to utilize SQL. The project is broken into steps below.
To see the final code and analysis use this link: https://github.com/AdkinsWx/OpenStreetMap_Udacity/blob/master/Final_Code.ipynb
Make sure all programming exercises are solved correctly in the "Case Study: OpenStreetMap Data" Lesson in the course you have chosen (MongoDB or SQL). This is the last lesson in that section.