Project work for DAND - OSM Data Wrangling

Project 3: Wrangle OpenStreetMap Data In this project, you’ll use data munging techniques, such as assessing the quality of the data for validity, accuracy, completeness, consistency and uniformity, to clean the OpenStreetMap data for a part of the world that you care about. Supporting Lesson Content: Data Wrangling with MongoDB or SQL Lesson Title Learning Outcomes DATA EXTRACTION FUNDAMENTALS ➔ Properly assess the quality of a dataset ➔ Understand how to parse CSV files and XLS with XLRD ➔ Use JSON and Web APIs DATA IN MORE COMPLEX FORMATS ➔ Understand XML design principles ➔ Parse XML & HTML ➔ Scrape websites for relevant data DATA QUALITY ➔ Understand common sources for dirty data ➔ Measure the quality of a dataset & apply a blueprint for cleaning ➔ Properly audit validity, accuracy, completeness, consistency, and uniformity of a dataset WORKING WITH MONGODB ➔ Understand how data is modeled in MongoDB ➔ Run field and projection queries ➔ Import data into MongoDB using mongoimport ➔ Utilize operators like $gt, $lt, $exists, $regex ➔ Query arrays and using $in and $all operators ➔ Change entries using $update, $set, $unset ANALYZING DATA ➔ Identify common examples of the aggregation framework ➔ Use aggregation pipeline operators $match, $project, $unwind, $group SQL FOR DATA ANALYSIS ➔ Understand how data is structured in SQL ➔ Run queries to summarize data ➔ Use joins to combine information across tables ➔ Create tables and import data from csv CASE STUDY: OPENSTREETMAP DATA ➔ Use iterative parsing for large datafiles ➔ Understand XML elements in OpenStreetMap