Data Wrangling with MongoDB

Lesson 1: Data Extraction Fundamentals

- Assessing the Quality of Data - Intro to Tabular Formats - Parsing CSV - Parsing XLS with XLRD - Intro to JSON - Using Web APIs

Lesson 2: Data in More Complex Formats

- Intro to XML - XML Design Principles - Parsing XML - Web Scraping - Parsing HTML

Lesson 3: Data Quality

- What is Data Cleaning? - Sources of Dirty Data - Measuring Data Quality - A Blueprint for Cleaning - Auditing Validity - Auditing Accuracy - Auditing Completeness - Auditing Consistency - Auditing Uniformity

Lesson 4: Working with MongoDB

- Data Modelling in MongoDB - Introduction to PyMongo - Field Queries - Projection Queries - Getting Data into MongoDB - Using mongoimport - Operators like $gt, $lt, $exists, $regex - Querying Arrays and using $in and $all Operators - Changing entries: $update, $set, $unset

Lesson 5: Analyzing Data

- Examples of Aggregation Framework - The Aggregation Pipeline - Aggregation Operators: $match, $project, $unwind, $group - Multiple Stages Using a Given Operator

Lesson 6: Case Study - OpenStreetMap Data

- Using iterative parsing for large datafiles - Open Street Map XML Overview - Exercises around OpenStreetMap data - Final Project Instructions

• Shannon Bradshaw - Shannon is Director of Education at MongoDB, managing MongoDB University's in-person training and free online courses. Prior to joining MongoDB, Shannon was an Associate Professor of Computer Science at Drew University with research interests in user experience, information science, and the semantic web. For the past several years, Shannon has divided his time between academia and industry. He has trained software engineers at Goldman Sachs, designed text-retrieval systems at Morgan Stanley, and built many trading and analytics applications at boutique firms in the financial industry.