Data Wrangling with MongoDB

In this course, we will explore how to wrangle data from diverse sources and shape it to enable data-driven applications. Some data scientists spend the bulk of their time doing this!

Students will learn how to gather and extract data from widely used data formats. They will learn how to assess the quality of data and explore best practices for data cleaning. We will also introduce students to MongoDB, covering the essentials of storing data and the MongoDB query language together with exploratory analysis using the MongoDB aggregation framework.

This is a great course for those interested in entry-level data science positions as well as current business/data analysts looking to add big data to their repertoire, and managers working with data professionals or looking to leverage big data.

This course is also a part of our Data Analyst Nanodegree.

• Data Extraction Fundamentals
• Assessing the Quality of Data,Intro to Tabular Formats,Parsing CSV
• Data in More Complex Formats
• XML Design Principles,Parsing XML,Web Scraping
• Data Quality
• Sources of Dirty Data,A Blueprint for Cleaning,Auditing Data
• Working with MongoDB
• Data Modelling in MongoDB,Introduction to PyMongo,Field Queries
• Analyzing Data
• Examples of Aggregation Framework,The Aggregation Pipeline,Aggregation Operators: $match, $project, $unwind, $group
• Case Study - OpenStreetMap Data
• Using iterative parsing for large datafiles,Open Street Map XML Overview,Exercises around OpenStreetMap data