Data Wrangling with MongoDB
About this Course
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 …
Data Wrangling with MongoDB
About this Course
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 Scientists spend most of their time cleaning data. In this course, you will learn to convert and manipulate messy data to extract what you need.
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At the end of the class, students should be able to:
,- Programmatically extract data stored in common formats such as csv, Microsoft Excel, JSON, XML and scrape web sites to parse data from HTML.
- Audit data for quality (validity, accuracy, completeness, consistency, and uniformity) and critically assess options for cleaning data in different contexts.
- Store, retrieve, and analyze data using MongoDB.
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This course concludes with a final project where students incorporate what they have learned to address a real-world data analysis problem.
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lesson 1
Data Extraction Fundamentals
Assessing the Quality of Data
Intro to Tabular Formats
Parsing CSV
lesson 2
Data in More Complex Formats
XML Design Principles
Parsing XML
Web Scraping
lesson 3
Data Quality
Sources of Dirty Data
A Blueprint for Cleaning
Auditing Data
lesson 4
Working with MongoDB
Data Modelling in MongoDB
Introduction to PyMongo
Field Queries
lesson 5
Analyzing Data
Examples of Aggregation Framework
The Aggregation Pipeline
Aggregation Operators: $match, $project, $unwind, $group
lesson 6
Case Study - OpenStreetMap Data
Using iterative parsing for large datafiles
Open Street Map XML Overview
Exercises around OpenStreetMap data
