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.
Class Deals by MOOC List - Click here and see Udacity's Active Discounts, Deals, and Promo Codes.
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.
What You Will Learn
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
Prerequisites and Requirements
The ideal student should have the following skills:
Programming experience in Python or a willingness to read a little documentation to understand examples and exercises throughout the course.
The ability to perform rudimentary system administration on Windows or Unix
At least some experience using a unix shell or Windows PowerShell will be helpful, but is not required. No prior experience with databases is needed.
Why Take This Course
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.
This course concludes with a final project where students incorporate what they have learned to address a real-world data analysis problem.
