Introduction to Data Wrangling

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Course Resources

Datasets used in this course

You can download the full datasets used in this course. The ones used in the course UI are usually trimmed versions of these:

Downloadable Materials

You can download Supplemental Materials, Lesson Videos and Transcripts from Downloadables (bottom right corner of the Classroom) or from the Dashboard (first option on the navigation bar on the left hand side).

Installing additional Modules in Python

  • Requests
  • xlutils (xlrd/xlwd)
  • BeautifulSoup
  • or `pip install requests xlutils beautifulsoup4
    ([pip][6] is a package management system used to install and manage software packages written in Python)

Exercise and Example Code

You can download the code here as zip, or check out the repo on GitHub.

Course Syllabus

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

Case Study - OpenStreetMap Data

  • Using iterative parsing for large data files
  • Open Street Map XML Overview
  • Exercises around OpenStreetMap data

Final Project: OpenStreetMap Data

  • Use important skills from data munging to improve OpenStreetMaps data for a part of the world that you care about and give back to the community.
  • You can find the final project grading rubric here. It also contains final project submission instructions for students who are enrolled in the full course experience.