Getting and Cleaning Data

2020/2021

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

School of Linguistics

Course type:

Elective course

When:

2 year, 3 module

Instructor

Lander, Yury

Course Syllabus

Abstract

Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data. The Johns Hopkins University: https://www.coursera.org/learn/data-cleaning

Learning Objectives

• to introduce students to the basic ways that data can be obtained
• to introduce students to the basics of data cleaning and how to make data “tidy”

Expected Learning Outcomes

• applies data cleaning basics to make data "tidy"
• understands common data storage systems
• obtains usable data from the web, APIs, and databases
• uses R for text and date manipulation

Course Contents

• Finding data and reading different file types
• The most common data storage systems
• Organizing, merging and managing the data you have
• Text and date manipulation in R

Assessment Elements

• online course
• discussion with a HSE instructor
• online course
• discussion with a HSE instructor

Interim Assessment

• Interim assessment (3 module)
  0.3 * discussion with a HSE instructor + 0.7 * online course

Bibliography

Recommended Core Bibliography

• Mailund, T. (2017). Beginning Data Science in R : Data Analysis, Visualization, and Modelling for the Data Scientist. New York: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1484645

Recommended Additional Bibliography

• Wickham, H., & Grolemund, G. (2016). R for Data Science : Import, Tidy, Transform, Visualize, and Model Data (Vol. First edition). Sebastopol, CA: Reilly - O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1440131