Бакалавриат
2021/2022
Получение и очистка данных
Статус:
Курс по выбору (Фундаментальная и компьютерная лингвистика)
Направление:
45.03.03. Фундаментальная и прикладная лингвистика
Кто читает:
Школа лингвистики
Где читается:
Факультет гуманитарных наук
Когда читается:
3-й курс, 3 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
5
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
2
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"
- obtains usable data from the web, APIs, and databases
- understands common data storage systems
- 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
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