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Бакалаврская программа «Социология и социальная информатика»

Information Systems

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
8
Кредиты
Статус:
Курс обязательный
Когда читается:
1-й курс, 1, 2 модуль

Преподаватели

Course Syllabus

Abstract

The course covers theoretical and practical basics of working with quantitative data in the social sciences. We will start with the foundations of interacting with Excel, namely spreadsheet structure and basic formulas. During more advanced sessions we will study logic functions, pivot tables, and text data. A significant part of the course will be devoted to the programming language R. We will study different types and classes of objects within R as well as math and logical operators. Data subsetting in base R will be complemented by data manipulation and aggregation with tidyverse. The course will also introduce students to data visualisation with ggplot2 and tidyverse packages. Our last meetings will focus on a brief overview of several advanced R packages.
Learning Objectives

Learning Objectives

  • Explain the place of a person in the Information System
  • Introduce students to the data analysis tools such as Excel and R
  • Understand how the concept of Information Systems can be applied to social sciences
Expected Learning Outcomes

Expected Learning Outcomes

  • Able to run basic functions in Excel
  • Able to use Latex for making a presentation or short report
  • Know basic principles of programming in the framework of working with R
Course Contents

Course Contents

  • Introduction to Information Systems:
    1. How can students apply the IS to their career? 2. What is the information? How is it connected to data? 3. What is data analysis and Data Science?
  • Introduction to Excel and Word / Latex:
    1. Why Excel (Spreadsheets) is an important tool 2. Basic functions for the analysis in Excel: Rows and Columns, Cells, Data types, Sort & Filter 3. Data. Types of data. Observations and variables. Categorical data (nominal, ordinal), numeric data (discrete, continues) 4. Formulas: IF, Maximum, Count, Math operations, SUMIF, Unique values, Calculations with dates 5. Making reports using Microsoft Word / Latex
  • Advanced analysis in Excel:
    1. Logic functions; 2. Manipulation with the data; Pivot tables; 3. Applying functions to texts: Text data in excel: ДЛСТР (LEN); СТРОЧН (LOWER); ПОДСТАВИТЬ (SUBSTITUTE); СЖПРОБЕЛЫ (TRIM); Operator & or CONCATENATE (СЦЕПИТЬ); ЕЧИСЛО (ISNUMBER); НАЙТИ (FIND)
  • Introduction to R & Rstudio:
    1. The interface of Rstudio; Making reports using RMarkdown 2. Types of objects: vectors, data frames, lists, indexes 3. Classes of objects: numeric, factor, character, integer Math and logical operations in R, subsetting data
  • Data manipulation using Rstudio:
    1. tidyverse: filter(), select(), arrange(), mutate(), %>%, %in%, group_by(), summarise(), count() 2. ggplot2: layers of visualization in ggplot2, geom_boxplot(), geom_bar(), geom_hist(), geom_point(), geom_line() 3. stringr: str_c(), str_detect(), str_extract(), str_replace(), str_replace_all(), str_remove(), str_count() 4. Descriptive statistics 5. Short review of other useful packages: networks (igraph+ggraph), texts (tidytext), advanced vis (plotly), interactive systems (Shiny), statistical analysis (caret), web-scrapping (rvest)
Assessment Elements

Assessment Elements

  • non-blocking Seminar Preparation and Activity
  • non-blocking Weekly homeworks
  • non-blocking Excel + Word / Latex (Midterm 1)
    First homework consists of two tasks. Via the first one, students are supposed to demonstrate basic skills of manipulating data and answering research questions (10 questions) with the help of Microsoft Excel. Second one is oriented on creation of basic reports in Microsoft Word / Latex.
  • non-blocking Data aggregation and visualization in R (Midterm 3)
  • non-blocking Basics of R (Midterm 2)
  • non-blocking Report based on given data
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.15 * Basics of R (Midterm 2) + 0.2 * Data aggregation and visualization in R (Midterm 3) + 0.1 * Excel + Word / Latex (Midterm 1) + 0.25 * Report based on given data + 0.15 * Seminar Preparation and Activity + 0.15 * Weekly homeworks
Bibliography

Bibliography

Recommended Core Bibliography

  • Field, A. V. (DE-588)128714581, (DE-627)378310763, (DE-576)186310501, aut. (2012). Discovering statistics using R Andy Field, Jeremy Miles, Zoë Field.
  • Роберт И., Кабаков - R в действии. Анализ и визуализация данных в программе R - Издательство "ДМК Пресс" - 2014 - 588с. - ISBN: 978-5-97060-077-1 - Текст электронный // ЭБС ЛАНЬ - URL: https://e.lanbook.com/book/58703

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