Delivered at:: Big Data and Information Retrieval School

Course type:: Compulsory course

When:: 1 year, 3, 4 module

Instructor

Maximova, Daria

Full Syllabus

Abstract

This course is aimed at building initial competencies in the field of working with data. The course will cover the basic topics that are required to safely and efficiently use digital technologies and Internet resources. It will also consider tools for scientific research, paper design, presentation of results. In addition, specialized topics related to the application of modern technologies in the humanities will be considered.

Learning Objectives

This course is an adaptation of two university-wide courses, Digital Literacy and Statistics for Data Analysis, for BA students in Foreign Languages and Intercultural Communication. The course will cover the basic topics that are necessary for the safe and effective use of digital technologies and Internet resources. Tools for conducting research, presentation and publishing of the results will also be considered. In addition, the statistics foundations necessary for research result processing will be considered.

Expected Learning Outcomes

Is able to retrieve a bibliographic reference from Google Scholar
Calculates descriptive statistics such as measures of central tendency and measures of dispersion and is able to interpret the results obtained
Determines outliers and missing data, is able to process them
Is able to apply MS Excel logical functions for table processing
Is able to assemble collections of bibliographic descriptions and articles, add material to Zotero via browser, explorer or manually, create reference lists in a few clicks
Is able to cite literature sources correctly
Is able to create aggregated and pivot tables
Is able to create numbered lists, split text into columns, optimize work with text, create styles (e.g. headings)
Is able to create slides, copy content and/or style, change slide background, copy formatting, save and export slides in various formats
Is able to determine data type
Is able to determine the version of the installed operating system
Is able to distinguish between different data types
Is able to do basic data visualisations
Is able to format solid text attributes: font, alignment, margins, spacing
Is able to preprocess and do primary data analysis
Is able to protect personal data from fraudsters and malicious software
Is able to transform data from one type to another, type in formulae, expand
Is able to work with graphic and tabular data
Is able to work with images and text
Is familiar with the concepts of machine learning, different types of machine learning models, evaluation algorithms
Knows and is able to use modern technologies to preserve cultural heritage
Knows general terms related to statistics and data analysis
Understands how search engines work
Understands how the Internet works
Understands the threats of the digital space

Course Contents

Computer Literacy
Computer Security
Internet and Search
Basic Text Technologies
IT and Academic Software
Working with Tabular Data
Data Culture
Basics of Slide-Making in MS PowerPoint
Statistics: An Introduction
Tabular Data Preprocessing
Descriptive Statistics
Z-score. Processing of missing and outlier data.
Correlation. Calculation and interpretation of correlation coefficient. Linear regression. Calculation and interpretation of a single- and multivariable linear regression.

Assessment Elements

Digital Literacy control work: carried out as an independent assessment
Data Analysis control work: carried out as an independent assessment
Digital Literacy online course
Data Science Statistics online course
Tests
Project

Interim Assessment

2021/2022 4th module
0.2 * Data Analysis control work: carried out as an independent assessment + 0.2 * Tests + 0.1 * Data Science Statistics online course + 0.1 * Digital Literacy online course + 0.2 * Project + 0.2 * Digital Literacy control work: carried out as an independent assessment

Bibliography

Recommended Core Bibliography

Slager, D. (2016). Essential Excel 2016 : A Step-by-Step Guide. [Berkeley]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1361984

Recommended Additional Bibliography

Дюк, В., & Фомин, В. (2008). Интеллектуальный анализ данных в гуманитарных областях. Программные Продукты и Системы, (3). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsclk&AN=edsclk.15549904

Bachelor’s Programme 'Foreign Languages and Intercultural Communication'

Data Culture