Bachelor
2021/2022
Digital Literacy
Type:
Compulsory course
Area of studies:
Business Informatics
Delivered by:
Bachelor's Programme in Digital Product Management
Where:
Graduate School of Business
When:
1 year, 4 module
Mode of studies:
distance learning
Online hours:
20
Open to:
students of one campus
Language:
English
ECTS credits:
6
Contact hours:
36
Course Syllabus
Abstract
People encounter a lot of data at work and outside of work: for example, they look at predicted travel times and choose which mode of transportation to take, analyze prices of products they buy, plan dates for upcoming events, etc. Companies also operate with a lot of data: it can be financial (e.g., costs or revenue) and non-financial (e.g., level of service). This data can be discussed verbally or processed in mind but much more often it is visualized in some way and presented in a way that is easy for different people to work with. When is there a need to present data? For example in the preparation of reports, in presentations on a consulting project, at meetings to change the assortment of the company, when making decisions about inventory management, risk management, transportation, logistics, warehousing, etc. How correctly data is searched, processed, analyzed and visualized often determines a company's decision-making. Despite the evolving processes of digitalization, companies in all areas of business continue to use seemingly basic tools (such as Excel, Power Point and Word, Google Docs) for most data-related tasks (concerning both analysis and presentation) because of their relative simplicity and convenience. Even when using more advanced technologies for these tasks, employees need a basic understanding of why and how data work is needed. That's why this course will remain relevant throughout training and on the job, regardless of the position or area of business. The course is an adaptation of the university-wide Digital Literacy course; specifically for the Management and Digital Innovation program. This course focuses on an introduction to the basic principles of working with data. In particular, the course includes such topics as: preparation of data for processing and analysis, working with data, data aggregation, visualization using graphs and infographics, building a logical structure of the presentation for the most correct presentation of analysis results, computer literacy and security, basics of media communication, work with search engines, etc.
Learning Objectives
- The main goal of our course is to give students a holistic view of how to search for information and data; how to analyze different types of data and how to prepare competent presentation of the analysis results.
Expected Learning Outcomes
- Analyzes the risks of leaks of sensitive information
- Applies basic Excel function knowledge: in particular, knows how to convert data from one type to another, works with combined formulas and functions (simple ones like SUM, dollar sign, useful functions like SUMMESLY and VPR), including formula stuffing, stretching formulas, tables, copying data from one sheet to another, switching cell reference styles, fixing cells in formulas
- Applies basic Word hotkeys
- Applies data filters in spreadsheets and pivot tables
- Applies Excel tools to create selected chart and graph types (including pie charts, bar charts, scatter charts)
- Applies file conversion from one format to another
- Applies primary data processing and analysis
- Applies the basic Power Point hotkeys
- Applies the Power Point tools to create a selected type of infographic/chart, and design a constructed chart
- Applies the principles of working with personal data
- Applies the rules of referencing to academic papers
- Applies the simplest statistical analysis methods for simple forecasts in MsExcel (e.g. trending. correlation analysis, etc.).
- Compares different types of data visualization and applies the selected type to the given task
- Compares formats for images and audio and applies the most appropriate one
- Creates correct and appropriate emails for educational purposes
- Creates formatting of different text components: knows how to create lists of different formats, align and change font, etc.
- Creates or knows how to insert pictures, diagrams, tables, hyperlinks in text
- Creates relevant search queries for finding information on the Internet
- Creates slides, and applies the following skills: copy, copy style, change background of slide, duplicate formatting, save and export slides in different formats
- Creates spreadsheets and uses the search function to solve problems
- Creates various visual elements such as tables, graphs, charts, etc.
- Describes the basic components of media literacy and online communication skills
- Describes the basic ways of structuring information on a slide
- Describes types of machine learning models and their essence, algorithm for assessing model quality
- Determines the version of the operating system installed on the computer
- Develops a plan of work (project/course/report, etc.) for a given topic, based on a search, selection and analysis of sources
- Explains how search engines work
- Explains the concept of machine learning
- Explains the types of structure of the presentation and the situations in which they can be applied
- Identifies the difference between a phishing email and a normal email
- Identifies the features of different types of data
- Makes a list of digital threats
- Names basic Excel hotkeys
- Names the methods of copyright protection on the Internet
- Names transition adapters for connecting incompatible devices
- Solves the task of locating bibliographic references using search engines/Google Scholar/Research Gate
- Verifies the authenticity of information in primary sources
Course Contents
- Computer literacy
- Basics of documents processing
- Internet and media literacy
- Academic literacy and the use of digital technology for research
- The basics of working with data in tables
- Basics of computer security and legal aspects of information technologies.
- Basics of presentation creation
- Working with data
Assessment Elements
- Online course
- Homeworks
- Activities in seminars and lectures
- Final test for the online course
- Final projectThe defence takes place in person. Sample project components: 1. Selection of the project theme (not evaluated) 2. Creating an outline of the project 3. Preparing the theoretical part (brief literature review) 4. Data collection, processing and visualisation of results 5. Preparation of the practical part 6. Submission of the policy brief no later than 7 days before the date of the examination 7. Preparation of the presentation 8. Defending the project during the session
Interim Assessment
- 2021/2022 4th module0.35 * Final project + 0.15 * Final test for the online course + 0.2 * Homeworks + 0.1 * Online course + 0.2 * Activities in seminars and lectures
Bibliography
Recommended Core Bibliography
- Bailey, S. Academic Writing: A Handbook for International Students / Stephen Bailey. – 4th edition. – Oxon: Routledge; Taylor & Francis Group, 2015. – 305 p. – ISBN 978113877668022. - Текст: электронный // DB ProQuest Ebook Central (ebrary) [сайт]. – URL: https://ebookcentral.proquest.com/lib/hselibrary-ebooks/reader.action?docID=1811067&query=Bailey%252C%2BStephen
- Alpaydin, E. (2014). Introduction to Machine Learning (Vol. Third edition). Cambridge, MA: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=836612
- Brian W. Kernighan. (2017). Understanding the Digital World : What You Need to Know About Computers, the Internet, Privacy, and Security. Princeton University Press.
- Freedman, J. J. (2013). Microsoft Word 2013 Plain & Simple. Microsoft Press.
- Held, B., Moriarty, B., & Richardson, T. (2019). Microsoft Excel Functions and Formulas (Vol. Fifth edition). Dulles, Virginia: Mercury Learning & Information. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2051937
- Muir, N. (2013). Microsoft PowerPoint 2013 Plain & Simple. Microsoft Press.
- Vijayan, J. (2016). Google Now Combining Browsing Data With Personally Identifiable Info. EWeek, 1. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsu&AN=119088888
Recommended Additional Bibliography
- Ad A.M. Prins, Rodrigo Costas, Thed N. van Leeuwen, & Paul F. Wouters. (2016). Using Google Scholar in research evaluation of humanities and social science programs: A comparison with Web of Science data. Research Evaluation, (3), 264. https://doi.org/10.1093/reseval/rvv049
- Can Google Scholar and Mendeley help to assess the scholarly impacts of dissertations? (2019). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.C32203D9
- Davenport, T. H. (2014). Big Data at Work : Dispelling the Myths, Uncovering the Opportunities: Vol. [Academic Subscription]. Harvard Business Review Press.
- Farney, T., McHale, N., & Library and Information Technology Association (U.S.). (2013). Maximizing Google Analytics : Six High-Impact Practices. ALA TechSource.
- Hales, J., & Aldrich, L. (2012). Powerpoint Tips & Tricks. [Boca Raton, Florida]: QuickStudy Reference Guides. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1534329
- I. Korotkina B., & И. Короткина Б. (2017). Academic Literacy and Methods of Global Scientific Communication ; Академическая грамотность и методы глобальной научной коммуникации. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.8C60FBE4
- Martín-Martín, A., Orduna-Malea, E., Thelwall, M., & López-Cózar, E. D. (2018). Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories. https://doi.org/10.1016/j.joi.2018.09.002