Прикладное программное обеспечение

Бакалавриат 2025/2026

Статус: Курс по выбору (Социология и социальная информатика)

Кто читает: Департамент социологии

Где читается: Санкт-Петербургская школа социальных наук

Когда читается: 3-й курс, 2, 3 модуль

Охват аудитории: для своего кампуса

Преподаватели: Морева Юлия Евгеньевна

Язык: английский

Кредиты: 3

Контактные часы: 40

Full Syllabus Ask Question

Abstract

The course “Applied Software” develops in sociology students applied skills in working with data in a modern BI environment: from extracting data from a relational database to designing, implementing and presenting an interactive dashboard. The course is built around an end-to-end project: each student goes through a full cycle of analytical work - from formulating a research question and extracting data with an SQL query to building a dashboard in the Fastboard BI platform and its public defence. The course content is based on modern practice as a data analyst. The base stack is SQL for data extraction, Python (pandas) for preparation and preprocessing, domestic BI platform Fastboard for visualisation and assembly of dashboards. The SQL block is deliberately shortened, since students simultaneously study an independent course “Databases”; here SQL is considered in an applied way - as a tool for obtaining data suitable for visualisation. A significant part of the course is devoted not to tools, but to methodology: principles of visual perception (preattentive attributes, gestalt laws), choosing the type of graph for an analytical task, principles of designing dashboards (typology of dashboards, Dashboard Canvas, BI-as-a-product), as well as storytelling - turning a dashboard into a means of communication with the direct customer. These skills are transferable and do not depend on changing a specific BI platform. The course is aimed at students of sociology without prior training in the field of BI; a basic familiarity with spreadsheet software and basic statistics is sufficient. All technologies are taught from scratch. At the end of the course, the student is able to independently pose an analytical problem, download data, prepare it, and build a dashboard that answers a meaningful research question.

Learning Objectives

• To form students’ understanding of the role of BI analytics in social research, marketing, corporate governance and government analytics;
• Provide practical skills in extracting data from relational databases using SQL for subsequent visualisation;
• Learn basic techniques for preparing and converting tabular data in Python (pandas library);
• Develop an understanding of the theoretical principles of data visualisation and the skills of choosing the correct type of graph for an analytical task;
• Train in the design of dashboards using modern methodology (typology of dashboards, Dashboard Canvas) and their implementation in the Fastboard BI platform;
• Develop skills in public presentation of analytical results and data-based storytelling.

Expected Learning Outcomes

Find relevant data and academic literature
Use the results of analysis to prepare literature reviews
Work with bibliographic databases
Apply network methods to analyze citation data

Course Contents

Introduction
Principles of working with citation databases
Creating bibliometric maps
Proper citation. Instruments for bibliographic data storage and systematization.
Principles of literature review organization

Assessment Elements

In-class participation
Project Proposal
Literature review
Midterm test

Interim Assessment

2025/2026 3rd module
0.3 * In-class participation + 0.1 * Midterm test + 0.2 * Project Proposal + 0.4 * Literature review

Bibliography

Recommended Core Bibliography

Eck, N. J. P. (Nees J. van, & Waltman, L. (Ludo). (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.3AC7DCD5
Wang, G. T., & Park, K. (2016). Student Research and Report Writing : From Topic Selection to the Complete Paper. West Sussex: Wiley-Blackwell. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1108252

Recommended Additional Bibliography

Nicola De Bellis. (2009). Bibliometrics and Citation Analysis : From the Science Citation Index to Cybermetrics. Scarecrow Press.
Wang, J., Veugelers, R., & Stephan, P. (2017). Bias against novelty in science: A cautionary tale for users of bibliometric indicators. Research Policy, (8), 1416. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.eee.respol.v46y2017i8p1416.1436
Yves Gingras. (2016). Bibliometrics and Research Evaluation : Uses and Abuses. The MIT Press.

Authors

MOREVA IULIIA EVGENEVNA

Course Syllabus