Прикладное программное обеспечение

Бакалавриат 2026/2027

Статус: Курс по выбору (Социология и социальная информатика)

Кто читает: Департамент социологии

Где читается: Санкт-Петербургская школа социальных наук

Когда читается: 3-й курс, 1, 2 модуль

Охват аудитории: для своего кампуса

Преподаватели: Ляпин Илья Викторович

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

Retrieve data from a relational database by formulating valid SQL queries using SELECT, WHERE, JOIN, GROUP BY constructs and basic aggregate functions
Prepare and transform table data in Python using the pandas library: filtering, grouping, joining, whitespace cleaning, and type casting.
Apply the principles of visual perception (preattentive attributes, gestalt laws, data-ink ratio) when constructing graphs and justify the choice of graph type for a specific analytical task.
Design a dashboard using the Dashboard Canvas methodology: formulate a target task, identify a user and a set of indicators, develop a layout before connecting data.
Implement interactive dashboards in the Fastboard BI platform: connect data sources, configure widgets and filters, organize layout and navigation.
Present analytical results to the customer in a storytelling format, correctly interpreting the data and reasonably answering questions.

Course Contents

Introduction to BI Analytics and the Role of a Data Analyst
Data Extraction: SQL Basics for the Analyst
Data Preparation in Python: Pandas Library
Principles of Data Visualisation
BI platform Fastboard: interface and working with data
Dashboard Design: Dashboard Canvas and Typology
Building complex dashboards in Fastboard
Data storytelling and presentation of results

Assessment Elements

Activity at seminars
SQL + Python homework
Test
Interim project: first dashboard
Final project: dashboard and defence

Interim Assessment

2026/2027 2nd module
0.1 * Activity at seminars + 0.15 * SQL + Python homework + 0.15 * Test + 0.2 * Interim project: first dashboard + 0.4 * Final project: dashboard and defence

Bibliography

Recommended Core Bibliography

Beaulieu, A. (2009). Learning SQL : Master SQL Fundamentals: Vol. 2nd ed. O’Reilly Media.
Knaflic C.N. Storytelling with data: a data visualization guide for business professionals. New Jersey: Wiley, 2015.
McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925

Recommended Additional Bibliography

9781491912140 - Vanderplas, Jacob T. - Python Data Science Handbook : Essential Tools for Working with Data - 2016 - O'Reilly Media - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1425081 - nlebk - 1425081
Corbett, J. (2002). Edward Tufte, The Visual Display of Quantitative Information, 1983. CSISS Classics. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.ADE6E165
Wilke, C. V. (DE-588)121247104, (DE-627)081180608, (DE-576)292607067, aut. (2019). Fundamentals of data visualization a primer on making informative and compelling figures Claus O. Wilke. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.103046006X

Authors

LYAPIN ILYA VIKTOROVICH

Course Syllabus