Научно-исследовательский семинар "Бизнес аналитика и цифровая экономика" (2026/2027 учебный год)

Бакалавриат 2026/2027

Статус: Курс по выбору (Социология и социальная информатика)

Кто читает: Департамент социологии

Где читается: Санкт-Петербургская школа социальных наук

Когда читается: 4-й курс, 1-3 модуль

Охват аудитории: для своего кампуса

Преподаватели: Вознесенская Анна Александровна, Дымова Полина Максимовна

Язык: английский

Кредиты: 3

Контактные часы: 30

Full Syllabus Ask Question

Abstract

This research seminar provides sociology students with interdisciplinary training at the intersection of business analytics and digital economy. The course combines theoretical frameworks with hands-on data analysis skills to explore and analyze current trends and emerging social phenomena in the digital economy. The students will critically examine recent research in digital business models, platform economies, and data-driven decision-making through sociological lenses. The course also offers training of practical skills in data analysis for studying digital consumption patterns, market segmentation strategies and forecasting economic inequalities while addressing fundamental concepts and common models of digital economy.

Learning Objectives

Course objective - to develop a systematic understanding of the digital economy as an environment in which data is a key resource and business analytics is a core tool for managerial decision-making, while also building practical skills in framing analytical problems, working with data, and interpreting results for business.

Expected Learning Outcomes

learn the difference between relational and non-relational databases;
learn metrics and kpis; learn principles of ab testing
create their own BI instance; use this instance for data visualization; learn differences between different BI systems and visualizations
Student can describe the key characteristics of the digital economy, its participants, technologies, and main value-creation models.
Student can describe the ecosystem of a selected digital market, including key players, users, platforms, data flows, and forms of interaction.
Student can explain how digital platforms, multi-sided markets, network effects, and digital ecosystems work.
Student can analyze how digitalization affects business models, consumer behavior, employment, and social relations.
Student can identifie social, ethical, and legal risks associated with the use of data, algorithms, and digital platforms.
Student can distinguishe between a business problem, a research problem, and an analytical task.
Student can formulate research questions and testable hypotheses based on the business context and available data.
Ba able to identifie stakeholders, constraints, and criteria for a successful analytical solution.
Be able to select qualitative, quantitative, and behavioral methods for data collection and analysis in line with the task.
Be able to identifie the required data sources and assesses their sufficiency, quality, and limitations.
Student can select and interprets basic business and product analytics metrics.
Be able to calculate and applie conversion, activation, retention, churn, and other user-funnel metrics.
Student can develop a metric framework for evaluating a business or product decision.
Be able to distinguishe between observations, analytical conclusions, insights, and recommendations and explain the differences between them.
Student can formulate evidence-based recommendations that take into account the available data, limitations, and alternative explanations.
Student presents analytical results in a written report and presentation and explains them in language that is clear to non-specialists.
Student justifies research and analytical decisions in a professional discussion.
Be able to use generative AI tools to support research and analytical work while critically evaluating the outputs.
Be able to uderstand how to gather and refine business requirements.
Can describe and model business processes at a basic level.
Be able to use SQL to extract, aggregate, and prepare data.
Be able to use BI tools to visualize and present results.
Be able to analyze changes in indicators across periods, segments, and channels.
Be able to perform basic customer segmentation and interprets behavior using cohorts and funnels.
Be able to understand the logic of A/B testing and interpret experiment results.
Be able to interpret trends, seasonality, and simple time-series forecasts.
Be able to formulate practical analytical conclusions and recommendations based on data.

Course Contents

Module 1. Topic 1. Introduction to the Digital Economy
Module 1. Topic 2. Digital Platforms and Ecosystems
Module 1. Topic 3. User Behavior in Digital Environments
Module 1. Topic 4. Effects and Social Implications of Digitalization
Module 1. Topic 5. Practical Analysis of a Digital Service
Module 2. Topic 1. Business Analytics and Data-Driven Decision-Making
Module 2. Topic 2. From a Business Problem to an Analytical Task
Module 2. Topic 3. Hypotheses, Indicators, and Metrics
Module 2. Topic 4. Methods and Data for Solving a Business Problem
Module 2. Topic 5. From Analysis to Conclusions and Recommendations
Module 3. Topic 1. Business Process Modeling and BPMN
Module 3. Topic 2. SQL for Business Analytics: Data Extraction and Preparation
Module 3. Topic 3. BI Tools: Visualization and Dashboards
Module 3. Topic 4. Practice with Business Analysis Tools

Assessment Elements

Classwork
Classwork includes practical assignments, case analysis, participation in discussions, and oral justification of proposed solutions.
Homework Assignment No. 1
Homework at the end of Module 1 - "Digital Economy"
Homework Assignment No. 2
During Module 2, "Business Analytics." Topic: Analytical Brief — Framing a Business Problem
Group Project
Students work on the project throughout Modules 1–2; the final defense takes place at the end of Module 2.
Classwork
Classwork includes practical assignments, case analysis, participation in discussions, and oral justification of proposed solutions.
Practical Assignment
Group Project

Interim Assessment

2026/2027 3rd module
Final grade = 0.1·Classwork₁₋₂ + 0.025·HW₁ + 0.025·HW₂ + 0.1·Project₁₋₂ + 0.1·Classwork₃ + 0.05·Practical Assignment₃ + 0.1·Project₃

Bibliography

Recommended Core Bibliography

Basic concepts of probability and statistics, Hodges, J. L., 2005
Basic statistics for business and economics, Kazmier, L. J., 1984
Dekking F. M. et al. A Modern Introduction to Probability and Statistics: Understanding why and how. – Springer Science & Business Media, 2005. – 488 pp.
Kuniavsky, M., Goodman, E., & Moed, A. (2012). Observing the User Experience : A Practitioner’s Guide to User Research (Vol. 2nd ed). Burlington: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=472263
Malik, U., Goldwasser, M., & Johnston, B. (2019). SQL for Data Analytics : Perform Fast and Efficient Data Analysis with the Power of SQL. Packt Publishing.
Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big Data, Big Analytics : Emerging Business Intelligence and Analytic Trends for Today’s Businesses. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=518564
Platform revolution : how networked markets are transforming the economy and how to make them wor..., Parker, G. G., 2016

Recommended Additional Bibliography

9781118387986 - Demidenko, Eugene - Advanced Statistics with Applications in R - 2019 - John Wiley & Sons - http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=2318899 - nlebk - 2318899
Ben-Gad, S. (2016). Platform Revolution: How Networked Markets Are Transforming the Economy and How To Make Them Work for You. Library Journal, 141(5), 120. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=113815587

Course Syllabus