Бакалавриат
2026/2027
Научно-исследовательский семинар "Анализ данных в бизнесе 2"
Статус:
Курс обязательный (Прикладной анализ данных)
Где читается:
Факультет компьютерных наук
Когда читается:
4-й курс, 1-3 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
4
Контактные часы:
52
Course Syllabus
Abstract
The 4th-year Research Seminar represents the culmination of the student's academic journey, shifting the primary focus from learning analytical methodologies to executing original, independent research for the Final Diploma Thesis (ВКР). The 4th-year seminar operates as an advanced research incubator. It combines rigorous academic workshops aimed at thesis development with cutting-edge "Frontier" industry blocks. Practitioners deliver seminars on state-of-the-art topics (e.g., AI Agents, Quantitative Finance, Mobile Ecosystems) not just as isolated skills, but as potential methodological foundations and data sources for the students' diploma research.
Research methodology (formulating questions, literature review, research design, reporting conventions) is retained as a guided self-study track that students complete independently.
The course aims to:
• Deepen students' ability to identify, articulate, and justify a novel research gap in data science or business analytics.
• Strengthen methodological competence: selecting, justifying, and defending research design choices at a level appropriate for a diploma thesis.
• Expose students to frontier research and industry-frontline problems through practitioner-led blocks and discussion seminars.
• Provide structured milestones and feedback loops that guide students from topic selection to a polished, defensible diploma thesis.
• Cultivate scholarly communication skills: academic writing, peer review, and confident oral defense.
Course Architecture
1. Research Expertise & Thesis Development (Integrated into seminars + guided independent work)
2. Frontier & Practitioner Blocks (In-class seminars led by practitioners and researchers)
3. Milestones (Defenses, discussions, feedback loop, assessments)
Research Methodology (Self-Study). This block is not delivered as a lecture series. Students receive a structured self-study materials and follow it at their own pace. Consultation slots with the course coordinator are available for questions.
Frontier & Practitioner Blocks. These blocks are designed to expose students to the absolute cutting edge of data science in business. Students are encouraged to leverage the tools, datasets, and frameworks introduced here for the empirical chapters of their Diploma Theses.
Milestones. Represents the formal assessment gates of the course, where students demonstrate their cumulative learning through structured milestones. These evaluation points may include a written research proposal, oral defenses and presentations of the thesis paper topic and final project, other assessments, and a final test.
Learning Objectives
- Formulate, defend, and execute a novel, rigorous research project that contributes to the field of Data Science and Business Analytics.
- Critically evaluate state-of-the-art (SOTA) academic papers and industry whitepapers, integrating advanced methodologies (e.g., Agentic AI, Causal Inference) into their own research.
- Extract, clean, and model complex, multi-modal datasets (unstructured text, financial terminal data, mobile telemetry) for empirical analysis.
- Navigate professional financial and analytical platforms (e.g., C-Bonds) to source high-quality data for economic and business research.
- Interpret and critically discuss analytical results, assessing significance, limitations, and business implications.
- Confidently present and defend complex analytical research to both academic committees and industry practitioners.
Expected Learning Outcomes
- Interpret and critically discuss analytical results, assessing significance, limitations, and business implications.
- Formulate, defend, and execute a novel, rigorous research project that contributes to the field of Data Science and Business Analytics
- Critically evaluate state-of-the-art (SOTA) academic papers and industry whitepapers, integrating advanced methodologies (e.g., Agentic AI, Causal Inference) into their own research.
- Extract, clean, and model complex, multi-modal datasets (unstructured text, financial terminal data, mobile telemetry) for empirical analysis.
- Navigate professional financial and analytical platforms (e.g., C-Bonds) to source high-quality data for economic and business research.
- Confidently present and defend complex analytical research to both academic committees and industry practitioners
Course Contents
- Introductory Seminar: Course Navigation & Research Framing.
- Research Methodology (Self-Study).
- Frontier Lab: Recent Advances.
- Research Gaps & Tasks.
- Business Insights from Unstructured Data.
- Artificial Intelligence in Mathematical Finance.
- Data Analytics Applications in the Financial Markets.
- Analytics in the Mobile Ecosystem.
- Financial Analysis on Professional Platforms: C-Bonds.
- Agent Harness, Dissected.
- Applied Deep Dives.
- Thesis paper topic defense (idea, concept, methodology).
Assessment Elements
- Participation & Attendance
- Class Assignments & Quizzes
- Home Assignments & Projects
- Defense of the Diploma/Thesis Topic
- Research Proposal (written)
Interim Assessment
- 2026/2027 3rd module0.1 * Participation & Attendance + 0.29 * Home Assignments & Projects + 0.15 * Research Proposal (written) + 0.29 * Class Assignments & Quizzes + 0.17 * Defense of the Diploma/Thesis Topic
Bibliography
Recommended Core Bibliography
- Doing statistical analysis : a student's guide to quantitative research, Thrane, C., 2023
- Joe F. Hair Jr, Michael Page, & Niek Brunsveld. (2019). Essentials of Business Research Methods. [N.p.]: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2280258
- Mike Lambert. (2019). Practical Research Methods in Education : An Early Researcher’s Critical Guide. Routledge.
- Research and technical writing for science and engineering, Qiu, M., 2022
- Saunders, M. N. K., Thornhill, A., & Lewis, P. (2019). Research Methods for Business Students (Vol. Eighth edition). Harlow, United Kingdom: Pearson Education Limited. Retrieved from https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=5774742
- Ted Gournelos, Joshua R. Hammonds, & Maridath A. Wilson. (2019). Doing Academic Research : A Practical Guide to Research Methods and Analysis. Routledge.
Recommended Additional Bibliography
- Rugg, G., & Petre, M. (2007). A Gentle Guide to Research Methods. Maidenhead: McGraw-Hill Education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=234246
- Монич, И. П. Методология научного исследования: учебные материалы = Research Methods: Teaching Materials : учебное пособие / И. П. Монич, О. А. Баранова. — Чита : ЗабГУ, 2022. — 212 с. — ISBN 978-5-9293-3045-2. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/363413 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.