Бакалавриат
2026/2027
Научно-исследовательский семинар "Анализ данных в бизнесе"
Статус:
Курс обязательный (Прикладной анализ данных)
Где читается:
Факультет компьютерных наук
Когда читается:
3-й курс, 1-4 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
4
Контактные часы:
68
Course Syllabus
Abstract
This course is designed to immerse 3rd-year students in real-world analytical practice through hands-on blocks delivered by industry practitioners. Each practical block is structured around a concrete business problem, a real or simulated dataset, and a step-by-step analytical workflow that students execute during seminars. Research methodology (formulating questions, literature review, research design, reporting conventions) is retained as a guided self-study track that students complete independently and demonstrate through their Research Proposal and Course Paper deliverables. Seminar contact hours are therefore dedicated mostly to applied analytical tasks. The course aims to equip students with both theoretical foundations and practical skills essential for conducting impactful research in applied data science within a business environment.
Course Architecture
1. 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.
2. Practical Analytical Blocks (Seminar Core). Each block below is delivered by an industry practitioner over 2–3 consecutive seminars. Every block follows a common pedagogical frame: Business Context → Data Introduction → Step-by-Step Analytical Workflow → Hands-On Task → Mini-Deliverable. Students work with provided datasets (real or realistically simulated), follow explicit task steps, and produce a graded artefact. The curriculum may also cover different or additional topics than those presented in the course structure, depending on industry trends and emerging challenges, providing a comprehensive view of practical data science applications.
3. Milestones. Represents the formal assessment gates of the course, where students demonstrate their cumulative learning through structured milestones. These evaluation points include a written research proposal, oral defenses and presentations of the course paper topic/concept, and a final test.
Learning Objectives
- Understand the fundamentals of research methodology in data science and business.
- Develop skills to formulate research questions and objectives.
- Conduct comprehensive literature reviews.
- Learn to design and implement appropriate research methods.
- Analyze and interpret research results critically.
- Engage with industry experts to understand practical challenges and solutions in data analysis.
Expected Learning Outcomes
- Explain key research principles and methods in data science and business.
- Develop clear research questions, aims, and objectives based on business challenges.
- Conduct and synthesize comprehensive literature reviews to support research.
- Formulate suitable research frameworks and select appropriate methodologies.
- Collect, preprocess, and analyze data ethically to generate insights.
- Interpret research results critically, assessing their significance and limitations.
- Present research findings clearly through reports and visualizations.
- Apply industry tools and technologies to implement research insights in business.
Course Contents
- Introductory Seminar: Course Navigation & Research Framing.
- Research Methodology (Self-Study).
- Frontier Lab: Recent Advances.
- Intelligence in Action: LLM for Unstructured Data Processing.
- Data Analysis in Retail.
- Understanding Business Functions: How Metrics Drive Decision-Making.
- Advanced Product Analytics and Modeling.
- Demand Forecasting and Predictive Analytics for Retail and Supply Chain.
- ML application in industry.
- Data in motion: building automated processing workflows.
- Course paper topic defense (idea, concept, methodology).
Assessment Elements
- Participation in discussions & Attendance
- Class Assignments & Quizzes
- Home Assignments & Projects
- Defense of the Topic of a Term/Course Paper
- Final Test
Interim Assessment
- 2026/2027 4th module0.25 * Home Assignments & Projects + 0.1 * Defense of the Topic of a Term/Course Paper + 0.25 * Class Assignments & Quizzes + 0.1 * Participation in discussions & Attendance + 0.3 * Final Test
