Кто читает:: Департамент больших данных и информационного поиска

Статус:: Курс обязательный

Когда читается:: 3-й курс, 1-4 модуль

Преподаватели

Джха Аника

Димова Елена Анатольевна

Кирьянов Владислав Владимирович

Макаров Артём Максимович

Ратников Федор Дмитриевич

Шабанова Диана Сундаровна

Full Syllabus Ask Question

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

Introduction to Research in Data Science & Business
Identifying Research Problems, Aims, and Objectives in Business Contexts
Literature Review
Research Design & Methodology
Data Collection, Preparation, and Analysis
Interpreting Research Results
Discussion & Critical Analysis
Reporting & Presenting Research
Industry Insights & Practical Applications

Assessment Elements

Participation & Attendance
Students are expected to attend classes and actively participate in discussions and group work.
Class Assignments & Quizzes
Home Assignments & Projects
Research Proposal (written)
What is a Research Proposal? A research proposal is a detailed plan outlining the topic, objectives, methodology, and significance of your intended research. It demonstrates your understanding of the subject, the relevance of your study, and the feasibility of your approach. The proposal helps guide your research process and provides a basis for evaluation by your supervisor. What is Expected? Students are expected to identify a relevant research problem within Data Science and Business Analytics, review existing literature, formulate clear research questions, and propose appropriate methods for data collection and analysis. The proposal should be well-structured, concise, and demonstrate critical thinking and understanding of the research process. Length of the Research Proposal: The research proposal should typically be between 1500 to 2000 words (approximately 3-4 pages), excluding references.
Defense of the Topic of a Term/Course Paper
Purpose of the Defense: The defense provides an opportunity for students to present their research topic, justify its significance, and demonstrate their understanding of their proposed research approach. It assesses the student’s clarity of thought, depth of knowledge, and ability to communicate effectively. What is Expected during the Defense: Students should prepare a clear and concise presentation summarizing their research topic, objectives, methodology, and expected outcomes. They should be ready to answer questions, defend their choices, and discuss their research plan confidently.
Defense of the Term/Course Paper
Purpose of the Defense: The defense allows students to present and justify their research work, demonstrate their understanding of the topic, and engage in scholarly discussion. It evaluates their communication skills, depth of knowledge, and ability to critically analyze their research. What is Expected during the Defense: Students should prepare a structured presentation summarizing their research problem, objectives, methodology, key findings (if applicable), and conclusions. They should be able to answer questions, defend their approach, and clarify their research contributions.
Final Test

Interim Assessment

2025/2026 3rd module
0.17 * Defense of the Topic of a Term/Course Paper + 0.15 * Research Proposal (written) + 0.1 * Participation & Attendance + 0.29 * Home Assignments & Projects + 0.29 * Class Assignments & Quizzes
2025/2026 4th module
0.2 * Class Assignments & Quizzes + 0.25 * Final Test + 0.25 * Defense of the Term/Course Paper + 0.2 * Home Assignments & Projects + 0.1 * Participation & Attendance

Bibliography

Recommended Core Bibliography

Doing statistical analysis : a student's guide to quantitative research, Thrane, C., 2023
Practical research: planning and design, Leedy, P. D., 2010
Qualitative research methods, Hennink, M., 2012
Research methods and methodologies in education, , 2017

Recommended Additional Bibliography

Business research methods, Bell, E., 2019
The international encyclopedia of communication. Vol.3: Communication professions and academic research - digital divide, , 2008
The SAGE handbook of action research : participative inquiry and practice, , 2013

Authors

Dimova Elena Anatolevna

Бакалаврская программа «Прикладной анализ данных»

Контакты

Research Seminar "Data Analysis in Business"