Продуктовая разработка: от идеи до ПРОДа

Бакалавриат 2026/2027

Статус: Курс обязательный (Прикладной анализ данных)

Кто читает: Департамент больших данных и информационного поиска

Где читается: Факультет компьютерных наук

Когда читается: 3-й курс, 1, 2 модуль

Охват аудитории: для своего кампуса

Преподаватели: Самойлов Артем Александрович

Язык: английский

Кредиты: 4

Контактные часы: 56

Full Syllabus Ask Question

Abstract

The course is built as an end-to-end product project. Teams of 3 to 5 students take a data product through the full cycle: from the first conversation with a customer and the framing of business requirements to launching a working solution in production and measuring its impact. The emphasis is on the product side of development rather than on algorithms. Students learn to uncover the real business problem, write a technical specification, design the architecture, collect and label data, bring a prototype to a demo, and prove its value to potential stakeholders through metrics and a financial-effect estimate. Cases span three domains: corporate (B2B), consumer (B2C), and government (B2G). The course closes the gap between technical skills and the ability to build a product that solves the customer's problem and delivers a measurable result.

Learning Objectives

To teach students to take a data-driven product through its entire lifecycle, from identifying a business need to launching in production and evaluating the result. The course builds a practical skill of making product and engineering decisions under real constraints on time, budget, and data. The purpose is to prepare students for the real world of IT.

Expected Learning Outcomes

Interview stakeholders, separate the real business pain from a proposed solution, and formulate a product vision with success criteria.
Prioritize requirements using RICE, MoSCoW, and a Value vs Effort matrix.
Produce analytical documents for a specific audience: a one-pager for executives, a research report, and a pitch for an investment committee.
Write a technical specification with functional and non-functional requirements, acceptance criteria, and a Definition of Done.
Run exploratory analysis of an unfamiliar dataset and extract insights from unstructured data.
Design a data collection and labeling process, write instructions for annotators, and compute inter-annotator agreement.
Design a solution architecture that accounts for trade-offs in cost, latency, scalability, and maintainability, and estimate total cost of ownership.
Build a working prototype in Python with a Streamlit interface and prepare it for handover to engineering.
Evaluate solution quality through A/B tests and benchmarking, run systematic error analysis, and formulate improvement hypotheses.
Prepare a pitch and demo, compute product metrics, ROI, and payback period, and build a product roadmap.

Course Contents

Gathering and analyzing business requirements
Preparing analytical and research reports
Writing technical specifications and requirements
Working with unstructured data and extracting insights
Building and labeling datasets
System Design and solution architecture
Building a Proof of Concept (PoC)
Evaluation, testing, and optimization
Preparing the demo and pitching the project
Post-launch: metrics, monitoring, and product development
Closing sessions (based on the intake survey)

Assessment Elements

Technical specification
Homework (team)
Pitch
Project (team)
Architecture
Project (team)
Demo Day
Project, final defense (team)
Project
Engineering practice (team)
Brief
Homework (individual)
Report
Homework (individual)
Data
Homework (individual)
Metrics
Homework (individual)
Activity
Class participation (individual)

Interim Assessment

2026/2027 2nd module
Final = 0.27*I1 + 0.10*Architecture + 0.15*Demo Day + 0.25*Project + 0.08*Metrics + 0.15*Activity; where I1 = (4*Brief + 6*Report + 5*Pitch + 5*TS + 7*Data) / 27. Each element appears in exactly one formula. The two components carry weights 0.27 (Module 1, via I1) and 0.73 (Module 2), though, activity is calculated in Module 2 only. Effective element weights match the source design and sum to 100%. No element exceeds 70% of the final grade. The course has no separate exam; the final grade is cumulative.

Bibliography

Recommended Core Bibliography

Cagan, Marty. Inspired: How to Create Tech Products Customers Love. –Wiley, 2018. – ЭБС Books 24x7.
Kleppmann, M. (2017). Designing Data-Intensive Applications : The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1487643

Recommended Additional Bibliography

Knaflic, C. N. (2015). Storytelling with Data : A Data Visualization Guide for Business Professionals. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1079665
Ries, E. (2011). The Lean Startup : How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. New York: Currency. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=733896
Steven N. Kaplan, & Bernadette A. Minton. (2012). How Has CEO Turnover Changed? International Review of Finance, (1), 57. https://doi.org/10.1111/

Authors

Abdulkhakimov Mukhiddin

Course Syllabus