• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Digital Product Management

2022/2023
Academic Year
ENG
Instruction in English
5
ECTS credits
Delivered at:
Department of Marketing (Nizhny Novgorod) (Faculty of Management (Nizhny Novgorod))
Course type:
Elective course
When:
4 year, 1, 2 module

Instructor

Course Syllabus

Abstract

Product management is one of the most complicated processes in organizations because it encompasses almost all functions such as: finance, marketing, R&D, manufacturing, sales etc. At the same time strategies of companies in real corporate world more and more often rely on innovations, new products, new technologies which are directly dependent on proficiency in product management. The course is for students who strive to improve their knowledge and skills in main domains of digital product management. Its structure follows body of knowledge developed by Product Development and Management Association (PDMA). Students are highly encouraged to continue their education for successful professional certification of PDMA. Throughout the course students work in project teams on assignments which reflect main stages and approaches to product development and management. Instructor acquaints students with frameworks, concepts, and models used within each phase. Afterwards students consciously choose concepts and tools, which seem relevant for particular stage. Students will learn all contemporary frameworks, approaches, and tools currently applied by generalist and business-oriented product managers. Course will not address more specific technical issues, which are usually dealt by engineering-oriented product managers. Students will go through all main activities performed by real product managers.
Learning Objectives

Learning Objectives

  • To learn all contemporary frameworks, approaches, and tools currently applied by generalist and business-oriented product managers.
  • To master basic skills which are necessary for successful development and management of digital products.
Expected Learning Outcomes

Expected Learning Outcomes

  • Can apply Scrum, Lean startup, Kanban in product management
  • Can develop value proposition canvas
  • Can integrate voice-of-customer into product management
  • Can use different techniques to generate ideas for new products
  • Can use MVP to test main assumptions about value
  • Can work with data science team to refine product
  • Know how to adjust marketing research techniques for different types of products
  • Know how to evaluate and screen ideas
  • Know main responsibilities of product managers
Course Contents

Course Contents

  • Product manager as a position in a company: responsibilities and qualifications.
  • Ideation and hypothesis development for products
  • Market analytics and voice-of-customer for product manager
  • Agile approaches to product development
  • Data science fundamentals for product managers
Assessment Elements

Assessment Elements

  • non-blocking Participation
  • non-blocking Assignments
  • non-blocking Test
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    0.4 * Test + 0.4 * Assignments + 0.2 * Participation
Bibliography

Bibliography

Recommended Core Bibliography

  • Bruce T. Barkley, Project Management in New Product Development, McGraw-Hill © 2008 https://library.books24x7.com/toc.aspx?bookid=23646

Recommended Additional Bibliography

  • A Guide to the Project Management Body of Knowledge (PMBOK® Guide), Sixth Edition, 2017. Режим доступа: http://library.books24x7.com/bookshelf.asp
  • Cadogan, John, et al. Cross-cultural and cross-national consumer research, Emerald Publishing Limited, 2015. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=2070198.
  • Fundamentals of Qualitative Research
  • Moran, A. (2015). Managing Agile : Strategy, Implementation, Organisation and People. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=969008
  • Vanderplas, J.T. (2016). Python data science handbook: Essential tools for working with data. Sebastopol, CA: O’Reilly Media, Inc. https://proxylibrary.hse.ru:2119/login.aspx?direct=true&db=nlebk&AN=1425081.