Software Product Management (SE456) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Software Product Management	SE456	Area Elective	3	0	0	3	5

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	Elective Courses
Course Level	Natural & Applied Sciences Master's Degree
Mode of Delivery
Learning and Teaching Strategies	.
Course Coordinator
Course Lecturer(s)	Dr. Haluk Altunel
Course Assistants
Course Objectives
Course Learning Outcomes	The students who succeeded in this course; To understand the role and responsibilities of a ProdM. To identify market gaps and user needs and formulate a technically feasible product vision and strategy. To effectively prepare Product Requirement Documents (PRD) and develop strategies for managing technical debt. To understand DevOps and Release Management processes and up-to-date approaches, and to effectively make data-driven decisions.
Course Content	The Product Manager?s role, market analysis, and business models. Product Discovery and identifying technical assumptions. Technical feasibility and PRDs. Agile, Release, Quality and Technical Debt Management. Team Collaboration. Product Analytics. Go-to-Market and Product-Led Growth. AI integration with MLOps principles.

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Product Manager’s role and responsibilities.	Reading lecture hand-outs
2	Market and Technical Business Model Analysis	Reading lecture hand-outs
3	Product Vision and Strategy	Reading lecture hand-outs
4	Goal Setting and Metrics	Reading lecture hand-outs
5	Product Discovery and Requirement Gathering	Reading lecture hand-outs
6	Opportunity Assessment, Prioritization, and Technical Feasibility	Reading lecture hand-outs
7	Project Presentations	Reading lecture hand-outs
8	Agile and Technical Debt Management	Reading lecture hand-outs
9	PRD and Definition of Done (DoD) Principles	Reading lecture hand-outs
10	UX/UI, System Architecture, and Engineering Collaboration: The impact of system architecture, such as Microservices and APIs, on product decisions. Communication and alignment with Engineering and Design teams.	Reading lecture hand-outs
11	Product Analytics: Funnel and Cohort analysis. Logging and Data Collection Infrastructure Strategies. Technical interpretation of metrics and A/B test results	Reading lecture hand-outs
12	Go-to-Market (GTM), Release, and Quality Management: Launch and Release Plans, introduction to DevOps and similar up-to-date processes. ProdM management of QA (Quality Assurance) processes. Product-Led Growth (PLG)	Reading lecture hand-outs
13	Artificial Intelligence (AI) Integration (Week 1): The use of AI/ML Features in Software Products. Viewing AI as a product feature (e.g., recommendation, automation). Requirement Management and Data Infrastructure Strategy	Reading lecture hand-outs
14	Artificial Intelligence (AI) Integration (Week 2): AI/ML Management Challenges and MLOps. Integration of AI features into the technical architecture and MLOps (DevOps for ML) principles. Data bias and ethical challenges. Project Presentations	Reading lecture hand-outs
15	Final Examination	Preparation for the final examination.
16	Final Exam	Preparation

Sources

Other Sources	1. The Product Book: How to Become a Great Product Manager, Product School, Carlos González de Villaumbrosia & Josh Anon, 1st Edition, Product School, 2017.
	2. PDMA Essentials: New Product Development, Global Product Development and Management Association (PDMA), 3rd Edition, Wiley, 2017.
	3. Inspired: How to Create Tech Products Customers Love, Marty Cagan, 2nd Edition, Wiley, 2018.
	4. The Lean Product Playbook: How to Innovate with Minimum Viable Products and Rapid Customer Feedback, Dan Olsen, 1st Edition, Wiley, 2015.
	5. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 1st Edition, O'Reilly Media, 2022.

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	-	-
Presentation	1	10
Project	2	12
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	1	20
Final Exam/Final Jury	1	40
Toplam	5	82

Percentage of Semester Work	60
Percentage of Final Work	40
Total	100

Course Category

Core Courses	X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	Applies knowledge of mathematics, science, and engineering.
2	Designs and conducts experiments, analyzes and interprets experimental results.
3	Designs a system, component, or process to meet specified requirements.
4	Works effectively in interdisciplinary fields.
5	Identifies, formulates, and solves engineering problems.
6	Has awareness of professional and ethical responsibility.
7	Communicates effectively.
8	Recognizes the need for lifelong learning and engages in it.
9	Has knowledge of contemporary issues.
10	Uses modern tools, techniques, and skills necessary for engineering applications.
11	Has knowledge of project management skills and international standards and methodologies.
12	Develops engineering products and prototypes for real-life problems.
13	Contributes to professional knowledge.
14	Conducts methodological and scientific research.
15	Produces, reports, and presents a scientific work based on original or existing knowledge.
16	Defends the original idea generated.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	3	48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class	12	2	24
Presentation/Seminar Prepration	1	4	4
Project	2	12	24
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	1	5	5
Prepration of Final Exams/Final Jury	1	15	15
Total Workload			120