ECTS - Software Product Management
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 Lecturer(s) |
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| Course Objectives | |
| Course Learning Outcomes |
The students who succeeded in this course;
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| 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 | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | To be able to use mathematics, science and engineering knowledge in solving engineering problems related to information systems. | |||||
| 2 | Design and conduct experiments in the field of informatics, analyze and interpret the results of experiments. | |||||
| 3 | Designs an information system, component and process according to the specified requirements. | |||||
| 4 | Can work effectively in disciplinary and multidisciplinary teams. | |||||
| 5 | Identify, formulate and solve engineering problems in the field of informatics. | |||||
| 6 | Acts in accordance with professional ethical rules. | |||||
| 7 | Communicates effectively both orally and in writing. | |||||
| 8 | Gains awareness of the necessity of lifelong learning. | |||||
| 9 | Learn about contemporary issues. | |||||
| 10 | To be able to use modern engineering tools, techniques and skills required for engineering practice. | |||||
| 11 | Knows project management methods and recognizes international standards. | |||||
| 12 | Develop informatics-related engineering products and prototypes for real-life problems. | |||||
| 13 | Contributes to professional knowledge. | |||||
| 14 | Can do methodological scientific research. | |||||
| 15 | Produce, report and present a scientific work based on an original or existing body of knowledge. | |||||
| 16 | Can defend 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 | ||