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IT Product Development

  • Course name: IT Product Development
  • Code discipline: CSE807
  • Subject area: Software Engineering

Short Description

This course has two parts: 1) building and launching a user-facing software product with the special emphasis on understanding user needs and 2) the application of data-driven product development techniques to iteratively improve the product. Students will learn how to transform an idea into software requirements through user research, prototyping and usability tests, then they will proceed to launch the MVP version of the product. In the second part of the course, the students will apply an iterative data-driven approach to developing a product, integrate event analytics, and run controlled experiments.

Prerequisites

Prerequisite subjects

  • CSE101: Introduction to Programming
  • CSE112: Software Systems Analysis and Design
  • CSE122 OR CSE804 OR CSE809 OR CSE812

Prerequisite topics

  • Basic programming skills.
  • OOP, and software design.
  • Familiarity with some development framework or technology (web or mobile)

Course Topics

Course Sections and Topics
Section Topics within the section
From idea to MVP
  1. Introduction to Product Development
  2. Exploring the domain: User Research and Customer Conversations
  3. Documenting Requirements: MVP and App Features
  4. Prototyping and usability testing
Development and Launch
  1. Product backlog and iterative development
  2. Estimation Techniques, Acceptance Criteria, and Definition of Done
  3. UX/UI Design
  4. Software Engineering vs Product Management
Hypothesis-driven development
  1. Hypothesis-driven product development
  2. Measuring a product
  3. Controlled Experiments and A/B testing

Intended Learning Outcomes (ILOs)

What is the main purpose of this course?

The main purpose of this course is to enable a student to go from an idea to an MVP with the focus on delivering value to the customer and building the product in a data-driven evidence-based manner.

ILOs defined at three levels

Level 1: What concepts should a student know/remember/explain?

By the end of the course, the students should be able to ...

  • Describe the formula for stating a product idea and the importance of delivering value
  • Remember the definition and main attributes of MVP
  • Explain what are the main principles for building an effective customer conversation
  • Describe various classification of prototypes and where each one is applied
  • State the characteristics of a DEEP product backlog
  • Elaborate on the main principles of an effective UI/UX product design (hierarchy, navigation, color, discoverability, understandability)
  • List the key commonalities and differences between the mentality of a software engineer and a product manager
  • Explain what is hypothesis-driven development
  • Describe the important aspects and elements of a controlled experiment

Level 2: What basic practical skills should a student be able to perform?

By the end of the course, the students should be able to ...

  • Formulate and assess the product ideas
  • Perform market research for existing products
  • Design effective customer conversations
  • Prototype UI, design and conduct usability tests
  • Prototype user interface
  • Design and conduct usability testing
  • Populate and groom a product backlog
  • Conduct Sprint Planning and Review
  • Choose product metrics and apply GQM
  • Integrate a third-party Analytics tools
  • Design, run and conclude Controlled experiments

Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?

By the end of the course, the students should be able to ...

  • Conduct user and domain research to identify user needs and possible solutions
  • Elicit and document software requirements
  • Organize a software process to swiftly launch an MVP and keep improving it in an iterative manner.
  • Build a data pipeline to monitor metrics based on business goals and assess product progress in regards to design changes.
  • Evolve and improve a product in a data-driven evidence-based iterative manner