Software Engineering for Data Scientists and Programmers

Explore software engineering for data scientists and programmers, covering process models (waterfall, prototyping, incremental, rad, spinal), agile methods, unified modeling language, architecture, testing, and cost estimation.

Explore how software engineering uses scientific methods to design and deliver quality software, not programming, addressing the software crisis and emphasizing quality attributes such as correctness and portability.

Explore five software process models—linear sequential (waterfall), prototyping, rapid application development, incremental, and spiral—covering their workflows, advantages, and when to apply each in real projects.

Master the agile development model and its principles, including working software, incremental delivery, customer collaboration, and responding to change, with Kanban and Scrum frameworks.

Explore a four-step process of software requirements engineering—feasibility, elicitation and analysis, specification, and validation—aligning client needs with contracts through user, system, and software design specifications, plus functional and non-functional constraints.

Explore system modeling by analyzing context models, process models, data flow diagrams, behavioral models (data processing and state machines), semantic data models, and object models to design clear software architectures.

Explore how software architectural designs define components and interfaces, covering two-tier and three-tier client-server models, thin versus fat clients, and distributed system architecture with object request broker.

Learn real-time system design by mapping sensors and actuators to timely responses for periodic and unpredictable stimuli, using cooperative processes and a real-time operating system to meet strict timing constraints.

Explore component based software engineering (CBC) by identifying candidate components, defining their interfaces, and composing them through sequential, hierarchical, and additive methods to deliver a scalable system with reusable components.

Explore the full spectrum of software testing, from unit and model tests to integration, system, and acceptance testing, guided by test plans and debugging, and maintenance testing.

Explore software cost estimation techniques, from algorithmic models like Kokomo to expert judgment and Delphi, top-down and bottom-up methods, and learning oriented approaches using regression and machine learning.