Data Science and Engineering Project

Recommended Prerequisites

Previous courses of the BSc in Data Science and Engineering

Teaching Methods

Project based learning, using a medium-sized software project. Students have to develop a product (in the data science and engineering area) during the semester using concepts, tools and methodologies presented during the lectures a week or two before the deliverables are due. These deliverables focus on the software engineering artifacts (requirements, mockups, architecture and design, quality plan,...), while the laboratory classes focus on assessing the correct usage of processes to ensure the visibility and quality of the work performed.

Learning Outcomes

The student must understand why the complexity of big data systems requires an engineering approach and the different ways to organize the people and activities required to develop a product with quality, namely waterfall, linear and iterative approaches. He or she must understand the differences between them and which one(s) are more adequate for a specific usage context. The student must also be able to use the most common project management techniques, namely PERT/CPM, Gant, Risk analysis and others. The student must be able to describe core aspects the software artifact to be developed using UML modelling formalism. Finally, the student must be able to integrate concepts aquired in the previous courses of the program.

Work Placement(s)

No

Syllabus

1. Introduction to Software Engineering. The nature of software. Kinds of software. Quality of software.

2. Introduction to software development process. Requirements elicitation and analysis. Design. Implementation. Software testing. Waterfall development. Iterative and evolutive development.

3. Introduction to project management. Management activities. Project planning. Project scheduling. PERT/CPM and Gantt diagrams. Risk managements in software projects. Risk identification, analysis, planning and monitoring.

4. UML Language Use case diagrams. Class diagrams. Object diagrams. Interaction diagrams. Sequence Diagrams. Activity diagrams. State diagrams. Deployment diagrams. Mapping UML into code.

Head Lecturer(s)

Nuno Alexandre Martins Seixas

Assessment Methods

Assessment
Exam: 40.0%
Project: 60.0%

Bibliography

Software Engineering, Global Edition, by Ian Sommerville, ISBN-13: 978-1292096131,  Pearson Education 2015