Project scoping
Objectives
This course unit provides students with some exposure to the main activities and challenges that current scoping processes face, namely in the role of a Project Manager/ Translator. The course provides, on the one hand, concepts and methodologies that can be applied in every project and, on the other hand, specific examples concerning more technical and data related projects. The Project Scoping course prepares students to lead a project's ideation phase, equipping them to have fruitful discussions with internal and external project owners, giving them the tools to structure the challenge and its solution development, as well as to identify and deal with potential risks and maximize their attention to the project's key success factors. It also takes into account projects developed internally, within the context of a specific organization, and projects established with clients and external partners. In a global context where data takes a growing leading role in organizations, but where the majority of AI projects still fail, it's crucial to strengthen students' capacity to understand the importance of project scoping and how a structured and systematic project's kick-off can maximize the likelihood of a successful implementation.
General characterization
Code
2599
Credits
3.5
Responsible teacher
Lénia Mestrinho
Hours
Weekly - Available soon
Total - Available soon
Teaching language
English
Prerequisites
n/a
Bibliography
Provost, F., Fawcett, T. (2013). Data science for business: [what you need to know about data mining and data-analytic thinking]. Sebastopol, Calif.: O'Reilly.
Marr, B., Ward, M. (2019). Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Wiley
Moustafaev, J. (2014). Project Scope Management: A Practical Guide to Requirements for Engineering, Product, Construction, IT and Enterprise Projects (Best Practices in Portfolio, Program, and Project Management) 1st Edition.
Sahay, A. (2018). Business Analytics: A Data-Driven Decision Making Approach for Business, Volume I. Business Expert Press.
Ghani, R. (2016). Scoping Data (for Social Good) Projects. Data Science for Social Good
Teaching method
The classes of this course will be taught through different methods, with a constant focus on active learning. The main concepts will come with real-world examples and "what if" questions, promoting a collaborative discussion in class. Key success factors of Project Scoping will be demonstrated through the case method, allowing students to put themselves in the role of those with challenges and carefully think through problems, arriving at their thoughts and conclusions. Students will also have the chance to work in groups, in a specific project, to apply the scoping process as they learn it in class. To offer a first-person perspective into the way course contents live within organizations, guest speakers will be invited to enrich students with field stories and distinct points of view.
Evaluation method
The final grade will be calculated according to this formula:
Final grade = 50% Final Exam + 25% Quizzes + 20% Group Work + 5% Class Participation
Subject matter
1. Introduction to Project Scoping, Data Science Basics, and Analysis of Data Projects that Solve Business Challenges
2. Deep Dive on the Project Scoping Elements: Key Topics, Real-World Examples, Tools & Enablers
3. Scope Changes, Key Success Factors and Measuring Business Impact with Data