Applied Data Science Project (ADSP 01TXXSM)
2023-2024 1st Semester
The course is offered in the first semester of the 2nd year. The course is taught in English.
The main objective of the course is to learn how to develop an artificial intelligence solution utilizing a data science approach. The topics of the course range from the design, development and management aspects to the communication of the project leading to the finalization of the solution.
The goal of this course is to let students work on a long running project and learn how to manage all project steps (problem specification, task assignment, design and implementation of the solution, testing, milestones management, writing of intermediate and final reports, result communication).
Laboratory activities will expose students with first-hand experience with data science methodologies in collaboration with international companies and applied research institutes.
Topics
- Building an artificial intelligence solution with a data science approach [slides]
- Data Science project pillars and tips [slides]
- Design: life and human centered design
- Develop: foundation models and domain-adaptation
- Manage: GANTT e work breakdown structure
- Communicate: paper, deliverable and slides
- Model and Data-centric projects [slides]
- Foundation Models [slides]
- Transfer learning and Domain adaptation [slides]
- Impact of a project and SGDs [slides]
- Artificial intelligence ethics [slides]
- Tools
- Project design tools:
- Project development tools
- Project management tools: WBS, Work packages and tasks, Milestones, GANTT [slides]
- Project communication tools:
- Success stories [slides]
- Project proposals [slides]
Expected Learning Outcomes
- Knowledge of first-hand computational tools to address data science projects
- Knowledge of the design best practices and tools
- Knowledge of management strategies and tools
- Knowledge of communication tools and skills
- Knowledge of the solution impact
- Hands on experience with a real data science project offered by companies and research institutes
Pre-requirements
- Statistics
- Data mining
- Machine learning and Deep learning
- Python language
- Relational, NOSQL, graph databases (non mandatory)
Course structure
The course is structured in three folds:
- Introduction to the concepts to perform a project
- Introduction to the tools to put in place the concepts
- Laboratory sessions for the execution of the projects and meetups with the company key resources (project managers and project leaders) to - successfully execute the assigned projects.
Reading materials
Copies of the slides used during the lectures will be made available. All teaching material is downloadable from both the teaching portal and this website.
Reference books:
- Machine Learning Yearning, by Andrew Ng
- Data Science from Scratch, Joel Grus
- Harvard Business Review Project Management Handbook: How to Launch, Lead, and Sponsor Successful Projects, by Antonio Nieto-Rodriguez
- Oxford Guide to Effective Writing and Speaking: How to Communicate Clearly, by John Seely
- The Design of Everyday Things: Revised and Expanded Edition, by Donald Norman
- Noessel C. Designing Agentive Technology. AI That Works for People. Rosenfeld, 2013
Teaching Team
Giuseppe Rizzo (owner of the course, prof of AI and Data Science tools), Antonella Frisiello (prof of Service Design and UX), Giuseppe Tipaldo (prof of Communication), Alessandro Fiori, Edoardo Arnaudo, Bartolomeo Vacchetti (teaching assistants)
Previous editions
Share material
The entire course material is shared with license CC BY 4.0
Contacts
For any question, feel you free to drop an email to giuseppe.rizzo@polito.it