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Applied data science project

01TXXSM

A.A. 2023/24

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Data Science And Engineering - Torino

Course structure
Teaching Hours
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/05 12 D - A scelta dello studente A scelta dello studente
2022/23
The practical and theoretical knowledge acquired by the students during the master degree provide them with the tools to face and solve specific data science and data analytics problems. Further steps that students will likely take when entering the work market are: thinking in the large and applying diverse abilities, together with team mates in order to achieve objectives and build complex systems. The goal of this course is to let them face, for the first time a long running real-world project, proposed by an international company, including all the related technical and management challenges.
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 carry on the development of 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 face, for the first time, 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 to first-hand experience in projects that massively use data science methodologies in collaboration with companies and applied research institutes with an international breadth.
• Knowledge of the agile software development methodologies • Ability to interact with a client, cope with its needs, design a data science project, assess the project performance, and deliver data analytics processes in an incremental fashion.
- 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
• Advanced knowledge of data science processes, data mining and machine learning algorithms, and statistical and mathematical models.
Statistics Data mining Machine learning and Deep learning Python language Relational and NoSQL databases
• Agile software development (0.5 cr.) • Scrum methodology (1.0 cfu) • Data science project development (10 cfu) • Retrospective and critical analysis (0.5 cfu)
Lectures (30 hours) - Building an artificial intelligence solution with a data science approach (1.5h) - Model and Data-centric projects (1.5h) - Data Science project pillars (3h) ---- Design: human-centered design ---- Development: foundation models and domain-adaptation ---- Management: GANTT e work breakdown structure ---- Communication: paper, deliverable, and slides - 10 Practical tips (1.5h) - Impact of a project and SGDs (1.5h) - Data Science project tools (18h) ---- Project design tools -------- Stakeholder maps and user personas -------- User journey ---- Project development tools -------- Functional requirements: From a user research to solution definition -------- Existing foundation models -------- Domain adaptation and downstream tasks -------- Version control and testing ---- Project management tools -------- GANTT -------- Work breakdown structure, work packages and tasks, milestones ---- Project communication tools -------- Project communication -------- Presentation -------- Paper -------- Deliverable - Success stories of past projects (1.5h) - Project proposals (1.5h) Laboratory activities (90 hours) - Generation of the solution
None
None
The course includes lectures and practices on the lecture topics, and in particular on data science process design, data preprocessing and data mining algorithms. Students will prepare an individual written report on an individual project assigned during the course. The course includes laboratory sessions on data science process design and data analytics. Laboratory sessions allow experimental activities on the most widespread commercial and open-source products. The course will be structured into two phases: 1. Introductory lectures, in the classroom with the whole class 2. Project work, in labs and classroom, team based • Projects are provided by external industrial companies and involve commitment by students to develop actually working data science processes • The development will be divided into three/four development iterations (sprints) according to the Scrum development methodology. Each spring will terminate with a demonstration to the customer and teachers, followed by a retrospective (with coordinating teachers), critical appraisal and planning for next iterations.
The course is structured in three folds: 1) Introduction to the concepts to perform a project 2) Introduction to the tools to put in place the concepts 3) Laboratory sessions for the execution of the projects and meetups with the company's key resources (project managers and project leaders) to successfully execute the assigned projects.
Copies of the slides used during the lectures will be made available. All teaching material is downloadable from the course website or the teaching portal.
Copies of the slides used during the lectures will be made available. All teaching material is downloadable from the teaching portal. 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
Modalità di esame: Prova orale obbligatoria; Elaborato progettuale in gruppo;
Exam: Compulsory oral exam; Group project;
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Compulsory oral exam; Group project;
The exam includes a mandatory oral part (20% of the overall mark), and the evaluation of the team project assigned during the course (80% of the overall mark). Learning objectives assessment The oral exam covers all the theoretical concepts introduced during the course and will assess - 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 The team project and the delivered report will assess - Ability to design, implement and evaluate a complete data science project - Ability to use first-hand computational tools to address data science projects - Ability to use management tools - Ability to use communication tools Exam structure and grading criteria The team project consists of managing, designing, and implementing an artificial intelligence solution utilizing a data science approach. Projects are assigned by companies or applied research institutes. The team project is assigned in the first half of the course and its report must be delivered before the end of the course. The team project is also characterized by intermediate milestones. Its score is valid for the entire academic year. The evaluation of the team project is based on the performance and accuracy of the proposed solution, in terms of standard quality measures (e.g., prediction accuracy) and completeness (i.e., in-depth analysis of each phase of the designed process and motivation for selecting given techniques and algorithms). The clearness and completeness of the delivered reports will also be considered. The oral exam covers all the theoretical parts of the course. The score is based on the completeness and clarity of the answers. The maximum grade for the team project is 32. The maximum grade for the oral part is 32. The final grade is given by the weighted average of the two parts (0.8*grade team project + 0.2*grade oral part). The exam is passed if the grade of the team project is greater than or equal to 18 and the grade of the oral part is greater than or equal to 18.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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