• Knowledge of the main phases characterizing a data science process. • Knowledge of the major data mining algorithms for classification, regression, clustering, and association rule mining. • Knowledge of the Python language. • Knowledge of the major data mining and machine learning libraries. • Ability to design, implement and evaluate a data science process. • Ability to design, implement and evaluate analytics scripts in the python language. • Ability to use and tune data mining and machine learning algorithms.

• Basic programming skills.

• Data science process: main phases (0.4 cfu) • Data collection, cleaning, transformation and enrichment, and feature engineering (0.5 cfu) • Data mining algorithms: classification, regression, clustering, and association rule mining (1.5 cfu) • Introduction to Python and data mining and machine learning libraries (e.g., scikit-learn) (1.5 cfu) • Case study analysis (0.6 cfu) • Data science process design in the lab (3.5 cfu)

The course includes lectures and practices on the lecture topics, and in particular on data science process design, data preprocessing and data mining algorithms (4.5 cfu). Students will prepare a written report on a group project assigned during the course. The course includes laboratory sessions on data science process design and data analytics (3.5 cfu). Laboratory sessions allow experimental activities on the most widespread commercial and open-source products.

Copies of the slides used during the lectures, examples of written exams and exercises, and manuals for the activities in the laboratory will be made available. All teaching material is downloadable from the course website or the teaching Portal. Book (only a few chapters needed) - Tan, Steinbach, Karpatne, Kumar, 'Introduction to data mining', 2 ed., Pearson, 2019

Lecture slides; Text book; Exercises; Exercise with solutions ; Lab exercises; Lab exercises with solutions; Video lectures (current year); Self-assessment tools;

Exam: Group project; Computer-based written test in class using POLITO platform;

The exam includes a group project and a written part. The final score is defined by considering both the evaluation of the group project and the written part. The teacher may request an integrative test to confirm the obtained evaluation. Learning objectives assessment The written part will assess - the knowledge of the data preparation techniques and the major data mining algorithms for classification, regression, clustering, and association rule mining. - the working knowledge of the Python language and the major data mining and machine learning libraries. The group project will assess - the ability to design, implement and evaluate a complete data science process, including the evaluation and tuning of machine learning algorithms. - the ability to design, implement and evaluate analytics scripts in the python language. Exam structure and grading criteria The group project consists in designing and implementing a data science process for solving a data analytics task. The project is assigned before the start of the exam session and its score is valid for the entire exam session. The evaluation of the group 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 written part covers the theoretical part of the course. It includes multiple choice and box-to-fill questions, based on solving exercises related to the theoretical part of the course (data cleaning, data pre-processing, data mining algorithms, data science process). For multiple choice questions, wrong answers are penalized. The score of each question will be specified in the exam text. The written exam lasts 90 minutes. Textbooks, notes, electronic devices of any kind are not allowed. The maximum grade for the group project is 12. The maximum grade for the written part is 20. The final grade is given by the sum of the two parts. The exam is passed if the grade of the group project is greater than or equal to 8, the grade of the written part is greater than or equal to 10, and the overall grade is greater than or equal to 18. If the final score is strictly greater than 31 the registered score will be 30 with honor.

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.