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Data science lab: process and methods

01TWZSM

A.A. 2019/20

2019/20

Data science lab: process and methods

The course, compulsory for the Master degree in Data science and Engineering, is offered on the 1st semester of the 1st year. The course is focused on the design and implementation of data-driven processes, which are commonly exploited today to extract knowledge from data and support decision-making. Specifically, the course initially introduces the data science process, focusing on all its main phases, and then provides the theoretical and practical knowledge about the data mining and basic machine learning algorithms that are commonly used for analyzing large and heterogeneous data. The course introduces also the Python language and the state of the art data mining and machine learning libraries. Many laboratory sessions, based on a learning-by-doing approach, allow experimental activities on all the phases of a standard data science process (e.g., data preparation and cleaning, data exploration and characterization, data mining algorithm selection and tuning, result evaluation) on the most widespread commercial and open-source products.

Data science lab: process and methods

The course, compulsory for the Master degree in Data science and Engineering, is offered on the 1st semester of the 1st year. The course is focused on the design and implementation of data-driven processes, which are commonly exploited today to extract knowledge from data and support decision-making. Specifically, the course initially introduces the data science process, focusing on all its main phases, and then provides the theoretical and practical knowledge about the data mining and basic machine learning algorithms that are commonly used for analyzing large and heterogeneous data. The course introduces also the Python language and the state of the art data mining and machine learning libraries. Many laboratory sessions, based on a learning-by-doing approach, allow experimental activities on all the phases of a standard data science process (e.g., data preparation and cleaning, data exploration and characterization, data mining algorithm selection and tuning, result evaluation) on the most widespread commercial and open-source products.

Data science lab: process and methods

• 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.

Data science lab: process and methods

• 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.

Data science lab: process and methods

• Basic programming skills.

Data science lab: process and methods

• Basic programming skills.

Data science lab: process and methods

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

Data science lab: process and methods

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

Data science lab: process and methods

Data science lab: process and methods

Data science lab: process and methods

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.

Data science lab: process and methods

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.

Data science lab: process and methods

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, Kumar, 'An introduction to data mining', 2 ed., Addison Wesley, 2005.

Data science lab: process and methods

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, Kumar, 'An introduction to data mining', 2 ed., Addison Wesley, 2005.

Data science lab: process and methods

Modalità di esame: Prova scritta (in aula); Progetto individuale;

Data science lab: process and methods

Data science lab: process and methods

Exam: Written test; Individual project;

Data science lab: process and methods

The exam includes an individual project assigned during the course and a written part. The final score is defined by considering the evaluation of the individual project and the written part. The individual project consists in designing and implementing a data science process for solving a data analytics task. The evaluation of the individual 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 and lasts 1 hour. It includes multiple choice questions, based on solving exercises related to the theoretical part of the course (data cleaning, data preprocessing, data mining algorithms, data science process). The maximum grade for the individual project is 20. The maximum grade for the written part is 12. The final grade is given by the sum of the two parts. The exam is passed if the grade of the individual project is greater than or equal to 12, the grade of the written part is greater than or equal to 7, and the overall grade is greater than or equal to 18. Students can use textbooks or notes during the written part.

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