AUTONOMOUS, DISTRIBUTED AND PERVASIVE SYSTEMS-3

Academic year
2021/2022 Syllabus of previous years
Official course title
AUTONOMOUS, DISTRIBUTED AND PERVASIVE SYSTEMS-3
Course code
PHD156-3 (AF:364605 AR:193148)
Modality
On campus classes
ECTS credits
2
Degree level
Corso di Dottorato (D.M.45)
Educational sector code
INF/01
Period
Annual
Course year
1
Where
VENEZIA
Moodle
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Contribution of the course to the overall degree programme goals
This is one of the three modules of the [PHD156-3] AUTONOMOUS, DISTRIBUTED AND PERVASIVE SYSTEMS course. The module deals with some computational statistics methods useful for the analysis of problems of interest in the field of computer science.
Expected learning outcomes
Regular and active participation in the teaching activities offered by the course will enable students to:

1. Knowledge and understanding:
1.1 To know some of the most important computational statistical methods for addressing problems of interest within the computer science field.

2. Ability to apply knowledge and understanding:
2.1 To know how to apply statistical methods.
2.2. To know how to apply autonomously the basic computational tools of the R environment.
2.3 To know how to use autonomously the R programming language to modify existing code or to write new code for data analysis.


3. Ability to judge:
3.1 To be able to select the most suitable statistical methods for the problem at hand.

4. Communication skills:
4.1 To be able to communicate the results to the various stakeholders.
4.2 To be able to interact with the lecturer and the other students during the theoretical lessons and practical applications.

5. Learning skills:
5.1 To be able to take lecture notes to integrate and clarify the content of the referral teaching material.
5.2 To be able to self evaluate by addressing the lecturer’s questions and solving exercises.
Pre-requirements
Basic knowledge of statistics, mathematics, coding.
Contents
1) Quality 4.0: introduction statistical quality control in the Industry 4.0 era. Shewhart, CUSUM, EWMA charts for product and services. LCL, UCL, ARL computation.

2) Application of control charts to electronics manufacturing processes. Managing Software Process Improvement through statistical process control.

3) Statistical methods for software and hardware scalability analysis.
Referral texts
Open source books on R
Scientific papers
Lecture notes
Assessment methods
Presentations of a brief research paper on the application of a computational statistics method to computer science.
Teaching methods
The module consists of
a) theoretical lessons describing the various concepts and methods
b) practicals with data analyses and result discussion and communication.
Teaching language
English
Further information
None.
Type of exam
written
Definitive programme.
Last update of the programme
31/05/2021