With this function you can construct your weekly calendar of lessons, which is customized on the basis of the courses that you intend to follow. Warning: the personal schedule does not replace the presentation of the study plan! It's an informal tool that can help you better manage the organization of class attendance before the study plan presentation. After the study plan presentation we recommend you to use the Lecture timetable service in your Online Services.
To create your customized schedule follow these instructions:
Click on the "Enable" link to proceed. You will be asked your surname and first name in order to determine your alphabetic grouping.
To add or remove courses from your personal schedule, use the small icons which are found next to the courses:
addition of the course
removal of the course
selection of the section of the Laboratory of Architecture (Note: the effective area in which the teaching will be carried out will be determined after the presentation of the Study Plans)
The sidebar on the left displays the number of lessons included in schedule. There are also these commands:
View the schedule: allows the viewing of the weekly synoptic schedule
Delete the schedule: cancels the selections made
When you have finished the entry, you can print the calendar you have made.
Course completely offered in italian
Course completely offered in english
The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching. These CFUs include:
Subject taught jointly with companies or organizations
Blended Learning & Flipped Classroom
Massive Open Online Courses (MOOC)
School of Industrial and Information Engineering
(Master of Science degree)(ord. 270) - MI (434) Engineering of Computing Systems
T2A - Sistemi e applicazioni dell'Informatica
DATA QUALITY: MAXIMIZING VALUE THROUGH MODELING, ASSESSMENT AND IMPROVEMENT
Credits (CFU / ECTS)
The course introduces the basic concepts, models and techniques of the data quality. It aims to provide the tools to assess and improve the quality of data used in different processes in order to avoid errors and inefficiencies.
Data errors, inconsistencies or delays most of the times negatively affect the output of the processes (from business processes to pure computational process). Most of the times these problems are due to the poor quality of the data used. Such issue is perceived as important in different fields and for different data sources (e.g., structured databases, logs, social media content, sensor values). One of the main goals of Data Quality research is to assess and eventually increase the reliability and value of the data in use. In recent years, several comprehensive methodologies for the Data Quality management have been proposed. They include the techniques and procedures to analyze data quality problems, define Data Quality dimensions, measure and improve data quality levels.
This course aims to:
- introduce the basic elements of Data Quality management;
- provide an overview of the current techniques used to assess the most used data quality dimensions in different data sources, i.e., accuracy, precision, completeness, timeliness and consistency. The course shows how the formulas and methods used for assessment vary on the basis of the type of data sources and consequently on the type of data, e.g., numerical vs. text values, structured vs. unstructured data;
- discuss the main data quality issues in data fusion: duplicate detection and conflict resolution;
- illustrate the techniques to improve data quality levels. The course presents both value-based improvement (e.g., data cleaning) and process-based improvement techniques; and
- discuss the main data quality open issues in new field such as IOT and big data.