DATA MANAGEMENT AND VISUALIZATION
knowledge of relational model
written exam related to the theoretical question and a project work.
The project work should be realized by at maximum 3 students and must satisfy at least 2 of 3 of the following requirements
1) the amount of data globally managed must be more than 2 gigabyte
2) two or more datasets with different data format and model must be integrated
3) data must be collected or analysied in real time
A discussion of the project work will conclude the evaluation
At the end of the module students will be able to select, design and query a database (relational or not) according to their application needs
Students will be able to use a NoSql database management system to acquire, memorize and query semi structured data
At the end of the course students will have acquired skills in analysis, development and evaluation of the quality of complex and interactive infographics.
nosql models and architecture
big data architecture
big data engine
0. Data Type
1. NoSQL models
1.1. Cap theorem
1.2. Document based system
1.3. Graph db
1.4. key value and columnar models
2. Data architecture
3. Architectures for big data analysis
3.1. Map Reduce
3.2. Main components (Hive, Spark, Flink, Impala..)
4. Data quality
5 Data integration
6. Data life cycle
- Introduction to Visualization.
- Data Transformation into sources of knowledge through visual representation.
- Requirements and heuristics for high-quality visualizations.
- Charts and standard views: relevance and appropriateness.
- Advanced and innovative tools for data visualization and advanced quantitative analysis.
- The evaluation of the quality of visualizations and infographics.
o Qualitative assessment: expert and heuristic;
o Quantitative assessment: user tasks; inferential statistical techniques.
o Validated psychometric questionnaires and their analysis and understanding.
- Workshops in which students will acquired practical skills to:
o extract unstructured data from web (import.io, kimono, etc.)
o manage and manipulate data in tabular format (google spreadsheet, excel, etc.)
o explore and present static data (RAWGraphs, Gephi, illustrator, etc.)
o explore and build interactive data visualizations (Tableau Public, Carto)
o design a "data-driven" narrative in a data journalism context.
G. Harrison Next Generation Databases, Apress, 2015
Yau, N. (2011). Visualize this: the FlowingData guide to design, visualization, and statistics. John Wiley & Sons.
Scientific articles and class pack provided by the lecturers.
Lectures and exercise in room and on PC
Lectures with the support of slideware, discussion of practical cases through the forum, discussion of practical home-work projects.