B0085 - DATA ANALYTICS AND VISUALIZATION

Learning outcomes

The lab aims to provide the concepts and tools necessary for data exploration and visualization through a very hands-on learning-by-doing approach. At the end of the lab, students will gain a good knowledge and understanding of the principles behind data analysis and the construction of an effective visualization, consistent with the problem and the users to whom the activity is directed. They will be able to apply the acquired knowledge in order to best communicate the information contained within a data set by constructing visualizations from raw data using the Python programming language and dedicated libraries (e.g., matplotlib, seaborn, plotly). Students will also be able to constructively observe and critique data visualizations built by third parties.

Course contents

Introduction and proper use of the Python programming language to analyze data and report statistical results:

• Data types and variables; creation and management of variables and data sets;
• Descriptive data analysis: frequency distributions, summarizing the distribution of variables, classifying variables, double entry tables;
• Graphical representations: pie charts, bar graphs, histograms, scatter plots, line graphs.

Readings/Bibliography

Materials will be provided by the teacher, in particular both slides (.pdf format) and Python codes/scripts.

 

Follow-up materials:

• Alan Agresti, Maria Kateri, Foundations of statistics for data scientists with R and Python, Taylor & Francis, 2021.
• Philipp Kats , David Katz, Learn Python by building data science applications, Packt, 2019.

Teaching methods

Class lectures.

 

In view of the type of activities and teaching methods adopted, the attendance of this training activity requires the prior participation of all students in Modules 1 and 2 of safety training in the workplace https://elearning-sicurezza.unibo.it, in e-learning mode.

Assessment methods

The exam will be a practical test of data analysis in a computer laboratory.

 

Grading policy

Insufficient <18; Sufficient 18-23; Good 24-27; Very good 28-30; Excellent> 30 cum laude.

Teaching tools

Material provided by the lecturer on Virtuale https://virtuale.unibo.it/.

 

Students with disability or specific learning disabilities (DSA) are required to make their condition known to find the best possibile accomodation to their needs.

Office hours

See the website of Anna Vesely