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 data sets, management of missing data
• Descriptive data analysis: classification of variables and description of their distribution in the sample (univariate, bivariate and multivariate) through tables, summary measures and indices of association
  
• Graphical representations
• Linear regression model
• Probability of an event: comparing groups through relative risk and odds ratio

Readings/Bibliography

Materials (slides and Python scripts) will be provided by the lecturer.

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. Students should bring their own laptop.

In view of the type of activity and the adopted teaching methods, the attendance at this activity requires the prior participation of all students in Modules 1 and 2 on safety training in the workplace, in e-learning mode.

Assessment methods

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

Teaching tools

Material provided by the lecturer will be available on Virtuale.

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