FROM DATA TO KNOWLEDGE

The course is one of the training activities of the Minor in Computer and Data Science. The aim is to get the student familiar with the main statistical tools for data analysis and knowledge.
The course provides knowledge of descriptive statistics and key basic concepts of inference, as well as skills in the use of specific programs for analyzing data and reporting.

At the end of the course, the student will be able to apply suitable methodologies depending on the context and data of interest, with the aim of how to interpret and communicate the obtained results.

1. Knowledge and understanding:
- to know the main tools for graphical representation and summary of a dataset
- to know the basic concepts of statistical inference
- to know the main tools for writing and combining statistical reports

2. Ability to apply knowledge and understanding:
- to use specific programs for data analysis and reporting
- to use the appropriate terminology in all the processes of application and communication of the acquired knowledge

3. Ability to judge:
- to apply the acquired knowledge in a specific context, identifying the most appropriate methods

4. Communication skills:
- to present in a clear and exhaustive way the results obtained from a statistical analysis, both in written and oral form
- to know how to interact with the other students and with the instructor during the classes and on the virtual forum

5. Learning skills:
- to use and integrate information from notes, books, slides, practical lab sessions and personal researches
- to assess the achieved knowledge through quizzes, exercises and assignments during the course

Notions in mathematics at the level of high school and basic ability in the use of computer.

The course provides a practical introduction to statistics, through examples and case studies. The aim of the first two weeks of the course is to get the students familiar with the most useful statistical techniques for summarizing and representing data sets, as well as the R program (https://cloud.r-project.org/ ) and the RStudio interface (https://www.rstudio.com/ ) for the synthesis, representation and analysis of the data and final reporting. The last week is devoted to more case studies, which are presented and discussed in detail. Theoretical presentations are always motivated by practical examples and applications to different contexts.

In particular, the statistical part is made up of
- elements of descriptive statistics: population and sample; types of variables; graphical representations and summaries for numerical variables and factors; relationships among variables
- hints of stochastic uncertainty: statistical error and how it is related to statistical inference
- introduction to regression methods

Ismay, Chester & Kim, Albert. (2019). "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse". Available at moderndive.com
Grolemund, Garrett, & Wickham, Hadley. (2017). "R for Data Science". Available at r4ds.had.co.nz

The achievement of the course objectives is assessed through a written exam.

The exam consists of a statistical report to be written in R and based on a case study never seen before, but chosen in order to measure
1. the theoretical knowledge of the course topics,
2. the ability to apply them for solving real data problems.

A total score exceeding 30 corresponds to 30 with honours. During the written test the use of books, notes, or electronic media is allowed, while collaboration between students is not. No other reporting software is allowed except R.
An example of exam will be available in Moodle.

Lectures will be in presence and online (dual mode - lessons recordings are guaranteed). Use of Moodle e-learning platform for discussions, learning assessment and sharing of the course material. Open-source programs for data analysis and reporting.

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