Learning objectives
The aim of the course is to lay a foundation for analysis of real-world data.
At the end of the course, students will have learned the appropriate skills to analyze complex data.
Requirements
Elements of descriptive statistics and probability theory.
Contents
The course is organized as follows.
First part (30 hours):
Introduction to R and RStudio.
Data visualization.
Data wrangling and tidy data.
Statistical foundations.
Predictive analysis: regression and machine learning.
Second part (30 hours):
Simulation.
Data querying: R and SQL.
Geospatial data.
Text as data.
In order to obtain the 4 CFU of the course “Data Analysis for social scientists Mod 1”, it is necessary to pass the first part of this course. The remaining 4 CFU will be obtained after the Mod 2 (Professor Parrotta).
Both parts are necessary for the 8 CFU course.
Methods of evaluation
4 CFU course.
Learning assessment will take place through two assignments and a final test.
The assignments will include exercises and the evaluation of each assignment will weigh 15% of the total.
The final test, which will weigh 20% of the total, will be a written test with multiple choice questions. The aim of the final test is to verify the understanding of all the statistical techniques addressed during the course.
8 CFU course.
Learning assessment will take place through two assignments and a final test.
The assignments will include exercises and the evaluation of each assignment will weigh 25% of the total.
The final test, which will weigh 50% of the total, will be a written test with multiple choice questions. The aim of the final test is to verify the understanding of all the statistical techniques addressed during the course.
Suggested reading list
Modern Data Science with R (2nd ed.) by Benjamin S. Baumer, Daniel T. Kaplan and Nicholas J. Horton. Chapman and Hall/CRC. ISBN-13: 978-0367191498
https://parch.unisi.it/usiena/leganto.php?idcourse=21856
More information
R programming language (R Core team) will be used for data analysis.
E-learning page: https://elearning.unisi.it/course/view.php?id=9998