DATA ANALYSIS

In today's data-driven world, the ability to extract valuable insights from large and complex datasets is highly relevant after across various fields and disciplines. The DATA ANALYSIS course aims to provide students with with a solid background on the main concepts and techniques to effectively collect, clean, analyze, and interpret data to make informed decisions.

The course is intended to provide students with an overview of some of the main aspects of data analysis. Through a combination of theoretical notions and practical sessions, students will learn about different types of data and the appropriate statistical techniques to analyse them. They will also be introduced to the statistical software R, and will learn some of the most important commands to analyse data on such a platform.

Students are expected to have some basic understanding of the mathematical concepts of variable, distribution, and statistic (mean, median, standard deviation...). Moreover, students are expected to have good computer utilisation skills.

The course will cover the following topics:
- Introduction to data analysis: types of data, collection methods, sources, principles and guidelines to handle and analyse data
- Introduction to the software for data analysis (R)
- Importing, cleaning, modifying and saving data
- Descriptive statistics (mean, median, variance, standard deviation), correlation, main distributions
- Data visualization: bar plot, scatter plot, line plot, box plot, histogram
. Inference and hypothesis tests: mean tests, variance test, correlation test, ANOVA
- Regression analysis: bivariate linear regression, multivariate linear regression, non-linear regression
- Spatial analysis: vector data, raster data, vector analysis, visualization

- Garrett Grolemund & Hadley Wickham, R for Data Science, 2nd edition
- Peter Dalgaard, Introductory Statistics with R (Statistics and Computing), 2nd Edition
- Måns Thulin, Modern Statistics with R: From wrangling and exploring data to inference and predictive modelling
- Jonathan Campbell & MIchael Shin, Essentials of Geographic Information Systems

Assessment
- 10% participation (>90%)
- 30% home assignments (three assignments worth 10% each)
- 60% final exam, composed of: written exam (multiple-choice + open questions) accounting for 30% of the total final exam’s weight; lab exam (computer exercises) accounting for 70% of the total final exam’s weight

written

The course will be based on both a theoretical of concepts underyling data analysis, and on a very practical approach of "learning by doing", also thanks to the strong emphasis on interactive work and lab sessions.

Office hours will be available upon appointment throughout the course duration and in the two weeks after the final examination.