Introduction to Programming for Statistics

Academic year

2022/2023 Syllabus of previous years

Official course title

Introduction to Programming for Statistics

Course code

PHD176 (AF:411286 AR:222420)

Modality

On campus classes

ECTS credits

3 out of 6 of Introduction to Programming for Statistics and Machine Learning

Degree level

Corso di Dottorato (D.M.45)

Educational sector code

SECS-S/01

Period

1st Semester

Course year

1

Where

VENEZIA

Moodle

Go to Moodle page

Contribution of the course to the overall degree programme goals

Statistical analysis is a powerful tool in environmental studies. Using correct statistical methods and tools can help us to understand the data, as well as to infer potential causal relationship. The course presents statistical tools in R programming environment to analyse climate data with a focus on exploratory data analysis, geospatial analysis and regression approaches.

Expected learning outcomes

Students are expected to attain a basic understanding of R and RStudio, perform basic arithmetic and statistical operations in R. In addition, students are expected to understand basic file formats used in earth observations, and common approaches to read, process and analyse the data.

Pre-requirements

Some basic understanding of any programming language would be useful not required. Undergraduate level understanding of linear algebra and statistics would be useful.

Contents

Introduction to R / Rstudio, Basic Data structures (Vectors, Matrices, Data Frames), Arithmetic/Statistical operations in R, Plots & Handling data in R (Input/Output), Raster operations/NetCDF files in R, some recent packages for Summary Statistics, advanced geo-spatial operations

Referral texts

In addition to the material provided in each lecture (which includes slides, data and scripts), additional information on below weblink are useful:

http://www.statmethods.net/ (Excellent to begin learning R)
https://cran.r-project.org/doc/contrib/ (Very useful resources)
https://cran.r-project.org/doc/manuals/R-intro.pdf (Quick Intro to R)
https://www.r-bloggers.com/tag/rwiki/ (Advance)

Assessment methods

During the course, the students will be asked to participate in interactive sessions (coding skills) and graded on their active engagement and a general understanding of programming concepts. These will count towards 100% for the final grade.

Teaching methods

Each lecture will combine a frontal lecture and in-class activities (hands-on sessions using sample data and analysis/scripts prepared in R). Activities will allow students to become familiar with the methods and tools introduced in the course for the analysis of environmental/geospatial data.

Teaching language

English

Further information

Further details about readings, required data and software installation including practical exercises will be communicated at the beginning of the course and published on Moodle.

Type of exam

oral

2030 Agenda for Sustainable Development Goals

This subject deals with topics related to the macro-area "Climate change and energy" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development

Definitive programme.

Last update of the programme: 09/05/2023