CODING FOR DATA SCIENCE | Università degli studi di Bergamo

CODING FOR DATA SCIENCE

Modulo Generico
Codice dell'attività formativa: 
149009-E1

Scheda dell'insegnamento

Per studenti immatricolati al 1° anno a.a.: 
2020/2021
Insegnamento (nome in italiano): 
CODING FOR DATA SCIENCE
Insegnamento (nome in inglese): 
CODING FOR DATA SCIENCE
Tipo di attività formativa: 
Attività formativa Caratterizzante
Tipo di insegnamento: 
Obbligatoria
Settore disciplinare: 
METODI MATEMATICI DELL'ECONOMIA E DELLE SCIENZE ATTUARIALI E FINANZIARIE (SECS-S/06)
Anno di corso: 
1
Anno accademico di offerta: 
2020/2021
Crediti: 
6
Responsabile della didattica: 
Altri docenti: 

Altre informazioni sull'insegnamento

Ciclo: 
Annualità Singola
Obbligo di frequenza: 
No
Ore di attività frontale: 
48
Ore di studio individuale: 
102
Ambito: 
Statistico-matematico
Prerequisites

Previous programming experience is desirable but not requested.

Educational goals

The course aims at providing the basic knowledge of Python language and R software and its integrated development environment RStudio. They are open-source and free software available at http://www.python.org, http://www.r-project.org and https://rstudio.com/, respectively.
At the end of the course the student will know the basics of programming techniques both in Python and R programming languages. In particular, the student will:
- learn Python language in procedural, functional and object-oriented programming
- know the main R object classes, functions and packages;
- be able to import data from external sources (e.g. text files), to manipulate data for exploratory analysis and visualization.
- be able to produce fully reproducible reports by using RMarkdown (https://rmarkdown.rstudio.com/)

Course content

- Review of Computer Science and data representation
- Introduction to Programming and Algorithmics
- Basics of Python language and data structures
- Introduction to R programming language and syntax
- Data import and basic data manipulation and analysis
- Data transformation with dplyr
- Data visualization with ggplot2
- How to create new functions in R

Teaching methods

The course consists in lab sessions. The learning-by-doing approach is adopted: the students reproduce step by step the syntax written by the teachers and are then asked to solve independently simple problems.

Assessment and Evaluation

The exam consists in short programs in Python and exercises to be solved using the R software. Usually real world data are provided, and the students have to manipulate and analyse the data in order to produce summary statistics and graphical representations.

The final score for the "Coding for Data Science" course will be based on a global evaluation of the knowledge and skills gained by the student in the two parts of the course.

Further information

If the course is delivered remotely (totally or partially), changes may occur in the program and/or in the exam, in order to adapt the course to on-line teaching methods.