Scheda programma d'esame
PROGRAMMING FOR DATA SCIENCE
SALVATORE RUGGIERI
Anno accademico2017/18
CdSDATA SCIENCE AND BUSINESS INFORMATICS
Codice667AA
CFU12
PeriodoPrimo semestre
LinguaItaliano

ModuliSettoreTipoOreDocente/i
PROGRAMMING FOR DATA SCIENCEINF/01LEZIONI96
GIUSEPPE PRENCIPE unimap
SALVATORE RUGGIERI unimap
Programma non disponibile nella lingua selezionata
Learning outcomes
Knowledge

This is an introductory course to computer programming and related mathematical/logic background for students without a Bachelor in Computer Science or in Computer Engineering. The objective is to smoothly introduce the student to the programming concepts and tools needed for typical data processing and data analysis tasks. The course consists of lectures and practice in computer labs.

Assessment criteria of knowledge

The student will be assessed on his/her demonstrated ability to use computer programming for problem solving. There is a lab exam (4 hours) and an oral exam.

Methods:

  • Final oral exam
  • Final laboratory practical demonstration
Skills

The student will be able to use computer programming languages and related mathematical notions for problem reasoning and solving.

Assessment criteria of skills

Skills will be assessed through a lab exam. 

Behaviors

The student will be able to separate apart the problem constraint and solutions from the actual coding in a specific computer programming language. Computational thinking is the expected ability at the end of the course.

Assessment criteria of behaviors

Autonomy and capacity of computational thinking will be assessed during the lab practices and at the oral exam.

Prerequisites

Basic mathematical notions as given in most of Bachelor programs.

Co-requisites

None.

Prerequisites for further study

This course specifically gives the necessary prerequisites for "Algoritmica e Laboratorio" (008AA) and, in general, for all INF/01 courses of the Master degree in Data Science and Business Informatics.

Teaching methods

Delivery: face to face

Learning activities:

  • attending lectures
  • participation in discussions
  • individual study
  • group work
  • laboratory work

Attendance: strongly advised

Teaching methods:

  • Lectures
  • Task-based learning/problem-based learning/inquiry-based learning
  • laboratory
Syllabus

Syllabus
– Sets, relations, functions, combinatorics, grammars, automata.
– Propositional and first order logic.
– Induction and recurrence relations.
– Imperative programming.
– Object oriented programming.
– Programming stack and development tools.
– Python programming.
– C programming.

 

Bibliography

Book chapters with reminds on theoretical background and software manuals will be provided at the course web site. Software tools will be downloadable with an academic/free licence.

Non-attending students info

No specific rules for non-attending students.

Assessment methods

Lab practice and oral exam. Mid-terms replace lab practice.

Ultimo aggiornamento 17/09/2017 12:03