Laurea Magistrale (M.Sc.) in Stochastics and Data Science

Programming for data science

Programming for data science

Academic year 2016/2017

Sommario del corso

• Course objectives
• Results of learning outcomes
• Course delivery
• Learning assessment methods
• Program
• Suggested textbooks and readings
• Teaching tools

Course objectives

Aim of the course is to introduce methods, techniques and related computer science instruments for the analysis of experimental data.

It  provides the basic knowledge to use computer science applications as  Spreadsheet  (e.g. Excel, Calc,...) and  programming languages  for statistical computing and graphics (e.g. R programming language)

Results of learning outcomes

KNOWLEDGE AND UNDERSTANDING – Completing the course students will be able to:

1)      use suitable descriptive and inferential statistics techniques to describe and understand the phenomena being studied;

2)      manage suitable computer science instruments such as worksheet or dedicated software programs for statistical data analysis.

APPLYING KNOWLEDGE AND UNDERSTANDING – Students will perform the statistical analyses required by the problem under study by selecting the most computationally and graphically suitable computer science support.

MAKING JUDGEMENTS – Students will decide which statistical techniques to use according to the available data sets to describe and understand the phenomena under consideration.

COMMUNICATION – The student will be able to justify the choices for the analysis to be performed and to give a synthetic description of the techniques employed and of the results obtained.

Course delivery

ELEMENTS OF COMPUTER SCIENCE

The course consists of  10 hours of lectures, and 15 hours of laboratories . Laboratories  include exclusively practical activities.

The slides presented during lectures are available to students as online materials.

Attendance to lessons is not mandatory, but highly recommended due to the necessity of learning and employing specific computer science instruments.

Learning assessment methods

The exam consists of a written test and requires a practice exercise on R  programming languages

WRITTEN EXAMINATION:

-  ten multiple choice questions on course topics (4 options, with the possibility of 0-4 correct options);

- a practice exercise on  R programming languages

The maximum possible score is 30 cum laude.

Program

ELEMENTS OF COMPUTER SCIENCE

• Introduction to R programming language;

• Basic R functionalities:

  • Data structures: vector, matrix, array, list, data.frame \ldots;

  • Apply operators;

  • Input/output operator;

  • Package and library.

• Programming with R:

  • Function;

  • Flow control: if,for, while, break ... statements;

  • Debugging in R.

• Probability distributions:

  • Densities;			

  • Cumulatives,

  • Quantiles;		

  • Randon numbers.

• Statistical graphics:

  • Graphical devices;

  • High level plot;

  • Low level plots.

• Statistical functions

- P. Dalgaard, Introductory Statistics with R, Springer 2008

- The R Manuals: An Introduction to R (http://cran.r-project.org/doc/manuals/r-releas /Rintro.pdf)

The teaching material used for lessons and a series of practical exercises are available on the web site of the course.

Class schedule

• Teaching materials
• Exams and finals
• Go to Moodle
• Visit the forums