
Course unit
APPLIED INFORMATICS
AVP7077919, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2019/20
Integrated course for this unit
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Basic courses 
INF/01 
Computer Science 
7.0 
Course unit organization
Period 
First semester 
Year 
1st Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Practice 
2.0 
16 
34.0 
No turn 
Laboratory 
2.0 
16 
34.0 
No turn 
Lecture 
3.0 
24 
51.0 
No turn 
Examination board
Examination board not defined
Common characteristics of the Integrated Course unit
Prerequisites:

Knowledges of elementary algebra (common denominator, algebraic expressions, simplification), simple and quadratic equations, and simple inequalities are prerequisites of the Mathematics and Applied Informatics course. 
Target skills and knowledge:

On successful completion of the course a student will be able to:
MATHEMATICS
1) understand the basic concepts of mathematical analysis in a real variable;
2) carry out a study of a function in one variable;
3) understand the basic elements of integral calculus;
4) identify maxima and minima of a function in two variables.
APPLIED INFORMATICS
1) use the exploratory data analysis to summarize real/experimental information;
2) interpret the results of statistical analysis, also in terms of biological meaning;
3) use the main features of a spreadsheet (Excel) to manage and analyse the data, and of PowerPoint to present the results using, for example, tables and figures. 
Examination methods:

The successful completion of the Mathematics and Applied Informatics course is achieved after:
 completion of a written exam for the Mathematics module;
 completion of a written exam for the Applied Informatics module.
Both the Mathematics and Applied Informatics examinations are based on questions and exercises. In order to provide the student with the opportunity to measure his/her preparation level, an optional midsemester test will be scheduled. The date of the test is planned in agreement with lecturers holding courses in the first semester of the firs year. A positive outcome of the test does not exempt the student from the final exam. Examples of midsemester tests and final exams used in previous academic years will be provided.
Lecturers of the course will assess the two modules and the final score will be the arithmetic mean of the outcomes of the two modules. 
Assessment criteria:

Assessment criteria to check the preparation of the student are:
1) knowledge of topics presented during classes;
2) ability of using the appropriate terminology;
3) ability of using the appropriate tools and methodologies to solve specific problems. 
Specific characteristics of the Module
Course unit contents:

Basic statistics:
 introduction to statistics (definition, applications, main glossary);
 exploratory data analysis (definition of random variable, types of variables, tables, graphs, basic symbols used in statistics, measures of central tendency and variability of experimental data), exercises;
 probability distributions (definition, types of distributions); Normal distribution (features, measures of the shape of Normal distribution, standardization), binomial distribution, exercises;
 study of the relationship between variables (sum of squares, covariance and coefficient of correlation; simple linear regression model: estimation of the regression parameters, coefficient of determination), exercises.
Computer Lab:
 use of the spreadsheet (Excel) for statistical analysis of experimental data;
 use of PowerPoint to present results. 
Planned learning activities and teaching methods:

The Applied Informatics module is organised in lectures (24 h) using PowerPoint, and exercises during classes (16 h) and in the Computer Lab (16 h).
Each topic presented and discussed during classes will be combined with practical exercises to ascertain the student's understanding of the topic itself. The text of the problem to solve will be displayed on a screen and students will be grant a reasonable amount of time to complete the activity, individually or in group. The correction will be driven by the lecturer using an intercative approach to involve students in the discussion and thus to lead to the critical interpretation of the results.
The topics presented and discussed during classes are also preparatory to Computer Lab sessions. The student will gain knowledge on the main features of the spreadsheet (Excel) to manage and analyse experimental data, and of PowerPoint to present main results from statistical analysis (tables, graphs). 
Additional notes about suggested reading:

The study material (slides, exercises, solutions) will be available on the elearning platform (Moodle) of the School of Agricultural Sciences and Veterinary Medicine of the University of Padova (https://elearning.unipd.it/scuolaamv/). To download the material, the student needs to login (username and password provided by the University of Padova upon enrollment), and insert a specific keyword provided by the lecturer during class.
For students regularly attending the module, the study material for the preparation of the final exam of the Applied Informatics module is composed of the material presented during classes (slides, exercises) completed with additional explanations/notes provided by the lecturer.
For those students who cannot attend the course (e.g. student workers), the preparation of the final exam will be based on specific parts/chapters (details will be provided by the lecturer in Moodle) of the following textbooks:
 Picci, Lucio. 1998. Introduzione alla statistica. CLUEB, Bologna.
 Sari Gorla, Mirella. 2011. Elementi di statistica applicata. Maggioli Editore, Seconda edizione.
The aforementioned textbooks are optional for students regularly attending the module and can be used for indepth analysis. 
Textbooks (and optional supplementary readings) 

Innovative teaching methods: Teaching and learning strategies
 Lecturing
 Laboratory
 Loading of files and pages (web pages, Moodle, ...)
Innovative teaching methods: Software or applications used
 Moodle (files, quizzes, workshops, ...)

