INFORMATICA CON ELEMENTI DI MATEMATICA E STATISTICA A - L

1. Knowledge

At the end of the course, students will be able to describe and understand the fundamental mathematical

concepts covered in the course, with particular reference to elementary functions, functions of a real

variable, limits, derivatives, and integrals; illustrate the basic principles of descriptive statistics and

biostatistics, including the main measures of frequency and risk and the fundamental characteristics of the

normal distribution; and describe the fundamental principles of computer science, hardware and software

organization of computer systems, information representation, and computer networks.

Students will also understand the role of mathematical, statistical, and computational tools in the analysis

and representation of problems related to life, biomedical, and pharmaceutical sciences.

2. Applying knowledge and understanding

Students will be able to use the main mathematical functions and basic concepts of differential and integral

calculus to address simple quantitative problems; apply the main measures and representations of

descriptive statistics to simple datasets; correctly interpret basic statistical and risk indicators; recognize the

main hardware and software components of a computer system and understand their functions; and apply

the acquired knowledge to the interpretation of simple problems and scenarios of biomedical and

pharmaceutical interest.

3. Making judgements

Through exercises, applied problems, and discussion of examples, students will be able to analyse simple

quantitative data and problems, identify appropriate mathematical or statistical tools for their description,

critically interpret the results obtained, and recognize potential limitations in the use of the computational

and quantitative tools considered.

4. Communication skills

Students will be able to clearly explain the main mathematical, statistical, and computer science concepts

covered during the course, appropriately using the specific terminology, notation, and language of the

disciplines. They will also be able to describe and justify the procedure adopted in solving simple problems

and exercises.

5. Learning skills

Students will be able to use the quantitative and computational methods acquired during the course to

independently approach subsequent courses requiring basic knowledge of mathematics, statistics, and

computer science and to further develop their understanding of these tools in biomedical and

pharmaceutical sciences through appropriate teaching materials and sources.

The course includes lectures devoted to the fundamental concepts of mathematics, statistics, and computer

science, complemented by guided exercises and the discussion of examples and applications in biomedical

and pharmaceutical sciences.

For the mathematics and statistics components, practical activities will focus on applying theoretical

concepts to simple problem solving, data interpretation, and understanding the main quantitative

applications in life sciences. For the computer science component, examples and guided activities will

support the understanding of the main hardware and software components and the fundamental concepts

of computer networks.

Lectures primarily contribute to the acquisition of knowledge and understanding of the disciplinary contents,

whereas exercises and applied activities are aimed at developing the ability to apply acquired knowledge,

analyse and solve simple problems, and appropriately use mathematical, statistical, and computer science

terminology.

Pursuant to the RDA, Article 12 – University Educational Credits (CFU), within the standard workload of 25

total hours of student commitment corresponding to one credit:

(a) seven hours may be devoted to lectures or equivalent instructional activities, with the remaining hours

reserved for individual study;

(b) a minimum of twelve and a maximum of fifteen hours may be devoted to classroom exercises or

equivalent supervised activities, with the remaining hours reserved for individual study and elaboration.

If the course is delivered in blended or remote mode, appropriate adjustments may be made to the above in

order to ensure consistency with the syllabus.

No specific prior disciplinary knowledge is required. Basic mathematical knowledge, including elementary

algebraic operations and the representation of simple functions, normally acquired during upper secondary

education, is useful. The specific mathematical, statistical, and computer science knowledge required for

the course will be introduced during the teaching activities.

Attendance is mandatory in accordance with the Academic Regulations of the Degree Programme.

Absences are permitted for no more than 30% of the total teaching hours, considering all forms of teaching

activity.

Academic Regulations of the Degree Programme in Pharmacy: https://www.dsf.unict.it/it/corsi/lm-

13/regolamento-didattico

1. Elements of Mathematics

First-degree equations and inequalities; second-degree equations.

Direct and inverse proportionality.

Proportions and percentages.

Use of a scientific calculator.

Elementary functions: power functions and nth roots, exponential and logarithmic functions; definitions,

properties, and graphical representation.

Applications of exponential and logarithmic functions in life sciences.

Functions of a real variable: domain, increasing and decreasing behaviour, absolute maxima and minima,

composition of functions, and graphical representation.

Limits: definitions, properties, main calculation rules, orders of infinity and infinitesimals, graphical

interpretation, and asymptotes.

Derivatives: fundamental concepts and simple applications.

Integrals: definition, properties, area calculation, and approximation using the trapezoidal rule.

Guided use of artificial intelligence tools to support learning and the solution of simple mathematical

problems.

2. Elements of Statistics and Biostatistics

Principles of descriptive statistics.

Organization and representation of data.

Frequency measures.

Risk measures.

Normal distribution: fundamental characteristics and interpretation.

Examples of statistical applications in biomedical and pharmaceutical sciences.

3. Elements of Computer Science

Fundamental concepts of information theory.

Hardware and software.

Types of computers and main components of a computer system.

Central processing unit and memory.

Input, output, and input/output devices.

Storage devices.

System and application software.

Graphical user interfaces.

Introduction to computer networks.

4. Applications to Biomedical and Pharmaceutical Sciences

Examples of applications of mathematical, statistical, and computational tools to life sciences, biomedical

sciences, and Drug Discovery.

Reference materials

1. Lecture notes, slides, and teaching materials provided by the teacher and made available through the

Studium platform.

2. Bramanti, Confortola, Salsa. Matematica per le scienze.

3. Benedetto. Matematica per le scienze della vita.

Additional reading materials may be indicated by the teacher during the course.

Learning assessment is carried out through a written examination, aimed at assessing the achievement of

the intended learning outcomes related to the mathematics, statistics, and computer science components of

the course.

The examination includes questions and exercises concerning the topics covered during the course and is

aimed at assessing knowledge and understanding of the fundamental concepts, the ability to apply acquired

knowledge to simple problems, the ability to interpret data and results, and the appropriate use of

mathematical, statistical, and computer science terminology.

The written examination may include multiple-choice questions, short-answer questions, and applied

exercises, in order to assess both theoretical knowledge and the ability to apply it to simple contexts,

including biomedical and pharmaceutical applications.

At the student’s request, an optional oral examination may be taken in order to improve the grade obtained

in the written examination. The oral examination will cover the course topics and will allow further

assessment of the student’s knowledge and understanding, ability to connect different topics, and

appropriate use of subject-specific terminology.

Assessment will consider the accuracy and completeness of knowledge, the ability to apply the acquired

concepts and methods, the correctness of the procedures used to solve exercises, the ability to interpret

results, and the appropriate use of subject-specific terminology.

Grading criteria

Fail: insufficient or fragmentary knowledge of the fundamental contents; significant errors in applying

concepts and methods; inability to correctly solve simple problems.

18–21: essential knowledge of the main contents; sufficient, although limited, ability to apply concepts and

methods to simple problems; some non-substantial inaccuracies.

22–25: satisfactory knowledge of the contents; adequate ability to apply concepts and solve the proposed

problems; overall correct procedures and interpretation of results.

26–28: good knowledge and understanding of the contents; good ability to correctly apply mathematical,

statistical, and computer science methods; correct solution and interpretation of the proposed problems.

29–30 with honours: comprehensive and in-depth knowledge of the contents; excellent command of

concepts and methods; ability to correctly apply them to more articulated problems and critically interpret

the results with rigour and independence.

Learning assessment may also be carried out on-line, should the conditions require it.

To ensure equal opportunities and in compliance with current laws, interested students may request a

personal interview in order to plan any compensatory and/or dispensatory measures based on educational

objectives and specific needs. Students may also contact Prof. Santina Chiechio

(santina.chiechio@unict.it), the CInAP referring teacher for the Department of Drug and Health Sciences.

1 Sia f:A → B, essa è detta Biiettiva se: A) la funzione è Iniettiva. B) f(A)=B. C) la funzione è Iniettiva e Suriettiva. D) ∀x',x”∈A,x'≠x”⇒f(x')≠f(x”)

2 Qual è la caratteristica principale della distribuzione di Gauss (o distribuzione normale)? A) È una distribuzione che presenta simmetria bilaterale rispetto al suo valore medio. B) È una distribuzione che assume solo valori discreti. C) È una distribuzione che non ha media né deviazione standard definite. D) È una distribuzione che descrive i fenomeni naturali con una curva a forma di rettangolo.

3 Contrassegnare la risposta Vera. Il seek time misura: A) Il tempo che impiega la testina a spostarsi in senso radiale fino a raggiungere la traccia desiderata. B) Il tempo trascorso affinché Il settore desiderato passa sotto la testina. C) Il tempo di lettura vero e proprio. D) la velocità di avvio del sistema operativo.