PHYSICS, COMPUTER SCIENCE AND DATA ANALYSIS
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- Versione italiana
- Academic year
- 2017/2018
- Teacher
- MICHELE MARZIANI
- Credits
- 12
- Didactic period
- Primo Semestre
Training objectives
- The aim of the course is the training of the students to the use of scientific methodology in describing and interpreting the natural phenomena of concern for the medicine applied to motion sciences. The topics of the course are chosen with reference to their usefulness in the subsequent learning activity.
Prerequisites
- Elements of algebra and trigonometry. Basic functions of interest in the medical sciences.
Basic knowledge of calculus and functional analysis. Course programme
- PHYSICS
Physical quantities: the International System; scalars and vectors. Motion of a point: uniform and uniformly accelerated rectilinear motion; uniform circular motion; undamped harmonic motion; motion in a plane. Dynamics: laws of motion; work, potential and kinetic energy. Systems of mass points: centre of mass; rigid body equilibrium; energy, linear and angular momentum conservation; harmonic oscillator; simple pendulum; collision between mass points. Kinetic theory of matter: kinetic theory of gases, liquids and solids; heat and temperature; heat transfer. Fluid dynamics: equation of continuity; motion of a perfect and a viscous fluid in a rigid pipe; Archimede's law. Some properties of matter: elasticity; effects of cohesion forces; surface tension; function of elastic pipes under pulsate regime. The principles of thermodynamics: first principle of thermodynamics; Carnot's cycle; entropy; second principle of thermodynamics. Electric current and circuit: electric current and resistence; Ohm's law; real voltage generetor; analogies between electric and fluidic circuits.
DATA ANALYSIS
The course consists of two parts, a theoretical part explaining the common techniques of statistical analysis for univariate and bivariate data, and a practical part allowing the student to become familiar with some simple data analysis software code. During the theoretical part we will introduce graphing techniques of qualitative and quantitative data, and numerical techniques for computing the common location, dispersion and shape statistics. After introducing the element of probabilistic computing, we will describe the probability distributions of discrete and continuous random variables (Binomial Distribution, Normal distribution and Standard distribution) commonly used for the analysis of qualitative and quantitative data. Concepts of point estimate and interval estimation of population mean and proportion, and population standard deviation will be defined as well as the most common sampling distributions (t-Student, Chi-Squared). Hypothesis testing on the average, the proportion and the variance of the population will be the final topics of the theoretical part. The second part includes a series of practical computer lessons (12h/student) to learn how to perform the data analysis of some data-set concerning clinical data exploration.
COMPUTER SCIENCE
Data and information. Basic components and functions of a processing system. Data processing: the algorithm. Types of computers
Signal and data encoding. Codes for alphanumeric representation. Basic numeric conversion
Hardware Components. Software Components. Function, Structure and Types of an Operating System
Application software. User Interfaces. Elements of graphic interfaces. Graphics to PC. Digital images
Definition and file types. Store data and mass memories. File System. Data Protection. Spreadsheet Basic Items. Editing commands. Graphic, numerical and conditional cell formatting. Formulas and functions: the main logical, statistical, and mathematical functions. Auto-composition procedure of a chart. Sorting and filtering data. Pivot Tables.
Computer exercises on spreadsheet use. Analysis of experimental data through the most common descriptive statistics functions. Didactic methods
- Lectures and, for Computer Sciences, exercizes.
Learning assessment procedures
- PHYSICS
Written exam consisting of 15 multiple choice questions with 5 possible answers of which only one is correct. Each correct answer is worth 2 points, each answer is not given 0 points, -0.5 points for each wrong answer. Duration 45 minutes.
COMPUTER SCIENCES
Written examination consisting of 30 multiple-choice questions (with 1 correct answer) subdivided into arguments. Evaluation: 1.5 points for correct answer, -0.5 points for wrong answer or not given. Time available 30 min.
The exam is only passed if the student, regardless of the score obtained, has answered the correct one half plus one of the questions asked for each topic.
DATA ANALYSIS
Written exam to be performed via the "Question-Mark, Perception Software©". The student will answer to a series multiple choice and open questions, and solve some numeric exercises designed to assess the knowledge, competence and skills acquired during the lectures and computer exercises. Questions and exercises are extracted randomly from a suitable database and the assessment varied from 1 to 4 points depending on the difficulty degrees. The difficulty degrees are stated in the evaluation grid included in the task. The time allowed for the exam is one hour and the maximum possible score is 31 (30 cum laude).
The final grade is a weighted average of the marks obtained in each of the tests. Reference texts
- PHYSICS
E. Casnati, Elementi di Fisica Generale per Scienze Mediche. CEA Milano
COMPUTER SCIENCES
Testo di preparazione Patente Europea del Computer (ECDL) ultima versione del syllabus
DATA ANALYSIS
P.S. Mann, Introductory Statistics, VII Eds, Wiley N.Y.(2014)
J.R. Norman - D. L. Steiner, Biostatistica, II Eds. Ambrosiana Milano (2015)
