12783 - Mathematics, Statistics and Computer Science

Learning outcomes

At the end of the course, the student will have the basic knowledge of mathematics, necessary to deal with the other disciplines of the degree programme. In particular, the student will be able to: - understand and use the graph of a function in mathematical models; - understand the use of the tools of differential and integral calculus and linear algebra in applications; - use simple mathematical software to solve equations, draw graphs and study them, perform calculations with derivatives, integrals and matrices. He or she will also have some knowledge of basic statistical methods. In particular, the student acquires familiarity with the scientific method and is able to adopt the most suitable basic statistical analysis methods both in field and laboratory experiments. He or she will possess basic skills of some aspects of computer science. In particular, students will understand Internet basics and will be familiar with software for the management of data bases.

Course contents

Module 1: Mathematics (Rüdiger Achilles)
Enumerative combinatorics: Permutations and combinations without and with repetitions, Newton's binomial formula.
Numbers: A glimpse on real numbers, powers with real exponents.
Linear systems and matrices: Matrix algebra, linear transformations, linear systems, Gauss-Jordan elimination.
Real functions: Functions, function composition, inverse functions, real functions, in particular linear, polynomial, exponential, logarithmic, trigonometric functions and their graphs, limits and asymptotes, continuity.
Differential calculus: Derivatives and differentials, their meaning, differentiation rules, Taylor's formula.
Integral calculus: Indefinite integral, definite (Riemann) integral and its properties, mean-value theorem for definite integrals, fundamental theorem of calculus, integration techniques (integration by parts and integration by substitution), applications of the definite integral, differential equations with separable variables.

Module 2: Applied Statistics (Federico Plazzi)
6th October: presentation of the course; introduction to descriptive statistics; distribution of the initial questionnaire; measures of central tendency (arithmetic mean, weighted average, median, mode).
13th October: measures of dispersion (deviation, variance,  standard deviation); concept of statistical distribution of data; normal distribution.
20th October: Concept of statistical hypothesis testing; test of normality (quantiles and plot quantile-quantile, Shapiro-Wilk test).
27th October: LAB: introduction to R; how to write an input file; how to retrieve, view, manage data.
3rd November: Introduction to inferential statistics; Student's t-test: one-sample t-test, paired samples, two-sample t-test.
17th November: LAB: test of normality and Student's t-test with R.
19th November: Non normally distributed quantitative variables: Wilcoxon–Mann–Whitney tests. Quantitative variables: chi-square test.
24th November: Linear regression and correlation: Pearson's correlation coefficient, significance of a correlation.
1st December: Variance analysis, one-way and two-way ANOVA, Tukey's test.
15th December: LAB: chi-square test, regression and variance analysis with R.

Module 3: Computer Science (self-learning: https://elearning-cds.unibo.it/)
The structure of the Internet and Internet services; construction of a data-base.

Readings/Bibliography

Marco Abate: Matematica e Statistica 2/ed, Le basi per le scienze della vita.
McGraw-Hill Education (Italy) srl, 2013, ISBN: 9788838668227.

Claudia Neuhauser: Calculus For Biology and Medicine, 3/E.
Pearson, 2011,  ISBN: 9780321644688.

Teaching methods

Lessons and exercises in the classroom. Self-learning via Internet.

Assessment methods

There are independent assessments for each of the three course modules: exams in Mathematics and Statistics and a Computer Science test. The final mark, on a 30-point scale, will be the weighted score with respect to the credit points of Mathematics (6 ECTS) and Statistics (2 ECTS), increased by a possible bonus of one, two or three points if the score of the Computer Science test is between 24 and 26, 27 and 29 or 30 respectively.
The written test in Mathematics (the use of books, notes, calculators, electronic equipment is not allowed ) is designed to ascertain skills to solve exercises with respect to the topics covered in the course. It serves as a qualifying examination for admission to the oral examination (score at least 15/30), which is compulsory for all.  In Statistics, there is only a written exam.

Teaching tools

Alma Mathematica (http://www.dm.unibo.it/almamathematica/): an online math-bridge course which with its diagnostic tests offers students the possibility to complete the missing pieces and refresh the material necessary for a successful study of mathematics. This self-study course is complemented by a virtual tutorial where students can get instant help by skype, e-mail and telephone.
Exercises for homework and course material are available at http://www.dm.unibo.it/~achilles/.
Software R - a free software environment for statistical computing and graphics, available at http://www.r-project.org/.

Links to further information

http://www.dm.unibo.it/~achilles/scienze/index.html

Office hours

See the website of Hans Joachim Rudiger Achilles

See the website of Federico Plazzi