Fundamental of Mathematics and Statistics
A.Y. 2025/2026
Learning objectives
The aim of the course is to provide a basic knowledge of the mathematics needed in the natural sciences, and the tools of descriptive and inferential Statistics, together with concepts of probability on which they are based
Expected learning outcomes
At the end of the course students will be able to describe, interpret and explain simple mathematical models describing natural phenomena, also through statistical methods
Lesson period: Activity scheduled over several sessions (see Course syllabus and organization section for more detailed information).
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
A-L
Responsible
Lesson period
year
Course syllabus
Mathematics
2 - Vectors
2.1 From numbers to vectors
2.2 Vector operations
2.3 The direction of vectors
2.4 Scalar product
2.5 Linear Systems
3 - Matrices and transformations
3.1 Matrices and transformations
3.2 Matrix operations
4 — Giving a mathematical shape to natural phenomena
4.1 Phenomena, models, functions
4.2 Function graphics
4.3 Increasing and decreasing functions; maxima and minima
5 — Complex phenomena and elementary functions
5.1 Linear functions
5.2 Quadratic functions
5.3 Power functions and the dimensions of life
6 — Population dynamics
6.1 Exponential functions
6.2 Logarithms
6.3 Rate of divergence and convergence
6.4 Sequences, series, and recurrence relations
7 — Forecasting future
7.1 Asymptotic behaviour
7.2 Limit computation
7.3 Convergence and divergence speed
7.6 Evolution models with continuous time
8 — The laws of change
8.1 Mean and instantaneous variation rate
8.2 The rules of derivatives
8.3 Derivative: book of instruction
8.5 Continuous time evolutionary models
9 — Integrals
9.1 From derivative to functions
9.2 Integration
9.3 Differentiation
Statistics
Descriptive Statistics.
1) Population, sample, parameter, statistics. Types of data and variables. Sampling.
2) Graphs and tables. Frequency tables. Histograms/bar graphs.
3) Mean, modal value, median, midrange and their relations. Range, standard deviation, variance and their relations. Percentiles, quartiles and outliers. Boxplot. Weighted mean.
Probability and random variables.
4) Introduction.Events and space of events; probability of an event.
5) Probability of the union and the intersection. Complemento of an event. Independence. Conditional probability. Bayes Theorem.
6) Random Variables. Expected value, variance and deviation standard of discrete r.v.s.
7) Discrete r.v.s: Binomial and Poisson. Continuous r.v.s: Uniform and Normal.
8) Sample distributions. Centrale Limit Theorem. Normal approximation of the binomial distribution.
Confidence intervals and Hypothesis tests.
9) Confidence interval for a proportion.
10) Confidence interval for the mean, and known/unknown variance. T-Student Distribution.
11) Confidence interval for the variance of a population normally distributed. Chi-square distribution.
12) Hypothesis tests:general concepts. Null and alternative hypothesis, test statistic, critical region, level of significance, critical values, one/two tails test, P-value, errors of the first/second kind, power of a test.
13) Hypothesis test for a proportion. Hypothesis test for one sample: test on the mean (known/unknown variance), test on the variance or on the standard deviation.
14) Inference for two independent samples: inference on two proportions. Inference on two means, either for independent samples or for coupled samples.
Linear dependence.
15) Linear correlation and hypothesi test on the correlation coefficient.
16) Linear regression.
2 - Vectors
2.1 From numbers to vectors
2.2 Vector operations
2.3 The direction of vectors
2.4 Scalar product
2.5 Linear Systems
3 - Matrices and transformations
3.1 Matrices and transformations
3.2 Matrix operations
4 — Giving a mathematical shape to natural phenomena
4.1 Phenomena, models, functions
4.2 Function graphics
4.3 Increasing and decreasing functions; maxima and minima
5 — Complex phenomena and elementary functions
5.1 Linear functions
5.2 Quadratic functions
5.3 Power functions and the dimensions of life
6 — Population dynamics
6.1 Exponential functions
6.2 Logarithms
6.3 Rate of divergence and convergence
6.4 Sequences, series, and recurrence relations
7 — Forecasting future
7.1 Asymptotic behaviour
7.2 Limit computation
7.3 Convergence and divergence speed
7.6 Evolution models with continuous time
8 — The laws of change
8.1 Mean and instantaneous variation rate
8.2 The rules of derivatives
8.3 Derivative: book of instruction
8.5 Continuous time evolutionary models
9 — Integrals
9.1 From derivative to functions
9.2 Integration
9.3 Differentiation
Statistics
Descriptive Statistics.
1) Population, sample, parameter, statistics. Types of data and variables. Sampling.
2) Graphs and tables. Frequency tables. Histograms/bar graphs.
3) Mean, modal value, median, midrange and their relations. Range, standard deviation, variance and their relations. Percentiles, quartiles and outliers. Boxplot. Weighted mean.
Probability and random variables.
4) Introduction.Events and space of events; probability of an event.
5) Probability of the union and the intersection. Complemento of an event. Independence. Conditional probability. Bayes Theorem.
6) Random Variables. Expected value, variance and deviation standard of discrete r.v.s.
7) Discrete r.v.s: Binomial and Poisson. Continuous r.v.s: Uniform and Normal.
8) Sample distributions. Centrale Limit Theorem. Normal approximation of the binomial distribution.
Confidence intervals and Hypothesis tests.
9) Confidence interval for a proportion.
10) Confidence interval for the mean, and known/unknown variance. T-Student Distribution.
11) Confidence interval for the variance of a population normally distributed. Chi-square distribution.
12) Hypothesis tests:general concepts. Null and alternative hypothesis, test statistic, critical region, level of significance, critical values, one/two tails test, P-value, errors of the first/second kind, power of a test.
13) Hypothesis test for a proportion. Hypothesis test for one sample: test on the mean (known/unknown variance), test on the variance or on the standard deviation.
14) Inference for two independent samples: inference on two proportions. Inference on two means, either for independent samples or for coupled samples.
Linear dependence.
15) Linear correlation and hypothesi test on the correlation coefficient.
16) Linear regression.
Prerequisites for admission
Pre-calcalcus high school level mathematics
Teaching methods
Lectures, flipped classroom (with pre recorded videos), problem solutions, recitations; assessment tests; programming lab using R
Teaching Resources
MATHEMATICS
D. Benedetto e altri. Dalle funzioni ai modelli, il calcolo per le bioscienze.
Or, as an alternative:
D. Benedetto e altri. Matematica per le scienze della vita. Ambrosiana. Qualsiasi edizione
STATISTICS
R.E. Walpole - R.H. Myers - S.L. Myers - K.E. Ye. Probability and Statistics for Engineers and Scientists, 9th Edition. Pearson
Or, for exercises:
Anna Clara Monti
Statistica . Esercizi Svolti
Pearson, 2024
D. Benedetto e altri. Dalle funzioni ai modelli, il calcolo per le bioscienze.
Or, as an alternative:
D. Benedetto e altri. Matematica per le scienze della vita. Ambrosiana. Qualsiasi edizione
STATISTICS
R.E. Walpole - R.H. Myers - S.L. Myers - K.E. Ye. Probability and Statistics for Engineers and Scientists, 9th Edition. Pearson
Or, for exercises:
Anna Clara Monti
Statistica . Esercizi Svolti
Pearson, 2024
Assessment methods and Criteria
Written exam for each module, on a range out of 30.
Mathematics: semiclosed exercises and reality problems on the topics developed during the course. The evaluation is aimed at verifying the understanding of the topics and how they can be applied to real problems.
Statistics: exercises (probability, statistics, R) and theoretical questions on the topics developed during the course. The evaluation ranges out of thirty and is aimed at verifying the understanding of the theoretical notions and their application to real data analysis.
The duration of the written test is commensurate with the number and structure of the assigned exercises, but in any case will not exceed 3 hours.
Results will be communicated on the SIFA through the UNIMIA portal.
The final mark, out of 30, is given by the average weighted according to the CFU of each module.
Mathematics: semiclosed exercises and reality problems on the topics developed during the course. The evaluation is aimed at verifying the understanding of the topics and how they can be applied to real problems.
Statistics: exercises (probability, statistics, R) and theoretical questions on the topics developed during the course. The evaluation ranges out of thirty and is aimed at verifying the understanding of the theoretical notions and their application to real data analysis.
The duration of the written test is commensurate with the number and structure of the assigned exercises, but in any case will not exceed 3 hours.
Results will be communicated on the SIFA through the UNIMIA portal.
The final mark, out of 30, is given by the average weighted according to the CFU of each module.
MAT/04 - MATHEMATICS EDUCATION AND HISTORY OF MATHEMATICS - University credits: 8
MAT/06 - PROBABILITY AND STATISTICS - University credits: 4
MAT/06 - PROBABILITY AND STATISTICS - University credits: 4
Practicals: 48 hours
Practicals with elements of theory: 48 hours
Lessons: 40 hours
Practicals with elements of theory: 48 hours
Lessons: 40 hours
M-Z
Responsible
Lesson period
year
Course syllabus
Chapter 1 - Number Sets and the Concept of Domain
Chapter 2 - Vectors
2.1 From Numbers to Vectors
2.2 Operations with Vectors
2.3 Vector Direction
2.4 Dot Product
2.5 Linear Systems
Chapter 3 - Matrices and Transformations
3.1 Matrices and Transformations
3.2 Operations with Matrices
Chapter 4 - The Mathematical Form of Natural Phenomena
4.1 Phenomena, Models, Functions
4.2 The Graph of a Function
4.3 Increasing and Decreasing Functions, Maxima and Minima
Chapter 5 - Complex Phenomena and Elementary Functions
5.1 The World of Linear Laws
5.2 Quadratic Laws
5.3 Power Functions and the Dimensions of Life
Chapter 6 - Population Dynamics and Biological Rhythms
6.1 Exponential Functions
6.2 Logarithms
Chapter 7 - Predicting the Distant Future
7.1 The Problem of Asymptotic Behavior
7.2 Calculating Limits
7.3 Rates of Divergence and Convergence
Chapter 8 - The Laws of Change
8.1 Average and Instantaneous Rate of Change
8.2 Rules for Differentiation
8.3 Derivatives: User Guide
8.5 Evolution Models with Continuous Time
Chapter 9 - Integrals
9.1 From Derivative to Function
9.2 Integrating
9.3 Differentiating
Chapter 2 - Vectors
2.1 From Numbers to Vectors
2.2 Operations with Vectors
2.3 Vector Direction
2.4 Dot Product
2.5 Linear Systems
Chapter 3 - Matrices and Transformations
3.1 Matrices and Transformations
3.2 Operations with Matrices
Chapter 4 - The Mathematical Form of Natural Phenomena
4.1 Phenomena, Models, Functions
4.2 The Graph of a Function
4.3 Increasing and Decreasing Functions, Maxima and Minima
Chapter 5 - Complex Phenomena and Elementary Functions
5.1 The World of Linear Laws
5.2 Quadratic Laws
5.3 Power Functions and the Dimensions of Life
Chapter 6 - Population Dynamics and Biological Rhythms
6.1 Exponential Functions
6.2 Logarithms
Chapter 7 - Predicting the Distant Future
7.1 The Problem of Asymptotic Behavior
7.2 Calculating Limits
7.3 Rates of Divergence and Convergence
Chapter 8 - The Laws of Change
8.1 Average and Instantaneous Rate of Change
8.2 Rules for Differentiation
8.3 Derivatives: User Guide
8.5 Evolution Models with Continuous Time
Chapter 9 - Integrals
9.1 From Derivative to Function
9.2 Integrating
9.3 Differentiating
Prerequisites for admission
Solid background in mathematics at the level of the scientific high school diploma.
Teaching methods
The course will be conducted through lectures, both traditional and interactive, including group work on various types of problems or questions, as well as practice sessions focusing on more technical aspects.
The main reference for course materials is the textbook. Any additional materials will be made available to students on MyAriel.
The main reference for course materials is the textbook. Any additional materials will be made available to students on MyAriel.
Teaching Resources
Benedetto, D., Degli Esposti, M., & Maffei, C. (2014). Dalle funzioni ai modelli. Il calcolo per le Bioscienze. Casa Editrice Ambrosiana.
Further materials will be uploaded on MyAriel during the course.
Further materials will be uploaded on MyAriel during the course.
Assessment methods and Criteria
Assessment of learning is carried out through separate exams for the Mathematics and Statistics components. The two tests jointly contribute to a single final grade.
With regard to the Mathematics component, assessment consists of multiple tests:
Test A, consisting of questions focused on the course syllabus (exercises and problems involving description, interpretation, and explanation of mathematical models);
Test B, consisting of a modeling problem.
The two parts may be taken during the same exam session or in two separate sessions, provided both are completed within the exam sessions scheduled at the time the course is offered (i.e., by February of the academic year following the course). In later sessions, the student will be required to retake both tests.
In November, there will be the option to take an intermediate test that can partially replace Test A. In this case, during the exam, the student will be required to complete only part of the questions from Test A and/or Test B.
The objective of Test A is to assess the student's knowledge and understanding of the topics covered in lectures and exercises, their ability to apply them to answer questions by describing, interpreting, and explaining simple mathematical models, as well as their use of appropriate mathematical language. Evaluation criteria focus on the correctness of conceptual and algorithmic aspects, as well as the accuracy and effectiveness of the language used.
The objective of Test B is to assess whether the student can solve a modeling problem using the mathematical tools introduced during the course appropriately. Evaluation criteria focus on the student's independent judgment, particularly regarding the effectiveness of the choices made in mathematizing the problem and contextualizing the model, the conceptual appropriateness and contextual relevance of the mathematical tools selected, the accuracy of the solution, and the quality of its communication (clarity, conciseness, and proper use of technical language).
With regard to the Mathematics component, assessment consists of multiple tests:
Test A, consisting of questions focused on the course syllabus (exercises and problems involving description, interpretation, and explanation of mathematical models);
Test B, consisting of a modeling problem.
The two parts may be taken during the same exam session or in two separate sessions, provided both are completed within the exam sessions scheduled at the time the course is offered (i.e., by February of the academic year following the course). In later sessions, the student will be required to retake both tests.
In November, there will be the option to take an intermediate test that can partially replace Test A. In this case, during the exam, the student will be required to complete only part of the questions from Test A and/or Test B.
The objective of Test A is to assess the student's knowledge and understanding of the topics covered in lectures and exercises, their ability to apply them to answer questions by describing, interpreting, and explaining simple mathematical models, as well as their use of appropriate mathematical language. Evaluation criteria focus on the correctness of conceptual and algorithmic aspects, as well as the accuracy and effectiveness of the language used.
The objective of Test B is to assess whether the student can solve a modeling problem using the mathematical tools introduced during the course appropriately. Evaluation criteria focus on the student's independent judgment, particularly regarding the effectiveness of the choices made in mathematizing the problem and contextualizing the model, the conceptual appropriateness and contextual relevance of the mathematical tools selected, the accuracy of the solution, and the quality of its communication (clarity, conciseness, and proper use of technical language).
MAT/04 - MATHEMATICS EDUCATION AND HISTORY OF MATHEMATICS - University credits: 8
MAT/06 - PROBABILITY AND STATISTICS - University credits: 4
MAT/06 - PROBABILITY AND STATISTICS - University credits: 4
Practicals: 48 hours
Practicals with elements of theory: 48 hours
Lessons: 40 hours
Practicals with elements of theory: 48 hours
Lessons: 40 hours
Professors:
Asenova Miglena, Ugolini Stefania
Professor(s)
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