The course aims at providing students with theoretical and practical tools (using Python) useful to analyse and process data sets of biological and biotechnological kind.

1. Descriptive Statistics
1.1 Statistical data and their graphical representation: cartesian diagrams, bar graphs, histograms, aerograms (pie charts).
1.2 Absolute, relative, percent and cumulative frequences.
1.3 Centrality (position) indexes: mean, mode and median. Quantiles and quartiles.
1.4 Dispersion (variability) indexes: variance, standard deviation, mean absolute difference, range, variation coefficient, interquartile difference.
1.5 Shape indexes: skewness, kurtosis.
1.6 Interpolation and approximation of experimental data.
1.7 Simple linear regression: the mean squares line; index R^2.
1.8 Pearson's linear correlation coefficient.
1.9 Regression and correlation; residual analysis.

2. Probability Theory
2.1 Events and definitions of probability of an event: classical, frequentistic and axiomatic definition by Kolmogorov.
2.2 Disjoint and independent events. Conditional probability.
2.3 Theorem of the sum and of the product; total probability theorem; Bayes' theorem.
2.4 Diagnostic tests. Sensibility, specificity and predictive value of a test.
2.5 Hints of combinatorics: permutations, combinations and dispositions.
2.6 Random variables (r.vs.): discrete and (absolutely) continuous r.vs; cumulative distribution function, distribution or density function of a r.v.
2.7 Centrality and variability indexes for r.vs.
2.8 Probability distribution functions of (discrete) r.vs: Bernoulli, binomial, Poisson r.vs.
2.9 Probability density functions of (coninuous) r.vs: exponential and Gausian r.vs. Use of the tables of the standard Gaussian r.v.
2.10 Central Limit Theorem and corollaries. 

3. Basic Inferential Statistics
3.1 Deductive and inductive inference; direct and inverse inference.
3.2 Population and sample. Distribution of the sample mean for a normal population and of the sample proportion of a Bernoulli population (large samples).
3.3 Hypotheses testing (Neyman-Pearson) for the mean of a normal population and the proportion of a Bernoulli population (large samples): p-value.

4. Examples of applications using Python

Although not compulsory, having passed the exam of Mathematics (first year, 8 CFU) is highly recommended, especially for the comprehension of Section 2,  "Probability Theory".

Students will have to show:

D1. A detailed knowledge and not mnemonic comprehension of the treated subjects
D2. The ability to apply the known statistical methods to different and new contexts
D3. The capability of using simple computational tools (Python) for statistical data visualization and processing 
D4. The ability to interpret the achieved experimental results on the basis of the used statistical methods
D5. The use of specific scientific language while exposing the treated subjects.

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

Not available

Teaching

- Frontal lessons

- Examples and exercises using Python in the computer laboratory

- The whole material uploaded in the Moodle platform http://blended.uniurb.it

Attendance

Not compulsory 

Course books

M. Carletti, Elaborazione Statistica dei dati Sperimentali, McGraw-Hill, 2019 (credit M. Abate, McGraw-Hill) or any other equivalent book in English.

Assessment

Laboratory project in google Colab to be prepared at home and delivered before the exam.

Written exam with 11 closed-ended questions. For each question: 3pt correct, 0pt not answered, -1pt wrong.

The final mark is the weighted average (25% project, 75% written exam) of the project and the written examination.

Possibility to increase the mark with an oral examination.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

Teaching

- Use of the textbook

- Weekly check in the Moodle platform http://blended.uniurb.it

Attendance

Not compulsory

Course books

The same as attending students

Assessment

The same as attending students

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

None