Informatics and Statistics for Biotechnologies (common)

Computer science
This module will begin with an introduction on how do systems for data processing work, while the rest of thecourse will be dedicated to the principles of imperative and modular programming, using the R language as areference.

- Architecture of a computer
- Notes on the representation of information
- Main concepts of a generic programming language
- Programs and processes
- R Environment
- Variables and assignments
- ector data structure. Numeric, character, logical vectors
- Selection and access, different types
- Generation of regular sequences
- Matrices: construction, concatenation, product. Selection and access
- Comand table
- Heterogeneous data management: lists, data frames, their types of access
- Flow control
- Conditional instructions and cycles
- Cycle efficiency in R
- Functions and Scripts
- R graphical environment
- Plot, barplot, hist, boxplot
- 2D e 3D pie charts
- Figures saving
- Generation of pseudorandom numeric samples
- Probability distributions
- qqnorm, qqline, qqplot functions to compare probability distributions


Statistics
This module is designed to provide the basics of statistics for biotechnology. Particular attention is given to the applied statistics, with special emphasis on the interpretation of the data and the tests used in the analysis of biological data. The course includes numerous practical examples and provides the student with basic knowledge that allows to outline a logical workflow to support the choice of the most appropriate statistical approach when approaching a specific biological problem.

- Introduction to statistics. Basic concepts, populations and samples, sampling, data types, types of variables, types of studies
- Data visualization. Frequency tables, charts and histograms
- Statistical indexes and data description
- Estimation and uncertainty
- Probability. Event, probability of an event. Probability of complex events
- Probability distributions
- Hypothesis testing
- Statistical tests for nominal variables. Goodness-of-fit chi square test. Contingency tables, Odds Ratio, chi square test for independence, Fisher's exact test
- Statistical tests for continuous and discrete variables

Informatics
- Sketch of Computer architectures
- Some notes on information representation
- Program languages, main definitions
- Programs and processes
- R language environment
- Variables and assignments
- Vector data structure
- Accessing to vector elements
- Generating numeric sequences
- Matrices
- table command
- Heterogeneous data: lists and data frames
- Conditional and loop statements
- Loop efficiency in R
- Functions and scripts in R
- R graphical environment
- plot, barplot, hist, boxplot commands
- Pie charts
- Saving figures
- Graphical approach: qqnorm, qqline and qqplot functions


Statistics
- Introduction to statistics. Basic concepts, populations and samples, sampling, data types, types of variables, types of studies
- Data visualization. Frequency tables, charts and histograms
- Statistical indexes and data description
- Estimation and uncertainty
- Probability. Event, probability of an event. Probability of complex events
- Probability distributions
- Hypothesis testing
- Statistical tests for nominal variables. Goodness-of-fit chi square test. Contingency tables, Odds Ratio, chi square test for independence, Fisher's exact test
- Statistical tests for continuous and discrete variables