Statistics and Data Analysis
A.Y. 2018/2019
Learning objectives
The course aims at introducing the bases of descriptive statistics, probability theory and inferential statistics.
Expected learning outcomes
Students will acquire basic skills allowing them to summarize a data sample through numerical indices and graphical representations, to reason in terms of the main probability distributions, to perform simple statistical analyses, to understand statistical analyses performed by others, and to study more complex data analysis techniques.
Lesson period: First semester
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
Milan
Responsible
Lesson period
First semester
ATTENDING STUDENTS
Course syllabus
NON-ATTENDING STUDENTS
Introduction to python
Descriptive statistics:
- Frequencies and cumulate frequencies. Joined and marginal frequencies.
- Indices of centrality, dispersion, correlation, heterogeneity and concentration.
- Graphical methods: scatter plots, box plots and QQ plots.
- Classificators and ROC curves.
Probability:
- Combinatorics. Basics of set theory.
- Probability axioms.
- Conditional probability. Bayes' theorem. Independence.
- Discrete random variables.
- Multivariate random variables. Independent random variables.
- Continuous random variables.
- Markov and Tchebyshev inequalities.
- Bernoulli, binomial. geometric. Poisson, discrete uniform and hypergeometric models.
- Continuous uniform, exponential and gaussian models.
- Poisson process.
Inferential statistics:
- Population, random sample and point estimates.
- Sample mean. Central limit theorem.
- Sample variance.
- Unbiasedness and Consistency in mean square.
- Computation of the sample size.
Descriptive statistics:
- Frequencies and cumulate frequencies. Joined and marginal frequencies.
- Indices of centrality, dispersion, correlation, heterogeneity and concentration.
- Graphical methods: scatter plots, box plots and QQ plots.
- Classificators and ROC curves.
Probability:
- Combinatorics. Basics of set theory.
- Probability axioms.
- Conditional probability. Bayes' theorem. Independence.
- Discrete random variables.
- Multivariate random variables. Independent random variables.
- Continuous random variables.
- Markov and Tchebyshev inequalities.
- Bernoulli, binomial. geometric. Poisson, discrete uniform and hypergeometric models.
- Continuous uniform, exponential and gaussian models.
- Poisson process.
Inferential statistics:
- Population, random sample and point estimates.
- Sample mean. Central limit theorem.
- Sample variance.
- Unbiasedness and Consistency in mean square.
- Computation of the sample size.
Course syllabus
Introduction to python
Descriptive statistics:
- Frequencies and cumulate frequencies. Joined and marginal frequencies.
- Indices of centrality, dispersion, correlation, heterogeneity and concentration.
- Graphical methods: scatter plots, box plots and QQ plots.
- Classificators and ROC curves.
Probability:
- Combinatorics. Basics of set theory.
- Probability axioms.
- Conditional probability. Bayes' theorem. Independence.
- Discrete random variables.
- Multivariate random variables. Independent random variables.
- Continuous random variables.
- Markov and Tchebyshev inequalities.
- Bernoulli, binomial. geometric. Poisson, discrete uniform and hypergeometric models.
- Continuous uniform, exponential and gaussian models.
- Poisson process.
Inferential statistics:
- Population, random sample and point estimates.
- Sample mean. Central limit theorem.
- Sample variance.
- Unbiasedness and Consistency in mean square.
- Computation of the sample size.
Descriptive statistics:
- Frequencies and cumulate frequencies. Joined and marginal frequencies.
- Indices of centrality, dispersion, correlation, heterogeneity and concentration.
- Graphical methods: scatter plots, box plots and QQ plots.
- Classificators and ROC curves.
Probability:
- Combinatorics. Basics of set theory.
- Probability axioms.
- Conditional probability. Bayes' theorem. Independence.
- Discrete random variables.
- Multivariate random variables. Independent random variables.
- Continuous random variables.
- Markov and Tchebyshev inequalities.
- Bernoulli, binomial. geometric. Poisson, discrete uniform and hypergeometric models.
- Continuous uniform, exponential and gaussian models.
- Poisson process.
Inferential statistics:
- Population, random sample and point estimates.
- Sample mean. Central limit theorem.
- Sample variance.
- Unbiasedness and Consistency in mean square.
- Computation of the sample size.
INF/01 - INFORMATICS - University credits: 6
Practicals: 36 hours
Lessons: 24 hours
Lessons: 24 hours
Professor:
Malchiodi Dario
Professor(s)