Computer Technology and Statistics Knowledge
A.Y. 2020/2021
Learning objectives
The course aims to provide:
- knowledge of descriptive statistics.
- notions for the use of position and variability indicators.
- acquisition of the principles and techniques of regression and correlation between variables.
- knowledge of the fundamental notions of computer science.
- skills on spreadsheet management, to create formulas, to carry out statistical tests, and to create graphs.
- ability to use search engines for information retrieval.
- knowledge of descriptive statistics.
- notions for the use of position and variability indicators.
- acquisition of the principles and techniques of regression and correlation between variables.
- knowledge of the fundamental notions of computer science.
- skills on spreadsheet management, to create formulas, to carry out statistical tests, and to create graphs.
- ability to use search engines for information retrieval.
Expected learning outcomes
At the end of the course the student should be able to:
- describe phenomena using the main statistical indicators.
- prepare sample survey plans.
- apply the analysis of the variance with one and two factors.
- objectively evaluate the results of statistical analyses.
- use software packages for data management, statistical processing, data storage and their graphic representation.
- be able to use bibliographic databases.
- describe phenomena using the main statistical indicators.
- prepare sample survey plans.
- apply the analysis of the variance with one and two factors.
- objectively evaluate the results of statistical analyses.
- use software packages for data management, statistical processing, data storage and their graphic representation.
- be able to use bibliographic databases.
Lesson period: Second semester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
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
Single session
Lesson period
Second semester
At current status, the large majority of lessons will be kept online by means of the Teams platform, as specified at the voice "didactical methods".
Ariel is the official communication channel that will be used to updates and news, includind new dispositions for sanitary emergency. MS Teams is used only during lessons. Please verify your connection and request access if needed.
Ariel is the official communication channel that will be used to updates and news, includind new dispositions for sanitary emergency. MS Teams is used only during lessons. Please verify your connection and request access if needed.
Course syllabus
Course topics loosely consists of four units:
1) Data, measurements and experiments:
The meaning of measurements. Precision of a measurement. Measurement errors and their estimate. Sistematic and stocastic errors. Bias. Precision and accuracy. Uncertainty and randomness. Bayesian and Frequentistic interpretation. Relative and absolute errors. Square sums. Error propagation.
2) Notions of informatics:
Computer, memory, files. Programming languages: interpreted and compiled. The role of syntax. Tools: Python programming language, Google Sheets, Excel. Variables. Types of data. Loop and conditional statements. Multidimensional data (vectors and matrices). Functions, procedures, objects, modules. Exercises: Creation and processing of data. Basic input/output.
3) Stocastic Phenomena:
What is random? What is statistics? Law of big numbers. Distributions. Statistical indicators: mean and standard deviation. Normal distribution. Other common distributions. Central limit theorem. Maximum likelihood criteria. Exercises: random numbers, combinatory analysis, plot of data and distributions.
4) Models and applications:
Linear fit. Least square law. Higher order fit. Chi Square and fitness estimate. Standard error of mean. Monte Carlo methods. Examples of applications and analysis of real data.
1) Data, measurements and experiments:
The meaning of measurements. Precision of a measurement. Measurement errors and their estimate. Sistematic and stocastic errors. Bias. Precision and accuracy. Uncertainty and randomness. Bayesian and Frequentistic interpretation. Relative and absolute errors. Square sums. Error propagation.
2) Notions of informatics:
Computer, memory, files. Programming languages: interpreted and compiled. The role of syntax. Tools: Python programming language, Google Sheets, Excel. Variables. Types of data. Loop and conditional statements. Multidimensional data (vectors and matrices). Functions, procedures, objects, modules. Exercises: Creation and processing of data. Basic input/output.
3) Stocastic Phenomena:
What is random? What is statistics? Law of big numbers. Distributions. Statistical indicators: mean and standard deviation. Normal distribution. Other common distributions. Central limit theorem. Maximum likelihood criteria. Exercises: random numbers, combinatory analysis, plot of data and distributions.
4) Models and applications:
Linear fit. Least square law. Higher order fit. Chi Square and fitness estimate. Standard error of mean. Monte Carlo methods. Examples of applications and analysis of real data.
Prerequisites for admission
Knowledge and understanding of some mathematical concepts, subject of the first semester course, is needed. In particular derivative, integration, and determination of maxima and minima are useful tools for the practical part of the course.
Teaching methods
All didactics and material is in Italian, unless specific requests. You are invited to contact the responsible of the class for any language-related issue.
Fundaments of programming and statistics will presented mainly by means of Python programming language. It is however given to students the possibility of using a tool of their choice among Python tools and spreadsheets (e.g. Excel or GSheets), or any other suitable means, locally or remotely.
- Copy of lessons notes and material are upoaded on Ariel, usually not too long after the end of lessons.
Ariel is the official communication channel that will be used to updates and news, MS Teams is used only during lessons. Videos are automatically available on Teams at the end of each lesson. Videos will be updated on Ariel when possible.
Fundaments of programming and statistics will presented mainly by means of Python programming language. It is however given to students the possibility of using a tool of their choice among Python tools and spreadsheets (e.g. Excel or GSheets), or any other suitable means, locally or remotely.
- Copy of lessons notes and material are upoaded on Ariel, usually not too long after the end of lessons.
Ariel is the official communication channel that will be used to updates and news, MS Teams is used only during lessons. Videos are automatically available on Teams at the end of each lesson. Videos will be updated on Ariel when possible.
Teaching Resources
Statistics:
Taylor - An Introduction to Error Analysis
very good and readable book, it might be difficult to find (a replacement will be indicated if this is not available).
Informatics:
Depending on the choice of tools
Python
every beginner book is good, but there is also a lot of online material including interactive courses, es:
Dive_into_Python_3 (free online book)
or anything :
https://pythonitalia.github.io/python-abc/
https://www.python.it/doc/libri/
there are plenty of good free courses (some of them offer only a part for free, but this is more than enough for the course), e.g.:
https://www.kaggle.com/learn/python
https://www.codecademy.com/learn/learn-python
https://www.edx.org/professional-certificate/introduction-to-python-programming
https://www.edx.org/course/python-basics-for-data-science
https://www.coursera.org/learn/python
Excel and Google Sheets:
There are for sure very good tutorials on MS and Google help pages, e.g.:
https://support.google.com/docs/answer/9331278?hl=en&ref_topic=9296611
more details will be added.
Taylor - An Introduction to Error Analysis
very good and readable book, it might be difficult to find (a replacement will be indicated if this is not available).
Informatics:
Depending on the choice of tools
Python
every beginner book is good, but there is also a lot of online material including interactive courses, es:
Dive_into_Python_3 (free online book)
or anything :
https://pythonitalia.github.io/python-abc/
https://www.python.it/doc/libri/
there are plenty of good free courses (some of them offer only a part for free, but this is more than enough for the course), e.g.:
https://www.kaggle.com/learn/python
https://www.codecademy.com/learn/learn-python
https://www.edx.org/professional-certificate/introduction-to-python-programming
https://www.edx.org/course/python-basics-for-data-science
https://www.coursera.org/learn/python
Excel and Google Sheets:
There are for sure very good tutorials on MS and Google help pages, e.g.:
https://support.google.com/docs/answer/9331278?hl=en&ref_topic=9296611
more details will be added.
Assessment methods and Criteria
The exam will consist in the discussion of an elaborate presented by the student (chosen between a spreadsheet, a Python script or notebook, or an Orange project) in which tools and knowledge learned during the course will be applied to a practical problem. The elaborate will be the starting point for a discussion of the theoretical concepts learnt during the course.
- University credits: 6
Lessons: 48 hours
Professor:
Cotroneo Vincenzo