Basic Statistics with Computer Applications
A.Y. 2026/2027
Learning objectives
The course aims to provide basic knowledge and skills in the field of informatics and statistics, with particular attention to their use for reading, synthetizing, analyzing and interpreting complex phenomena. Therefore, the basic concepts of descriptive statistics will be introduced, such as the different ways of organization and representation of the data, the measures of central tendency and the associated indices of dispersion, the study of the relation between two statistical variables.
With the aim of developing statistical and computational thinking, the theoretical notions will be transferred into the application field through the use of up-to-date computer tools.
The transversal aim of the course is to provide digital, communicative, relational, decision-making and problem-solving skills useful for addressing the academic career and the world of work.
With the aim of developing statistical and computational thinking, the theoretical notions will be transferred into the application field through the use of up-to-date computer tools.
The transversal aim of the course is to provide digital, communicative, relational, decision-making and problem-solving skills useful for addressing the academic career and the world of work.
Expected learning outcomes
At the end of the course, the student will be able to:
· use the main tools for information collection and use public sources to derive secondary data;
· represent phenomena related to daily experience, both graphically and through appropriate synthesis values, and interpret them also through the exploration of dependency relationships between variables;
· use software and write scripts useful for management, processing, automation, archiving, data representation;
· design and produce multimedia contents with independent judgement to offer third-party users knowledge on key issues of agricultural and environmental sciences;
· adopt an ethical approach to the use of communication and information technologies;
· make a critical use of participative web tools
· use the main tools for information collection and use public sources to derive secondary data;
· represent phenomena related to daily experience, both graphically and through appropriate synthesis values, and interpret them also through the exploration of dependency relationships between variables;
· use software and write scripts useful for management, processing, automation, archiving, data representation;
· design and produce multimedia contents with independent judgement to offer third-party users knowledge on key issues of agricultural and environmental sciences;
· adopt an ethical approach to the use of communication and information technologies;
· make a critical use of participative web tools
Lesson period: Second semester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
The course, worth 6 CFU (32 hours lessons + 32 hours practice), is divided into two components of equal weight: Computer Science (32 hours) and Statistics (32 hours). The course activities involve a progressive integration of theoretical knowledge and practical computer exercises using Excel.
Part 1 - Computer Science (32 hours)
Basic principles of using Excel: organizing worksheets, saving and managing files, cell references, mathematical and statistic functions, and complex formulas.
Importing and managing data from text files and CSV files.
New variables in dataset, with =IF( ), =OR( ) and =AND( ) functions and their interactions.
Pivot tables and key functions for database management and analysis.
Graphical representation of data: pie charts, histograms, radar, stock and surface charts, and XY scatter plots, area, funnel and hierarchical charts.
Selecting and creating appropriate graphical representations and optimally presenting results.
Practical exercises focused on managing, processing, and representing datasets related to agri-food topics.
Part 2 - Statistics (32 hours)
Organization and representation of statistical data.
Main methods of descriptive statistics and related measures of central tendency and dispersion.
Study of the relationship between two statistical variables and the principles of regression.
One-way analysis of variance.
Use of Excel's "Data Analysis" feature for statistical processing. Practical exercises on datasets related to agri-food topics, aimed at applying statistical methods, correctly processing data, and interpreting results.
The distribution of course activities—32 hours of Computer Science and 32 hours of Statistics—ensures consistency between the expected academic workload and the 6 CFU credits for the course. Within each component, theoretical activities are progressively integrated with practical exercises, in which the knowledge acquired is applied to problem-solving and data analysis of experimental data, sensors and data management in plant and animal trial.
Part 1 - Computer Science (32 hours)
Basic principles of using Excel: organizing worksheets, saving and managing files, cell references, mathematical and statistic functions, and complex formulas.
Importing and managing data from text files and CSV files.
New variables in dataset, with =IF( ), =OR( ) and =AND( ) functions and their interactions.
Pivot tables and key functions for database management and analysis.
Graphical representation of data: pie charts, histograms, radar, stock and surface charts, and XY scatter plots, area, funnel and hierarchical charts.
Selecting and creating appropriate graphical representations and optimally presenting results.
Practical exercises focused on managing, processing, and representing datasets related to agri-food topics.
Part 2 - Statistics (32 hours)
Organization and representation of statistical data.
Main methods of descriptive statistics and related measures of central tendency and dispersion.
Study of the relationship between two statistical variables and the principles of regression.
One-way analysis of variance.
Use of Excel's "Data Analysis" feature for statistical processing. Practical exercises on datasets related to agri-food topics, aimed at applying statistical methods, correctly processing data, and interpreting results.
The distribution of course activities—32 hours of Computer Science and 32 hours of Statistics—ensures consistency between the expected academic workload and the 6 CFU credits for the course. Within each component, theoretical activities are progressively integrated with practical exercises, in which the knowledge acquired is applied to problem-solving and data analysis of experimental data, sensors and data management in plant and animal trial.
Prerequisites for admission
The student should know the mathematical language and procedure
Teaching methods
The course activities consist of theoretical lectures and hands-on computer exercises, using Excel for both the computer science and statistical components.
The theoretical lectures are designed to provide students with knowledge of data management, statistical procedures, and the principles underlying the methodologies used. The hands-on exercises allow students to transform this knowledge into practical skills through problem-solving and the processing of datasets.
The use of Excel allows students to directly apply the knowledge they have acquired to the various stages of the analysis process: data organization, processing, graphical representation, application of statistical procedures, and interpretation of results.
The practical component is therefore designed to achieve learning outcomes related to the ability to manage, process, represent, and interpret data using computer tools.
The use of datasets related to agri-food topics also facilitates the application of knowledge to real-world problems and the development of analytical, interpretive, and problem-solving skills.
The theoretical lectures are designed to provide students with knowledge of data management, statistical procedures, and the principles underlying the methodologies used. The hands-on exercises allow students to transform this knowledge into practical skills through problem-solving and the processing of datasets.
The use of Excel allows students to directly apply the knowledge they have acquired to the various stages of the analysis process: data organization, processing, graphical representation, application of statistical procedures, and interpretation of results.
The practical component is therefore designed to achieve learning outcomes related to the ability to manage, process, represent, and interpret data using computer tools.
The use of datasets related to agri-food topics also facilitates the application of knowledge to real-world problems and the development of analytical, interpretive, and problem-solving skills.
Teaching Resources
All course materials necessary for exam preparation—including handouts, presentations, examples, and materials used during the exercises—are made available to students through the Team course assigned to each academic year.
The following texts are recommended as supplementary reading and for further study:
M. K. Pelosi, T. M. Sandifer, Introduction to Statistics, McGraw-Hill, 2009.
D. Giuliani, M. M. Dickson, Statistical Analysis with Excel, Apogeo, 2015.
The listed texts are not the only sources for preparing for the final exam. The content to be assessed consists of the material covered during lectures and exercises and included in the course materials made available on Teams. Students may use these texts to explore and reinforce the topics covered.
The following texts are recommended as supplementary reading and for further study:
M. K. Pelosi, T. M. Sandifer, Introduction to Statistics, McGraw-Hill, 2009.
D. Giuliani, M. M. Dickson, Statistical Analysis with Excel, Apogeo, 2015.
The listed texts are not the only sources for preparing for the final exam. The content to be assessed consists of the material covered during lectures and exercises and included in the course materials made available on Teams. Students may use these texts to explore and reinforce the topics covered.
Assessment methods and Criteria
The final assessment of learning consists of two individual practical exams, one covering the Statistics component and one covering the Computer Science component. Each exam involves solving applied problems presented through datasets and carried out using Excel.
For both exams, the following are evaluated: the ability to understand the problem presented, identify and correctly apply the required procedures, use Excel tools, process the data, and achieve the required result.
In the Statistics test, particular emphasis is placed on the accuracy of the final result, as well as on the correct methodological approach and the proper execution of the required procedures. The exam is proposed with some exercises (8-10 with different difficulty levels). In the Computer Science test, the assessment focuses specifically on the correct handling and processing of data, the appropriate use of Excel tools, and the correct production of the required results. The exam ask to prepare some tables (3-4) and graphs (2-3 with different difficulty levels) on the basis of a simplified Excel database.
The main evaluation criteria are therefore:
correctness of the approach and procedure used;
correctness of data processing;
correctness of the final result;
ability to use Excel tools appropriately;
ability to present and, where required, correctly interpret the results.
The grade is expressed as "PASS" or NO PASS. The two exams contribute to the overall assessment of learning for the course. The results of the assessments are communicated to students via the course's dedicated Team.
Throughout the course, exercises and mock exams are also provided—for both the statistics and computer science components—to allow students to progressively assess their level of mastery of the required knowledge and skills.
Students with SLD or disability certifications are kindly requested to contact the teacher at least 15 days before the date of the exam session to agree on individual exam requirements. In the email please make sure to add in cc the competent offices: [email protected] (for students with SLD) o [email protected] (for students with disability).
For both exams, the following are evaluated: the ability to understand the problem presented, identify and correctly apply the required procedures, use Excel tools, process the data, and achieve the required result.
In the Statistics test, particular emphasis is placed on the accuracy of the final result, as well as on the correct methodological approach and the proper execution of the required procedures. The exam is proposed with some exercises (8-10 with different difficulty levels). In the Computer Science test, the assessment focuses specifically on the correct handling and processing of data, the appropriate use of Excel tools, and the correct production of the required results. The exam ask to prepare some tables (3-4) and graphs (2-3 with different difficulty levels) on the basis of a simplified Excel database.
The main evaluation criteria are therefore:
correctness of the approach and procedure used;
correctness of data processing;
correctness of the final result;
ability to use Excel tools appropriately;
ability to present and, where required, correctly interpret the results.
The grade is expressed as "PASS" or NO PASS. The two exams contribute to the overall assessment of learning for the course. The results of the assessments are communicated to students via the course's dedicated Team.
Throughout the course, exercises and mock exams are also provided—for both the statistics and computer science components—to allow students to progressively assess their level of mastery of the required knowledge and skills.
Students with SLD or disability certifications are kindly requested to contact the teacher at least 15 days before the date of the exam session to agree on individual exam requirements. In the email please make sure to add in cc the competent offices: [email protected] (for students with SLD) o [email protected] (for students with disability).
- University credits: 6
Exercises: 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professors:
Parma Pietro, Tamburini Alberto Giuseppe Carlo Maria
Shifts:
Professor(s)
Reception:
by appointment
Agricultural and Environmental Sciences - Production, Landscape, Agroenergy, Building 6
Reception:
by appointment (send an email)
Dipartimento di Scienze Agrarie e Ambientali (area Zootecnica) via Celoria 2 Milano