DATA ANALYSIS

This course belongs to the interdisciplinary educational activities of the Data Science curriculum of the Bachelor in Computer Science. The course aims at providing students with the basic tools of statistical inference and data analysis. The objective of the course is to develop skills to answer statistical questions that arise in the technological, scientific, biomedical, economic and business fields. Special attention will be paid to the integration of the methodology with computational tools through the use of the R language. The achievement of the educational objectives of the course allows the student to obtain the basis for learning more advanced data science tools.

Regular and active participation in the teaching activities and individual study will allow students to:
1. (knowledge and understanding)
-- know and understand the main inferential methods
2. (applying knowledge and understanding)
-- synthetize and model phenomena characterized by variability and uncertainty
-- use statistical software for the manipulation, synthesis and analysis of data
3. (making judgements)
-- correctly interpret the results of analyzes produced by statistical softwares

Students are assumed to have reached the learning objectives of the course Probability e Statistics (www.unive.it/data/course/230177) although it is not formally required to have passed the exam. It is important that the students have a solid familiarity with the main properties and operations involving discrete and continuous random variables.

The course program includes presentation and discussion of the following topics:

1. Basic concepts
2. Point estimation
3. Interval estimation
4. Hypothesis testing
5. Dependence
Methods will be illustrated with simulated and real data using the R language (www.r-project.org).

- Baron M (2014). Probability and Statistics for Computer Scientistis. Second Edition. CRC Press. Selected parts of chapters 8-9-10-11
- Additional readings and materials distributed during the course through the Moodle platform

The achievement of the course objectives is assessed through a written exam. The exam consists of two parts. Each part consists of two exercises. The four exercises designed to measure
1. the theoretical knowledge of the course topics,
2. the ability to apply the theory to solve real data problems.

The maximal score for each exercise is 8 points. The final score is the sum of the scores of the four exercises. The exam is passed if sufficient score is obtained in each of the two parts, i.e. at least 9 points for each part. If the first part is not sufficient, then the second part of the exam will not be corrected. An overall score that exceeds 30 points corresponds to 30 with honors.

During the exam, the use of a laptop and the formula form made available by the teacher is allowed. No books, notes or other electronic media are allowed.

There will be a mid-term exam after half of the course. The mid-term exam corresponds to the first part of the exam (two exercises). If the intermediate test is passed (with a score of at least 9 points) then the student can only take the second part of the exam during the first session (**only the first session**) and the final score will be given from the sum of the score obtained in the mid-term exam and the score obtained in the second part of the exam of the first session.

Conventional theoretical lectures complemented by exercise classes and discussion of case studies. Teaching material prepared by the lecturer will be distributed during the course through the Moodle platform. The statistical software used in the course is R (www.r-project.org).

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