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 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. A total score exceeding 30 corresponds to 30 with honors. During the written test the use of books, notes, or electronic media is *not* allowed.

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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