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 data manipulation, synthesis and analysis
3. (making judgements)
-- correctly interpret the results of analyses produced by statistical softwares

It is assumed that the student has achieved the educational objectives of the Probability and Statistics course (www.unive.it/data/course/230177) even if it is not formally required to pass the exam. It is important that 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 Scientists. Second Edition. CRC Press. Selected parts of chapters 8-9-10-11
- Further reading 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 are designed to measure
1. the theoretical knowledge of the topics of the course,
2. the ability to apply the theory to solve real-world data problems.

The maximum score for each exercise is 8 points. The final score is given by the sum of the scores of the four exercises. To pass the exam it is necessary to obtain a sufficient score 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 exceeding 30 points corresponds to honours.

During the exam it is allowed to use a laptop and the cheat sheet made available by the teacher. No books, notes or other electronic media are allowed.

There will be an intermediate exam after the middle of the course. The intermediate test corresponds to the first part of the exam (two exercises). If the intermediate test is passed (with a score of at least 9 points) the student will be able to take only the second part of the exam during the first session (**only the first session**) and the final score will be given by the sum of the score obtained in the intermediate test and the score obtained in the second part of the exam of the first session.

Conventional theoretical lectures complemented by exercises and discussion of case studies. Teaching material prepared by the teacher 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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