Scuola Universitaria Interdipartimentale in Scienze Strategiche (SUISS)

VP1 (INTRODUCTION OF DATE ANALYSIS IN NATURAL AND SOCIAL SCIENCES )

Introduction Of Date Analysis In Natural And Social Sciences

Anno accademico 2017/2018

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Sommario insegnamento

• Obiettivi formativi
• Modalità di insegnamento
• Modalità di verifica dell'apprendimento
• Programma
• Testi consigliati e bibliografia
• Strumenti didattici

Obiettivi formativi

This course is designed for students with diverse backgrounds with the aim of introducing them to basic principles of quantitative data analysis. Students will get a chance to examine and analyze real data from different application areas, such as, genomics, medicine, biotechnology and social sciences. The course will bring the students to speed to the latest data analysis techniques with hands-on experience of executing these methods on data using different softwares (Microsoft excel, publicly available software R). The course assumes no background in software, Statistics or programming.  Course evaluation is heavily based on a project, where students will work in a group to critique and present a research paper using data science tools in the context of different application areas.

Modalità di insegnamento

Interactive lessons with hands-on in-class analysis of data sets using excel and R

Modalità di verifica dell'apprendimento

The course evaluation will be based on homework assignments (40%), one final project (40%), and class participation based on discussions during lectures (20%). Concepts introduced in lectures will be reinforced through computer-based homework assignments given on a biweekly basis, where students will use software to perform basic data analysis.  Towards the end of class, student from different backgrounds will come together to form a group (2-3 students per group), and each group will be a assigned a research paper that analyses data from specific application areas. The project evaluation will be based on presenting the paper to the class, and answering question pertinent to the project. 

Programma

• Introduction to Data Gathering and Analysis 
• Graphical/Numerical methods for describing data
• Basic Statistics; Central Tendency; Dispersion
• Bivariate plots, covariance, correlation
• Simple Regression; Least Square; Intercept and Slope
• Multiple Regression
• Significance and Hypothesis testing
• Time series and autocorrelation
• Basics of Learning; Supervised Learning and Unsupervised Learning

Lecture notes and references provided therein.

Orario lezioni

• Materiale didattico
• Bacheca appelli
• Vai a Moodle
• Visita i forum