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Statistics and data analysis

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Statistics and data analysis

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Academic year 2022/2023

Course ID
NEU0264A
Teachers
Prof.ssa Caterina Guiot (Lecturer)
Dott. Fabrizio Pizzagalli (Lecturer)
Prof. Federico D'agata (Lecturer)
Ilaria Stura (Lecturer)
Year
1st year
Teaching period
First semester
Type
Distinctive
Credits/Recognition
5
Course disciplinary sector (SSD)
FIS/07 - applied physics (a beni culturali, ambientali, biologia e medicina)
Delivery
Formal authority
Language
English
Attendance
Obligatory
Type of examination
Written and oral (optional)
Type of learning unit
modulo
Modular course
DataScience (NEU0264)
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Sommario del corso

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Course objectives

The course aims to provide at-the edge statistical tools for exploring and analysing multimodal data. Using programming routines (R) it will introduce statistical methods for qualitatively and quantitatively testing experimental hypotheses.

 

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Results of learning outcomes

Students will be able to exploit scientific analytic methods to query large multimodal datasets. They will use data analytics skills to provide constructive guidance in decision making. They will be able to interpret the results correctly with detailed and useful information.

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Program

  • Introduction to data science
  • Programming for data analysis
    • Introduction to R
    •  Principles of reproducible computational research
    •  Pipelines and build systems
  • Advanced statistical methods
    •  Inference, hypothesis testing and multiple testing
    •  Resampling and bootstrapping
    •  Analysis of causality
  • Statistical learning and deep learning
    •  Linear regression
    •  Logistic regression
    •  Penalized regression
    •  Support vector machines, random forests, and neural networks
    •  General issues in statistical learning: bias-variance decomposition, the curse of dimensionality, overfitting, and cross-validation
    • Principal component analysis 
    •  Clustering
    •  Modern techniques for dimensionality reduction and clustering
    • Graph theory and  network science
    • Supervised learning: regression and classification
    • Unsupervised learning and dimensional reduction
  • Data harmonization
    • Introduction to Big Data analysis
    • Meta and Mega analysis techniques
    • ComBAT method
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Course delivery

Classroom lectures and practical sessions in the computer room.

Lectures 4/10/22 - 14/10/22: Data Sciences will be held instead of Statistics

Lectures 18/10/22 - 23/11/22: Teacher Ilaria Stura, WebEx link https://unito.webex.com/meet/ilaria.stura

Lectures 29/11/22 - 7/12/22: Teacher Fabrizio Pizzagalli, WebEx link https://unito.webex.com/meet/fabrizio.pizzagalli

Lectures 13/12/22 - 21/12/22: Teacher Galina Momcheva

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Learning assessment methods

Written test; optional oral test.

Suggested readings and bibliography

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An Introduction to Statistical Learning with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani



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Last update: 04/10/2022 11:51
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