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DataScience
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Data Science
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Academic year 2023/2024
- Course ID
- NEU0264
- Teachers
- Caterina Guiot (Lecturer)
Paolo Provero (Lecturer)
Ivan Molineris (Lecturer)
Fabrizio Pizzagalli (Lecturer)
Ilaria Stura (Lecturer)
Elena Grassi (Lecturer) - Year
- 1st year
- Teaching period
- First semester
- Type
- Distinctive
- Credits/Recognition
- 15
- 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
- Type of learning unit
- corso
- Prerequisites
- Mathematics at the level of elementary calculus. Basic probability and statistics. The "Mathematical Modeling" online course at https://start.unito.it/ covers most of the prerequisites.
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Sommario del corso
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Course objectives
The integrated course aims to provide different skills.
Bioinformatics:
The course aims to provide the students with the theoretical and practical knowledge necessary to understand and perform the analysis of modern high-throughput experimental assays, in particular of next generation sequencing (NGS); and to extract biologically useful and clinically actionable information from such analyses.
Programming for Data Sciences:
The course aims to introduce how to effectively operate on command line interfaces (with the linux shell) and the basic concepts of computer programming (with R). It will also explain how to structure pipelines, made from single steps implemented with either approaches. It will focus on some of the technical aspects of scientific reproducibility (package management systems). These fundamental skills will be leveraged as the starting point for the Bioinformatics lessons.
Statistics:
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
Bioinformatics:
At the end of the course the students will be able to understand the results of high-throughput biological experiments commonly used in the current literature; to perform the most common analysis tasks of NGS data; and to extract information of biological of clinical interest from the resulst of the analysis.
Programming data sciences:
At the end of the course the students will be able to independently work on the linux shell and write simple R scripts to perform basic data wrangling, selecting the most appropriate data structures to easily visualize and interpret aspects of different datasets. They will also be able to approach higher level problems dividing them in simpler steps, that can be tackled with basic programming constructs, and structuring them in small pipelines.
Statistics:
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
Please refer to the individual modules.
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Course delivery
Please refer to the individual modules.
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Learning assessment methods
For each module there will be a written test and an optional oral test. The exam for the Bioinformatics module includes also a practical test.
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Support activities
Please look at the individual modules.
Suggested readings and bibliography
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Please refer to the individual modules.
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Teaching Modules
- Bioinformatics (NEU0264B)
- Programming For Data Science (NEU0264C)
- Statistics and data analysis (NEU0264A)
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