41588 - INTRODUZIONE ALL'ANALISI DEI DATI

Learning outcomes

At the end of the course, the student: (i) know the basics of Python programming language; (ii) is aware of different types of data-analytics (diagnostic, predictive, prescription, etc) and of the main enabling techniques; (iii) is able to design and data-pipeline process, from the data acquisition until the data analysis and valorization; (iv) knows the main applications of data analytics, with a special emphasis on biology.

Course contents

The course is made of two parts: a first part concentrating on Python programming, and a second part consisting of data analysis in Python.

Python programming:

• Introduction to programming, algorithms, data structures.
• The Python language: data types, control flow, data structures, functions, libraries.

Data analysis in Python:

• Types of Biological data and suitable data structures. 
• Descriptive analytics for various data types using libraries.
  
• Predictive analysis using regression and/or classification models.
• Statistical tests and model analysis to identify important variables.
• Python libraries: numpy, scipy, matplotlib, pandas, scikitlearn

Readings/Bibliography

The slides and solutions to exercises will be made available through the virtual platform.

It is recommended that you complete the exam for the class  Fondamenti di Matematica, Probabilità e Statistica before taking this class.

For further reading:

• Starting out with Python, T. Gaddis, any edition.
• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Wes McKinney, any edition.

Teaching methods

Frontal lectures, Python exercises in computer lab.

Assessment methods

Evaluation will consist of an oral exam, including data analysis exercises in Python on a given dataset.  

Use of AI tools during the exam is prohibited. 

Students with learning disorders and\or temporary or permanent disabilities: please, contact the office responsible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

 

Teaching tools

Slides will be made available, and solutions to exercises completed in class. 

A set of exercises for self practice will also be made available.

Office hours

See the website of Alina Sirbu