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Oggetto:

Python programming for data science

Oggetto:

Python programming for data science

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Academic year 2023/2024

Course ID
MAT0338
Teacher
Marco Beccuti
Year
1st year
Teaching period
First semester
Type
D.M. 270 TAF F - Other activities
Credits/Recognition
3
Course disciplinary sector (SSD)
INF/01 - informatics
Delivery
Formal authority
Language
English
Attendance
Mandatory
Type of examination
Mixed
Prerequisites
Basic knowledge in Calculus as provided by the first-year Mathematics course.
No specific computer science knowledge is required.
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Sommario del corso

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Course objectives

The aim of the course is to introduce methods, techniques and related computer science instruments for the analysis of experimental data using Python language.

 

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Results of learning outcomes

KNOWLEDGE AND UNDERSTANDING – Completing the course students will be able to:

1) use suitable descriptive and inferential statistical techniques in Python to describe and understand the phenomena being studied;

2) manage suitable Python instruments for statistical data analysis.

APPLYING KNOWLEDGE AND UNDERSTANDING – Students will perform the statistical analyses required by the problem under study by developing Python programs

MAKING JUDGEMENTS – Students will decide which statistical techniques in Python to use according to the available data sets to describe and understand the phenomena under consideration.

COMMUNICATION – The student will be able to justify the choices for the analysis to be performed and to give a synthetic description of the techniques employed and of the results obtained.

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Program

1. Introduction to Python and associated development environments

2. Numpy basics: array and vectorized computation

3. Getting started with Pandas

4. Data loading and storage in Python

5. Data wrangling: clean, transform, merge and reshape

6. Data plotting and visualization

 

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Course delivery

The course consists of  10 hours of lectures and 14 hours of laboratories. Laboratories include exclusively practical activities.

The lectures will be in presence with exceptions in accordance with university regulations

The slides presented during lectures are available to students as online materials.

Attendance of lessons is not mandatory, but highly recommended due to the necessity of learning and employing specific computer science instruments.

 

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Learning assessment methods

The exam consists of an oral test  and requires a practice exercise on Python  programming languages

 

Suggested readings and bibliography

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Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter 3rd Edition
by Wes McKinney

The teaching material used for lessons and a series of practical exercises are available on the website of the course.

 



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Class scheduleV

DaysTimeClassroom
Friday14:00 - 17:00Aula 30 - Edificio Storico (3° piano) Polo di Management ed Economia

Lessons: from 29/09/2023 to 12/01/2024

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Last update: 17/10/2023 16:22
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