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Python programming for data science
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Python programming for data science
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Academic year 2024/2025
- 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. - Oggetto:
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 McKinneyThe teaching material used for lessons and a series of practical exercises are available on the website of the course.
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Courses that borrow this teaching
- Programming for Data Science (ONC0254)Artificial Intelligence for Biomedicine and Healthcare
- Programming for Data Science (ONC0254)
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Class schedule
Days Time Classroom Friday 14:00 - 17:00 Aula 30 - Edificio Storico (3° piano) Polo di Management ed Economia Lessons: from 27/09/2024 to 04/01/2025
- Enroll
- Open
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