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Programming for Data Science
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Programming for Data Science
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Academic year 2023/2024
- Course ID
- ONC0254
- Teacher
- Marco Beccuti (Lecturer)
- Year
- 1st year,
- Teaching period
- First semester
- Type
- Basic
- Credits/Recognition
- 6
- Course disciplinary sector (SSD)
- INF/01 - informatics
- Delivery
- Formal authority
- Language
- English
- Attendance
- Obligatory
- Type of examination
- Written and oral (optional)
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Sommario del corso
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Course objectives
Since this course is divided into two modules, we reported hereafter the objective for each module
1) R Programming for data science
The module aims to introduce methods, techniques, and related computer science instruments for the analysis of experimental data.
2) Python programming for data science
The module aims 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 R and Python to describe and understand the phenomena being studied;
2) manage suitable R and Python instruments for statistical data analysis.
APPLYING KNOWLEDGE AND UNDERSTANDING – Students will perform the statistical analyses required by the problem under study by developing R and Python programs
MAKING JUDGEMENTS – Students will decide which statistical techniques in R and 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
Since this course is divided into two modules, we reported hereafter the program for each module.
1) R Programming for data science
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Introduction to Data science;
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Visualization using ggplot2;
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Basic R functionalities:
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Data structures: vector, matrix, list and data frame, tibble;
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Apply operators;
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Input/output operator;
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Package and library.
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Tidy data in R
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Data Transformation;
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Programming with R:
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Function;
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Flow control: if,for, while, break ... statements;
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Debugging in R.
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Creation of package in R
2) Python programming for data science
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Introduction to Python and associated development environments
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Numpy basics: array and vectorized computation
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Getting started with Pandas
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Data loading and storage in Python
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Data wrangling: clean, transform, merge, and reshape
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Data plotting and visualization
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Course delivery
The course consists of 20 hours of lectures and 28 hours of laboratories. Laboratories include exclusively practical activities.
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 R and Python programming languages
Suggested readings and bibliography
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1) R Programming for data science
- Garrett Grolemund and Hadley Wickham, R for Data Science, O'Reilly Media, Inc, USA, 2017.
- P. Dalgaard, Introductory Statistics with R, Springer 2008
- The R Manuals: An Introduction to R (http://cran.r-project.org/doc/manuals/r-releas /Rintro.pdf)
The teaching material used for lessons and a series of practical exercises are available on the website of the course.
2) Python programming for data science
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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