Know the fundamental constructs of the Python language
Curriculum
teacher profile teaching materials
Introduction to multidimensional arrays and memory efficiency management. Vector and matrix operations, mathematical functions for numerical data processing.
2) Scientific Computing Libraries: Pandas
Fundamental data structures: Series and DataFrame. Techniques for data loading, cleaning, filtering, and transformation. Handling of missing values, aggregation operations, computation of statistical measures.
3) Scientific Computing Libraries: Matplotlib
Graphical data visualization and communication of results. Management of figure structure, axes, legends.
4) Scientific Computing Libraries: SciPy
Overview of the main modules for advanced numerical analysis. Numerical integration methods and statistical distributions.
5) Applications and Numerical Simulations
Practical implementation of scientific problems: applied matrix algebra, numerical solution of differential equations, simulation of stochastic processes.
NumPy Official Documentation (https://numpy.org/doc/stable/)
Pandas Official Documentation (https://pandas.pydata.org/docs/index.html)
Matplotlib Official Documentation (https://matplotlib.org/stable/users/index.html)
SciPy Official Documentation (https://docs.scipy.org/doc/scipy/index.html)
Programme
1) Scientific Computing Libraries: NumPyIntroduction to multidimensional arrays and memory efficiency management. Vector and matrix operations, mathematical functions for numerical data processing.
2) Scientific Computing Libraries: Pandas
Fundamental data structures: Series and DataFrame. Techniques for data loading, cleaning, filtering, and transformation. Handling of missing values, aggregation operations, computation of statistical measures.
3) Scientific Computing Libraries: Matplotlib
Graphical data visualization and communication of results. Management of figure structure, axes, legends.
4) Scientific Computing Libraries: SciPy
Overview of the main modules for advanced numerical analysis. Numerical integration methods and statistical distributions.
5) Applications and Numerical Simulations
Practical implementation of scientific problems: applied matrix algebra, numerical solution of differential equations, simulation of stochastic processes.
Prerequisiti
A good command of basic Python syntax, data structures, and control flow is required. Familiarity with fundamental mathematical concepts and the main concepts of linear algebra (vectors and matrices) is recommended.Core Documentation
Jake VanderPlas, "Python Data Science Handbook". O'ReillyNumPy Official Documentation (https://numpy.org/doc/stable/)
Pandas Official Documentation (https://pandas.pydata.org/docs/index.html)
Matplotlib Official Documentation (https://matplotlib.org/stable/users/index.html)
SciPy Official Documentation (https://docs.scipy.org/doc/scipy/index.html)
Attendance
OptionalType of evaluation
The exam consists of two parts, both assessed during the oral examination: (i) the presentation and discussion of a project developed using the main Python libraries for scientific computing and programming introduced during the course, and applied to scientific problems and case studies; (ii) an oral examination aimed at assessing the knowledge acquired. The final grade is determined as the arithmetic mean of the grades obtained in the two parts of the exam. teacher profile teaching materials
Introduction to multidimensional arrays and memory efficiency management. Vector and matrix operations, mathematical functions for numerical data processing.
2) Scientific Computing Libraries: Pandas
Fundamental data structures: Series and DataFrame. Techniques for data loading, cleaning, filtering, and transformation. Handling of missing values, aggregation operations, computation of statistical measures.
3) Scientific Computing Libraries: Matplotlib
Graphical data visualization and communication of results. Management of figure structure, axes, legends.
4) Scientific Computing Libraries: SciPy
Overview of the main modules for advanced numerical analysis. Numerical integration methods and statistical distributions.
5) Applications and Numerical Simulations
Practical implementation of scientific problems: applied matrix algebra, numerical solution of differential equations, simulation of stochastic processes.
NumPy Official Documentation (https://numpy.org/doc/stable/)
Pandas Official Documentation (https://pandas.pydata.org/docs/index.html)
Matplotlib Official Documentation (https://matplotlib.org/stable/users/index.html)
SciPy Official Documentation (https://docs.scipy.org/doc/scipy/index.html)
Mutuazione: 20411064-2 IN401 - MODULO B: TECNICHE DI PROGRAMMAZIONE SCIENTIFICA in Matematica LM-40 R Ravoni Alessandro
Programme
1) Scientific Computing Libraries: NumPyIntroduction to multidimensional arrays and memory efficiency management. Vector and matrix operations, mathematical functions for numerical data processing.
2) Scientific Computing Libraries: Pandas
Fundamental data structures: Series and DataFrame. Techniques for data loading, cleaning, filtering, and transformation. Handling of missing values, aggregation operations, computation of statistical measures.
3) Scientific Computing Libraries: Matplotlib
Graphical data visualization and communication of results. Management of figure structure, axes, legends.
4) Scientific Computing Libraries: SciPy
Overview of the main modules for advanced numerical analysis. Numerical integration methods and statistical distributions.
5) Applications and Numerical Simulations
Practical implementation of scientific problems: applied matrix algebra, numerical solution of differential equations, simulation of stochastic processes.
Prerequisiti
A good command of basic Python syntax, data structures, and control flow is required. Familiarity with fundamental mathematical concepts and the main concepts of linear algebra (vectors and matrices) is recommended.Core Documentation
Jake VanderPlas, "Python Data Science Handbook". O'ReillyNumPy Official Documentation (https://numpy.org/doc/stable/)
Pandas Official Documentation (https://pandas.pydata.org/docs/index.html)
Matplotlib Official Documentation (https://matplotlib.org/stable/users/index.html)
SciPy Official Documentation (https://docs.scipy.org/doc/scipy/index.html)
Attendance
OptionalType of evaluation
The exam consists of two parts, both assessed during the oral examination: (i) the presentation and discussion of a project developed using the main Python libraries for scientific computing and programming introduced during the course, and applied to scientific problems and case studies; (ii) an oral examination aimed at assessing the knowledge acquired. The final grade is determined as the arithmetic mean of the grades obtained in the two parts of the exam. teacher profile teaching materials
Introduction to multidimensional arrays and memory efficiency management. Vector and matrix operations, mathematical functions for numerical data processing.
2) Scientific Computing Libraries: Pandas
Fundamental data structures: Series and DataFrame. Techniques for data loading, cleaning, filtering, and transformation. Handling of missing values, aggregation operations, computation of statistical measures.
3) Scientific Computing Libraries: Matplotlib
Graphical data visualization and communication of results. Management of figure structure, axes, legends.
4) Scientific Computing Libraries: SciPy
Overview of the main modules for advanced numerical analysis. Numerical integration methods and statistical distributions.
5) Applications and Numerical Simulations
Practical implementation of scientific problems: applied matrix algebra, numerical solution of differential equations, simulation of stochastic processes.
NumPy Official Documentation (https://numpy.org/doc/stable/)
Pandas Official Documentation (https://pandas.pydata.org/docs/index.html)
Matplotlib Official Documentation (https://matplotlib.org/stable/users/index.html)
SciPy Official Documentation (https://docs.scipy.org/doc/scipy/index.html)
Mutuazione: 20411064-2 IN401 - MODULO B: TECNICHE DI PROGRAMMAZIONE SCIENTIFICA in Matematica LM-40 R Ravoni Alessandro
Programme
1) Scientific Computing Libraries: NumPyIntroduction to multidimensional arrays and memory efficiency management. Vector and matrix operations, mathematical functions for numerical data processing.
2) Scientific Computing Libraries: Pandas
Fundamental data structures: Series and DataFrame. Techniques for data loading, cleaning, filtering, and transformation. Handling of missing values, aggregation operations, computation of statistical measures.
3) Scientific Computing Libraries: Matplotlib
Graphical data visualization and communication of results. Management of figure structure, axes, legends.
4) Scientific Computing Libraries: SciPy
Overview of the main modules for advanced numerical analysis. Numerical integration methods and statistical distributions.
5) Applications and Numerical Simulations
Practical implementation of scientific problems: applied matrix algebra, numerical solution of differential equations, simulation of stochastic processes.
Prerequisiti
A good command of basic Python syntax, data structures, and control flow is required. Familiarity with fundamental mathematical concepts and the main concepts of linear algebra (vectors and matrices) is recommended.Core Documentation
Jake VanderPlas, "Python Data Science Handbook". O'ReillyNumPy Official Documentation (https://numpy.org/doc/stable/)
Pandas Official Documentation (https://pandas.pydata.org/docs/index.html)
Matplotlib Official Documentation (https://matplotlib.org/stable/users/index.html)
SciPy Official Documentation (https://docs.scipy.org/doc/scipy/index.html)
Attendance
OptionalType of evaluation
The exam consists of two parts, both assessed during the oral examination: (i) the presentation and discussion of a project developed using the main Python libraries for scientific computing and programming introduced during the course, and applied to scientific problems and case studies; (ii) an oral examination aimed at assessing the knowledge acquired. The final grade is determined as the arithmetic mean of the grades obtained in the two parts of the exam. teacher profile teaching materials
Introduction to multidimensional arrays and memory efficiency management. Vector and matrix operations, mathematical functions for numerical data processing.
2) Scientific Computing Libraries: Pandas
Fundamental data structures: Series and DataFrame. Techniques for data loading, cleaning, filtering, and transformation. Handling of missing values, aggregation operations, computation of statistical measures.
3) Scientific Computing Libraries: Matplotlib
Graphical data visualization and communication of results. Management of figure structure, axes, legends.
4) Scientific Computing Libraries: SciPy
Overview of the main modules for advanced numerical analysis. Numerical integration methods and statistical distributions.
5) Applications and Numerical Simulations
Practical implementation of scientific problems: applied matrix algebra, numerical solution of differential equations, simulation of stochastic processes.
NumPy Official Documentation (https://numpy.org/doc/stable/)
Pandas Official Documentation (https://pandas.pydata.org/docs/index.html)
Matplotlib Official Documentation (https://matplotlib.org/stable/users/index.html)
SciPy Official Documentation (https://docs.scipy.org/doc/scipy/index.html)
Mutuazione: 20411064-2 IN401 - MODULO B: TECNICHE DI PROGRAMMAZIONE SCIENTIFICA in Matematica LM-40 R Ravoni Alessandro
Programme
1) Scientific Computing Libraries: NumPyIntroduction to multidimensional arrays and memory efficiency management. Vector and matrix operations, mathematical functions for numerical data processing.
2) Scientific Computing Libraries: Pandas
Fundamental data structures: Series and DataFrame. Techniques for data loading, cleaning, filtering, and transformation. Handling of missing values, aggregation operations, computation of statistical measures.
3) Scientific Computing Libraries: Matplotlib
Graphical data visualization and communication of results. Management of figure structure, axes, legends.
4) Scientific Computing Libraries: SciPy
Overview of the main modules for advanced numerical analysis. Numerical integration methods and statistical distributions.
5) Applications and Numerical Simulations
Practical implementation of scientific problems: applied matrix algebra, numerical solution of differential equations, simulation of stochastic processes.
Prerequisiti
A good command of basic Python syntax, data structures, and control flow is required. Familiarity with fundamental mathematical concepts and the main concepts of linear algebra (vectors and matrices) is recommended.Core Documentation
Jake VanderPlas, "Python Data Science Handbook". O'ReillyNumPy Official Documentation (https://numpy.org/doc/stable/)
Pandas Official Documentation (https://pandas.pydata.org/docs/index.html)
Matplotlib Official Documentation (https://matplotlib.org/stable/users/index.html)
SciPy Official Documentation (https://docs.scipy.org/doc/scipy/index.html)
Attendance
OptionalType of evaluation
The exam consists of two parts, both assessed during the oral examination: (i) the presentation and discussion of a project developed using the main Python libraries for scientific computing and programming introduced during the course, and applied to scientific problems and case studies; (ii) an oral examination aimed at assessing the knowledge acquired. The final grade is determined as the arithmetic mean of the grades obtained in the two parts of the exam.