Scientific computing with Python

Course aims

Python is frequently used for high-performance scientific applications. It is widely used in academia and scientific projects because it is easy to write, easy to read, and performs well. The course aims to give an overview on the vast field of modern Python 3 tools and libraries (e.g., numpy and scipy modules) for scientific research.

Course content

• Why Python?
• Introduction to Python programming
• The developing work-flow: interactive environments and text editors
• Creating and manipulating numerical data with Numpy
• Data visualization and plotting with matplotlib
• Scipy: library of scientific algorithms (linear algebra, optimization, numerical integration, FFT, signal processing)
• Symbolic algebra with sympy
• Data analysis and statistics with pandas
• Machine learning with scikit-learn
• Using Fortran and C code in Python

Get prepared

It is recommended to use your own laptop, even if it will be possible to use computers in the lab. I suggest to pre-install on your machine some software, in particular:

• the Python environment (versions 3.5 and 3.6 are OK)
• all the Python modules listed in the requirements.txt file
• your preferred text editor or IDE (my favorite at the moment is PyCharm).

There are many ways to install the above, depending on your operating system. Arguably, the easiest way is to follow the instructions for the miniconda installation, (installer here). Then, install all the modules listed in the requirements.txt file with commands like

conda install numpy

Alternately, you can use the default Python package manager pip, which has a convenient command to install a bundle of modules:

pip install -r requirements.txt

Detailed schedule