OT-ST-WS-04 | Getting started with Python
Dr. Nikolay Koldunov
Currently python is one of the most popular general purpose programming languages. It gains popularity due to expressive syntaxis, vibrant community and large number of high-quality open-source libraries. There are libraries for data science, all branches of natural and computer sciences as well as tools for system administration and development of web applications and backend systems. Most of the popular machine learning libraries (not covered in this course!) are written with python as an interface language (TensorFlow, Keras, PyTorch). Parallel processing libraries (e.g. dask) allow to effectively work with huge amounts of data. This makes python knowledge a desirable skill for data scientists as well as for anyone who work with data in digital form in one way or another.
In this course you will learn basics of python, that allow you to perform data handling related tasks using this programming language. The main emphasis will be given to overview and hands on experiences with most popular python libraries used for data processing and visualization. In particular we will cover numpy (array operations), pandas (table data and statistics), xarray (labeled arrays), dask (parallel data processing), scipy (scientific computing), matplotlib and bokeh (visualization). The course will be held in interactive Jupyter environment.
Course on GitHub: https://github.com/koldunovn/python_data_train
Basic understanding of python syntaxis, data types and operations. Basic information about several main python libraries used for data processing and visualization. Experience on solving simple data handling problems using those libraries. Experience of working within interactive Jupyter environment. Knowledge of python data science libraries ecosystem and understanding of where to find information.
Basic experience with programming on any language would be an advantage.
- Own PC, laptop
- Internet (access to eduroam), web browser (up-to-date)
- A software environment will be provided via the online tool Jupyther Hub; for local installation, participants will receive installation instructions prior to the workshop