Data Analysis in Python

Target Group: Master’s students, doctoral candidates, and all researchers with a doctorate

Language: English

register: Data Analysis in Python

This workshop takes place in cooperation with the GMZ. It shows how to do basic data analysis in Python using Pandas and related packages. Topics covered include:

• Data science packages overview
• IPython
• NumPy basics
• Importing data
• Basic data manipulation with Pandas
• Plotting with Matplotlib and Seaborn

Students are expected to be familiar with basic Python concepts such as package management, data types, control flow constructs (conditions and loops), and functions. A working Python environment (for example as provided by Anaconda) is necessary to follow along in the workshop. Using many examples, this workshop will introduce the tools to implement a basic data analysis workflow in Python. This includes working with IPython, importing data from various text file formats, working with NumPy arrays and Pandas data frames, and creating visualizations of specific aspects of the data.

This workshop is based on selected chapters of the books "Python for Data Analysis” by Wes McKinney and “Python Data Science Handbook” by Jake VanderPlas.

Trainer:

Clemens Brunner is a senior postdoc with a background in electrical/biomedical engineering and computer engineering. He works at the Educational Neuroscience group at the Institute of Psychology, University of Graz, Austria. His research interests include neurophysiological substrates of number processing and arithmetic, EEG oscillations and connectivity analysis, biomedical signal processing, applied machine learning and statistics, brain-computer interfaces, and software development. He is a strong proponent of open-source software and believes that science should be open as well, including data and analysis scripts. Python is his favorite language, but he also enjoys performing data analysis and statistical modeling with R (and he is also interested in Julia). He maintains and develops MNELAB (a graphical user interface for processing EEG/MEG data using MNE), the Qt/C++ based biosignal visualization tool SigViewer, SCoT (a Python package for EEG-based source connectivity estimation), and XDF.jl (a Julia package for reading XDF files). He is part of the MNE and pyXDF development teams and has contributed to scikit-learn, pandas, Matplotlib, PsychoPy, pybv, and BioSig. More information is available on his website at cbrnr.github.io.

Registration & Fees: 

Please log in on the left-hand side on the homepage under ‘Login’ before registering for the course.

• Students and staff: €20
• External participants: €100