The Python Basics course is intended for participants who want to learn basic Python skills as well as
efficient handling of data preparation, data processing and data analysis in Python. In addition, general
“best practices” in Python will be taught, including, writing simple, readable and modularly extensible
code. All topics presented will be explained, demonstrated, and practiced in detail with the help of
participant exercises under intensive instruction.
- Introduction to the basics of Python
- Installation and use of Python and useful Python modules
- Creating and working with virtual environments
- Explanation of the most important data types, operators, functions and help pages
- Introduction to NumPy and Pandas
- Importing and exporting data
- Working with DataFrames and vectors (numeric, logical, character, factors), e.g. indexing,
splitting and transforming variables or data sets
- Calculating statistical ratios (e.g.: mean, quantiles, variance, etc.)
- Review of Python basics: built-in structures, numpy, IPython, jupyter notebook, package
- Series and DataFrames: generation, meaning of line index, filtering, pointer vs. copy
- Data cleansing: Handling missing values, editing strings, removing duplicates
- Transforming data by vectorized operations like map or apply
- Merging different data sources and creating a "good" table structure of the data
- Grouping of data and aggregations: Split-Apply-Combine
This course is suitable for participants with no knowledge of Python or to refresh the basics in Python. The workshop is very hands-on and thus limited to max. 15 participants.
Use a laptop/PC with reliable internet access and install the following software:
ABOUT THE TRAINER
Dr. Matthias Assenmacher works as trainer for Data Science Essential GmbH and is postdoctoral researcher at the Chair of Statistical Learning and Data Science (LMU) and the
NFDI Consortium for Business, Economic and Related Data (BERD@NFDI). He obtained his bachelor’s degree in Economics from LMU in 2014, afterwards I turned to Statistics (with a focus on
social and economic studies) and obtained his Master’s degree in
2017 (also from LMU). In October 2021 he finished his PhD with a focus on Natural Language Processing.
His expertise revolves around the practical application of state-of-the-art NLP architectures to real-world problems from various disciplines, as well as open and reproducible science.