Minimalist Data Wrangling with Python
Minimalist Data Wrangling with Python is envisaged as a student's first introduction to data science, providing a high-level overview as well as discussing key concepts in detail. We explore methods for cleaning data gathered from different sources, transforming, selecting, and extracting features, performing exploratory data analysis and dimensionality reduction, identifying naturally occurring data clusters, modelling patterns in data, comparing data between groups, and reporting the results.
This textbook is a non-profit project. Its online and PDF versions are freely available at https://datawranglingpy.gagolewski.com/.
A printed version (the same as the aforementioned PDF one) can be ordered from Amazon.
Dr Marek Gagolewski is currently a Senior Lecturer in Applied AI at Deakin University in Melbourne, Australia and an Associate Professor in Data Science (on leave) at the Faculty of Mathematics and Information Science, Warsaw University of Technology, Poland. His research interests are related to data science, in particular: modelling complex phenomena, developing usable, general purpose algorithms, studying their analytical properties, and finding out how people use, misuse, understand, and misunderstand methods of data analysis in research, commercial, and decision making settings. In his spare time, he writes books for his students and develops free (libre) data analysis software, such as stringi – one of the most often downloaded R packages, and genieclust – a fast and robust clustering algorithm in both Python and R.
See also: Deep R Programming at https://deepr.gagolewski.com/.
- Is published in
- Presentation: https://datawranglingpy.gagolewski.com (URL)
- Is supplement to
- Software: https://github.com/gagolews/datawranglingpy/tree/v1.0.3 (URL)
- Book: 978-0-6455719-1-2 (ISBN)