## Welcome to Python Data Science: Task Views

Task Views is a collaborative open source project that aims to provide guidance on which python packages are relevant for which data science task.

## Python versus R versus Julia overview

While Task Views are dedicated exclusively to Python data science tools, the project is inspired by the R ecosystem CRAN views. A side-by-side comparison with R and Julia packages available for data science helps identify important sub-domains where Python may currently lag. The overview is available in two formats:

- As a wiki page: Jupyter Overview Wiki.
- As a markdown document: Jupyter Overview Markdown

Task views aim to outline which packages could be included (or excluded) in a certain project to achieve certain
functionality. They are *not* meant to endorse the “best” package for any given task.

## Moderators

Anybody who has good knowledge of python data science tools used in a specific domain is welcome to contribute to the knowledge base. Similar to CRAN, we want to have a small number of moderators per topic to help organize the content and ensure it is a high quality resource that adds value to all users.

## How Does it Work?

Each Task View lives in a separate file hosted in this repository. Those files are automatically displayed in a Github Pages website

## Task Views

The initial proposed list of python task views mirrors the CRAN set. Over time this may develop to reflect more accurately the grouping of tasks in the Python universe

- Analysis of GeoSpatial Data
- Analysis of Pharmacokinetic Data
- Analysis of Ecological and Environmental Data
- Bayesian Inference
- Chemometrics and Computational Physics
- Clinical Trial Design, Monitoring, and Analysis
- Cluster Analysis & Finite Mixture Models
- Databases
- Differential Equations
- Econometrics
- Design of Experiments (DoE) & Analysis of Experimental Data
- Extreme Value Analysis
- Empirical Finance
- Functional Data Analysis
- General Statistics
- Graphical Models in Python
- Handling and Analyzing Spatio-Temporal Data
- High-Performance and Parallel Computing with Python
- Hydrological Data and Modeling
- Machine Learning & Statistical Learning
- Medical Image Analysis
- Meta-Analysis
- Missing Data
- Model Deployment with Python
- Natural Language Processing
- Numerical Mathematics
- Official Statistics & Survey Methodology
- Optimization and Mathematical Programming
- Phylogenetics, Especially Comparative Methods
- Probability Distributions
- Psychometric Models and Methods
- Regression Methods
- Reproducible Research
- Robust Statistical Methods
- Semantic Data
- Statistics for the Social Sciences
- Statistical Genetics
- Survival Analysis
- Teaching Statistics
- Time Series Analysis
- Visualization
- Web Technologies and Services