Learn about common types of data science projects and best practices for approaching them. From end-to-end individual work to production-ready projects, this guide covers it all.

The most widely used libraries for plotting in python are matplotlib, Plotly, seaborn, and bokeh. This article describes 10 other powerful plotting libraries available in Python that can be used to create high-quality plots and visualizations.

Using Plotly, Bokeh and Altair for interactive visualizations in the blog posts.

Using puzzle on tracing the high profile spy as excuse to showcase Markov Chains and demonstrate usage and properties e.g. Stationary distribution

Having several notebook-based projects behind you might result in a mess in the projects directory. Organize your Data Science project based on Jupyter notebooks in a way that one can navigate through it. Especially that "the one" will be most probably you in a few months time. To achieve that, keep your projects directory clean, name the project in a descriptive way and take care of the internal structure of the project.

Exploratory Data Analysis of the Kaggle's "What's cooking" competition dataset to get understanding what kind of data we are dealing with and get intuition of existing dependencies.

Python implementation of blockchain in few lines of code.