In this blog post, we will be using an open collection of football logs to create a web app that analyzes Messi and Ronaldo's game during LaLiga season 2017-18. We will be using Python/Jupyter notebooks to analyze the data and Python/Streamlit to create an interactive web app that compares both players stats and shows their positions on the pitch.
In 1971, Thomas Schelling published a paper titled: Dynamic models of segregation. The paper introduced an agent-based model that helped studying segregation in multi-ethnics cities. The model showed that a high segregation level in cities doesn't necessarily translate into intolerance at the individual level. In this post, I will explain how to implement Schelling's segregation model using Python and Streamlit.
Since its emergence in Asia late 2019, the coronavirus COVID-19 pandemic has been devastating. The virus spread to most countries causing severe respiratory infections and many human casualties. The virus also put half of the world population in lockdown which resulted in a slowdown of the world economy and a fall in stock prices. The goal of this tutorial is to introduce the steps for collecting and analyzing stock data in the context of the coronavirus pandemic.
In this tutorial, I will combine Coursera course catalogue together with social media data to assess the popularity of courses. To to this, I will use the Coursera API to retrieve the course catalogue, I will use the sharecount.com API to get social media metrics for each course, and I will use python's pandas library to query and order the courses by popularity. The technique introduced in this tutorial can be leveraged to other use cases that require a popularity ranking system for measuring the relevance of a list of links.
In this post, I will explain about Schelling's segregation model, implement the same in Python programming language, and explain the power of agent based simulations for understanding complex phenomenon.
In this tutorial, I will use Twitter data to compare the popularity of 3 programming languages: Python, Javascript and Ruby, and to retrieve links to programming tutorials.
Sabermetrics is the apllication of statistical analysis to baseball data in order to measure in-game activity. In this post, I will use Lahman’s Baseball Database and Python programming language to explain some of the techniques used in Sabermetrics.