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.
Blockchain is arguably one of the most significant and disruptive technologies that came into existence since the inception of the Internet. It's the core technology behind Bitcoin and other crypto-currencies that drew a lot of attention in the last few years. This blog post covers the core concepts behind blockchain and shows how to implement one using Python.
The goal of this blog post is to give you a hands-on introduction to deep learning. To do this, we will build a Cat/Dog image classifier using a deep learning algorithm called convolutional neural network. This post is divided into 2 main parts. The first part covers some core concepts behind deep learning, while the second part is structured in a hands-on tutorial format. We will use some Python code and a popular open source deep learning framework called Caffe to build the classifier.
In this blog post, I will introduce the building blocks for creating a simple IoT application. To do this, I will use an Arduino microcontroller with a photocell (light intensity sensor), a node.js server for capturing and transferring the data, and a cloud service called plotly to visualise the data. By the end of this tutorial, we will have a functioning IoT application that you can customise to other use-cases.
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.