The amount of data we need to work with, analyze, and explore; and that amount is only going to grow larger and larger as the technology around us advances. Now, imagine having to stare at thousands…
I am a data scientist and avid reader (and writer) of articles about data science and machine learning. It takes time to read journal articles, listen to podcasts, analyze interesting data, and play…
Any text can be represented as a network. The words are the nodes; the words’ co-occurrences are the connections between them. The resulting network structure can reveal how immune or susceptible the…
Shallow and deep copies are essential to understand when writing efficient code and managing a limited memory properly. Not knowing which manipulations operate on shallow and deep copies can also…
R/Shiny apps are a great way of prototyping, and visualising your results in an interactive way while also exploiting the R data science and machine learning capabilities. R/Shiny apps are easy to…
Credit Risk Management: EDA & Feature Engineering. How to clean and pre-process data using EDA and Feature Engineering techniques for Classification problems….
In this article, we will go through a classification problem that involves classifying a review as either positive or negative. The reviews used here are the reviews made by customers on a service…
In this article, I will perform text analysis of the famous speech given by Steve Jobs to find which are hist most frequently used words. However, it is not so easy as it may sound. I will explain…
In this blog, we will go through the step by step implementation of hosting Elasticsearch on a virtual machine, and how to connect and use the server using Python programming language Elasticsearch…
Find the best combination of stop loss level and parameter of Keltner Channel by turning the previous Python scripts into re-usable functions. In our last session, we added stop loss into our trading…
