Lying With Data - Why Business Insight Matters
When using data science in a business context, we aim to make decisions based on our data. This can have several advantages. Machines can potentially take decisions faster, at higher volume, and with less errors than humans, giving organizations that master data-driven decision making a huge competitive advantage. However, not all models and data sets are created equally, and business insight can make the difference between winning and shooting yourself in the foot.
Exploring IoT - Part 1: The Hardware
In 2022 the number of connected IoT devices grew to 14.4 billion, according to IoT Analytics, a market research company. This is a mind-boggling number. IoT is here to stay and thrive, so I felt like it would be fun to get my hands dirty and try to hack an IoT environmental monitor at home. In this post, we’ll look at the IoT hardware.
Prediction Errors With Less Errors
Last time we looked at model parameters and how to estimate their errors. Today we will take things a step further and look at how to use one of the methods we learned about, bootstrapping, to estimate errors on model predictions. As I previously explained, this is essential when basing business decisions on data. Let’s dive in!
Model Errors With Less Errors
In applied AI, predictive power of a model is often secondary. When business decisions are based on the predictions of a machine learning model, confidence in the predictions is often more important than pushing model metrics as far as one can. Let me explain.
Covid-19 in Norway, Second Wave (a quick graph)
Norway, like so many other countries, is seeing a second wave of the Covid-19 outbreak. But while numbers are rising, the hospitalization rate is much lower than during the first wave in March. Why is that?
Bayesian Estimation COVID-19's Reporduciton Number in Python
On the last data adventure, we estimated the novel Coronavirus’ basic reproduction number $R_0$ using some Python scripting and basic exponential fitting. As much fun as that was, I was wondering if one could gain a more dynamic understanding of the situation. Luckily, I stumbled upon this blogpost, based on this journal article, accompanied by this notebook, which attempts estimating a dynamic version of the reproduction number, called $R_t$. The interesting idea behind $R_t$ is that it will give some indication on how well measures aimed at reducing the spread of COVID-19 work in a given country or region.
COVID-19: Estimating R_0 in Python
Yes, I know, everyone and their brother is talking about the novel coronavirus SARS-CoV-2. There is a lot of bad information and misinformation out there. For this reason I thought it would be a good idea to go on a data adventure together to have a look at the numbers (and how to get them into an easy-to-use format for you to have a look for yourself) and make our very own rough estimation of the basic reproduction number $R_0$ that people keep talking about.
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