Learning DIY skills and carrying out home renovation projects has piqued my interest in the last few months. Perhaps it’s from all of the TikTok videos I watch of people remodelling and renovating their houses. So to get in the mindset, I have decided to use Notion for home renovation and DIY projects by building…
Pandas is a powerful Python library commonly used within data science. It allows you to load and manipulate datasets from a variety of sources and is often one of the first libraries you come across in your data science journey. When working with pandas, the default options will be suitable for the majority of people. However, there may…
Pandas is a powerful and versatile Python library for data science. It is often one of the first libraries you come across when learning how to use Python for data science applications. It can be used to load data, visualise it and manipulate it to suit the objectives of the project you are working on. However,…
k-Nearest Neighbors (kNN) is a popular non-parametric supervised machine learning algorithm that can be applied to both classification and regression-based problems. It is easy to implement in Python and easy to understand which makes it a great algorithm to start learning about when you start your machine-learning journey. Within this article, we will cover how…
When working with well log measurements and subsurface data we are often dealing with different file formats and sample rates. For instance, well log measurements are typically stored and transferred within .las files or dlis files and sampled every 0.1m or 0.5ft. Geological formation tops on the other hand are single discrete depth points. This requires interpolation…
When working with subsurface data we often deal with datasets that have been sampled in different ways. For example, well log measurements are continuously recorded over intervals of the subsurface at regular increments (e.g. measurements every 0.1 m), whereas formation tops are single depth points. Within this article, we will cover a way of integrating…
Data science is a rapidly growing field with the demand for data scientists projected to grow by 36% over the next decade according to the US Bureau of Labor Statistics. However, the field can be very competitive, and there are many steps between being an entry-level data scientist and getting promoted into a senior role at a large company.
Neural Networks are a popular (mostly) supervised machine learning algorithm. They can be used for modelling a variety of complicated tasks such as image processing, fraud detection, speech processing, and more. These algorithms can be applied to regression-based problems as well as classification-based problems. Within petrophysics and geoscience, we can use Neural Networks to predict…
Folium is a powerful Python library that makes it easy to visualise geospatial data. It utilises the power of Leaflet.js, which is a popular and leading JavaScript library that can be used to create interactive maps that can be used across both desktop and mobile platforms. The library has been designed in a way that makes it…
Reducing the Impacts of Garbage In Garbage Out on Machine Learning Models Ensuring you have good data quality prior to running machine learning algorithms is a crucial step within the overall data science and machine learning workflow. The use of poor-quality data can lead to severe degradation in the results and have further consequences when…