Getting started with performing geographical data analysis in Python using OSMnx and Kepler.gl — Introduction Geospatial data is ubiquitous and used for many different applications across all businesses (e.g. calculating the risk of properties depending on their location, designing new architecture development, planning shipment of goods, and finding possible routes between different locations). Geospatial data is typically stored in two possible formats: Raster and Vector:

Using Darts to streamline your Python time series analysis development — Introduction Time Series Forecasting is a unique field in Machine Learning. When working with time series in fact there is an inherent time dependency between the different points in the series and therefore the different observations are highly dependent on each other. …

A review of some of the most common Spark performance problems and how to address them — Introduction Apache Spark is currently one of the most popular big data technologies used in the industry, supported by companies such as Databricks and Palantir. One of the key responsibilities of Data Engineers when using Spark, is to write highly optimized code in order to fully take advantage of Spark's distributed…

Exploring some of the key concepts associated with Spark, and what defined its success in the Big Data realm — Introduction One of the main issues that characterized the inception of the internet as we know it today was the inability to scale (e.g. being able to search large amounts of data in a short time, support a constantly varying amount of users without any downtime, etc…). This was ultimately due…

Exploring some of the different approaches which can be used in order to create a side income online. — Introduction Working as a Data Scientist for a company can be a really rewarding opportunity, with average salaries in the US reaching about $117,806 per year in 2021 [1]. …

A guide on how to use Plotly Chart Studio and Datapane to share Python plots on the web — Introduction One of the most important steps in the Data Science pipeline is Data Visualization. In fact, thanks to Data Visualization, Data Scientists can be able to quickly gather insights about the data they have available and any possible anomaly. Traditionally, Data Visualization consisted of creating static images and summary statistics…

A guide through some of the key applications of AI in order to enhance cybersecurity systems. — Introduction One of the most promising applications of Artificial Intelligence (AI) is Cybersecurity. In fact, managing the security of large distributed systems can easily become an exponentially complicated task considering all the possible different scenarios an adversarial entity could try to use to take advantage of the security infrastructure in place…

Outlining some of the most common design patterns encountered when creating successful Machine Learning solutions — Introduction Design Patterns are a set of best practices and reusable solutions to common problems. Data Science and other disciplines such as Software Development, Architecture, etc. …

An Introduction to what are Feature Stores and how can they be used in order to streamline your Machine Learning processes — Introduction Creating Machine Learning models able to perform reliably in production, can be a very difficult process. These models can in fact only be as good as the data it is used to train them. …