As data scientists, one of the most important elements of our tech stack is the system we choose to connect to our working environment in order to manage our data. A Data Management System is responsible for managing and organizing large volumes of data throughout the organization lifecycle. It encompasses not only the software but…
Category: Data Analytics
From measuring performance to predicting outcomes, how can we design the models, metrics, and methods that turn raw data into business advantage. This section explores the analytical engine behind decisions like scoring systems, financial diagnostics, clustering methods, predictive models, and the architecture that makes it all run.
Data Science Tech Stack Series: Data Storage
Data storage forms the foundation for storing, managing, and processing data. In this post, we will explore two different approaches for storing data including on-premises storage solutions (1) and cloud-based storage solutions (2). 1. On-Premises Storage On-premises storage, also known as local storage, refers to storing data on servers located within an organization’s premises. Users…
Data Science Tech Stack Series: Working Environments
When performing Data Science, it is crucial to choose the right working environment. Our goal is to optimize our workflow efficiency during the various stages of data wrangling, coding, collaboration, and results interpretation. In this article, we will explore three types of environments with a progressive set of features: code editors (1), integrated development environments…
Data Science Tech Stack Series: Operating Systems and Interfaces
To start our Data Science journey, we need to select the appropriate hardware and Operating System (1) to manage it as well as the software on the computer. Then, we are offered with two ways to interact with the computer commands, a Command-Line Interface (2) or a Graphical User Interface (3). 1. Operating Systems (OS)…
Data Science Tech Stack Series: Languages, Libraries and Frameworks
Data science requires proficiency in one or more programming languages, depending on the task at hand (1). These languages can be connected together. To save time while programming, we can use libraries or frameworks. They are collections of pre-written code. We can import them into our code, allowing us to easily use their functions and…
The Data Analytics Lifecycle: From Exploration to Prescription
Let’s embark on an searching quest, akin to finding ‘Dollar Waldo’ in the expansive world of data. Just as Waldo blends into the crowd, waiting to be discovered, valuable actionable insights often hide within vast datasets, requiring a keen eye and systematic approach to be unearthed. In this post, we will embark on a journey…