Category: Marketing Analytics

In the dynamic world of data analysis, a Data Analyst plays a pivotal role in steering a company towards success. The primary goal of a Data Analyst is to provide actionable insights based on data to improve performance. His responsibilities span a broad spectrum, encompassing performance assessment, future performance forecasting, target development, and the shaping…

A Data Engineer (DE) is responsible for building a robust data environment by developping and maintening scalable databases, data pipelines and architectures. He focuses on the infrastructure and mechanics of data handling, ensuring that data is properly collected, stored, processed and made accessible for various analytical and operational needs. By enabling efficient data analysis, the…

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…

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…

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…

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 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 librairies 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 “now” economy focuses on immediate fulfillment of consumer demands. It is a rapidly evolving facet of modern business, mostly suported by millenials. In this economy, consumers expect goods and services to be available and delivered quickly, often in a matter of hours or even minutes. This concept has profoundly influenced various industries, including retail,…

The “sharing” economy is a socio-economic ecosystem built around the sharing of resources, such as property, services, or skills, with others for a fee or exchange. This model is facilitated by online platforms that connect providers of these resources with users who need them. The concept has radically transformed a wide range of industries, including…

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…