Tag: data science

In our previous article, we discussed probability sampling, a method where samples are selected randomly, giving each population member an equal chance of inclusion. This article will explore nonprobability sampling, a technique where samples are chosen based on criteria other than random chance. Before delving into nonprobability sampling, let&#03...

Sampling is a fundamental process in data analysis. It involves selecting a subset of individuals from a larger population to study. By analyzing this sample, researchers can draw reliable conclusions about the structural and statistical properties of the entire population. In this article, we will look into the classification of sampling technique...

The world is awash in data, yet we struggle to fully capitalize on its potential due to a severe shortage of skilled professionals. A significant mismatch persists between the demand for data scientists and analysts and the available talent pool.

Databases are one of the most popular sources of information in analytical projects. Megaladata supports work with various DBMS. This article covers all stages of work with them: connection, import, and export.

In this article, we'll explore the rising prominence of data science. As businesses increasingly rely on data to drive informed decisions, the demand for skilled data scientists continues to grow. Technological advancements and a robust job market are contributing to the growing popularity of this exciting field.

Empty values ​​in data can create serious difficulties in processing. Using the NULL value allows you to avoid the uncertainty that arises when operating with them. Let's consider how to correctly process sets of such data.

Among the new capabilities of the Megaladata platform, we would like to highlight the two strategies of workflow design: "upwards", from data to models, and "downwards", from models to data. This combination enables a more flexible modeling process, even allowing for the creation of analytical workflows without requiring data up...

In this article, we will provide an overview of three different roles and job titles associated with analytics and data science. We will first look at the evolution of the role of data scientist.