Tag: data analysis

In today's digitally driven business landscape, data has become an indispensable asset. The ability to effectively harness and utilize information is a key determinant of a company's success. To remain competitive, organizations must be agile and adaptable to both internal and external changes.

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...

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.

ADEAL Systems GmbH, a German vendor of IT solutions and services, and Megaladata, LLC, a data analysis solutions developer, have signed a partnership agreement.

The Calculator is one of the most popular components of the Megaladata platform. Let's look at examples of practical use of the Date/Time functions that are implemented in it.

Marketing is undergoing a revolution driven by data. Gone are the days of generic messaging. Today's empowered customers demand personalization, and businesses that harness data to understand individual needs will thrive. This article explores the power of data-driven marketing and how it can transform your business.

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.

Popular and exciting as it is, the work of a data professional consists not only of insights and revelations, but involves plenty of routine. Thankfully, more and more tools appear to automate the tasks one has to deal with from day to day, from dataset to dataset. Being a cutting-edge platform, Megaladata is happy to offer you all the special comp...

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.

Statistics are a powerful tool, but interpretation is key. Don't just look at the numbers – understand their meaning: uncover hidden insights from data, compare groups and make informed decisions, replicate findings and communicate them effectively

A tree model is one of the common structures for storing and transferring data. Universal exchange formats, such as JSON and XML, use exact hierarchical representations of information. However, most algorithms are designed to store data as flat tables. Megaladata has special components for working with tree structures.

Data is growing at an explosive rate, but your analysis tools are lagging behind. Does this sound familiar? Excel can't handle the data volumes. Data science specialists are hard to find and expensive to train. Choosing the right software feels like navigating a jungle of options. In the face of all these challenges, what can help?

Missing data is a very common issue in real-world data processing. The reasons may vary — data entry errors, information hiding, fraud, and so on. In this article, we discuss cases where incorrect handling of missing data using simple techniques leads to errors in models and decision-making.

When implementing analytical projects, it is not too efficient to try to improve the system as a whole. Let’s take a look at the theory of constraints to see how low code development helps reduce the developers’ workload, expanding the bottlenecks and significantly increasing the efficacy of data analytics.

The combined use of ABC and XYZ analysis will help you to better navigate the product range, optimize logistics and warehouse stocks, segment customers and partners, and properly set up interactions.