Tag: data analysis

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.