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.
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Demystifying Data Science Careers
The Limitations of Spreadsheets in a Data-Driven Financial World
The Finance sector is a data behemoth, with an estimated 150 zettabytes of data to be analyzed by 2025 according to IBM.
Statistics: The Foundation of Data Science
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...
Working with Tree Structures in Megaladata
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...
Megaladata Community Edition — Analytics Available to Everyone
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...
Data-Driven Decisions Made Easy: The Megaladata and AltMacros Partnership
This strategic partnership will leverage Megaladata's advanced analytical capabilities to enhance AltMacros' pre-built solutions, delivering even faster results and deeper insights to businesses. Expect...
Megaladata - Analysis Beyond Excel Capabilities
Excel is a software product that needs no introduction. However, despite its popularity, it has several limitations that create difficulties in solving complex analytical problems. In this article, we will...
Low-Code Philosophy as a Reason for Myth Making
Is low-code development truly a viable alternative or is it just a fad? Debunking five most common myths about low-code development and software.
Solving typical Excel problems using Megaladata
Excel is a software product that needs no introduction. However, despite its popularity, it has several limitations that create difficulties in solving complex analytical problems. In this article, we will...
What’s New in Megaladata 7.2
We have added a built-in Job Scheduler and a new OpenID authentication method employing Access Token. The list of data trees-related components was enhanced with the Calculator (Tree) component. Several...
Low-Code Analytics at Top Speed
The Megaladata platform employs methods that ensure high-speed processing, efficient memory use, and easy scaling. Let's take a deeper look at what enables the platform's high performance and how...
Megaladata: What's under the hood?
The article describes the technological basis of the low code Megaladata platform — its components, architecture, frontend and backend, functionality, and performance. We intend to answer the most...
Cohort Analysis: Why Is It Valuable?
Every entrepreneur striving for effective business management comes to ask themself a question: What is the income brought by one client?
Coding in Megaladata: Python vs. JavaScript
The article covers the technical aspects of using programming languages in Megaladata. We compare the two languages, JavaScript and Python, analyze their limitations, and provide guidance on how to use...
Shewhart Charts as a Tool to Control Business Processes
Every business experiences failures in business processes from time to time. Finding and eliminating the causes takes not only time but also money. It is crucial to perform real-time monitoring of the...
Advanced Analytics Platforms and BI Systems — What's the Difference?
Advanced analytics platforms and Business Intelligence systems employ different approaches when dealing with data. Although data analysis takes place in both cases, there are significant differences between...
Release notes 7.1.4
Fixed: critical error in Calculator, Megaladata Integrator issues, visual problems in the workflow area. Package installation optimized.
Missing Data Imputation
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...
Release notes 7.1.2
In the Server editions, it is now possible to connect to SAP HANA through ODBC. Fixed: Megaladata Integrator, Calculator, and some data source connections. Platform performance increased, logging...
Data leakage in machine learning
In machine learning, data leakage refers to a situation where one or more input features used during the model's learning process become unavailable when the model is applied in practice. Data leakage...
Release notes 7.1.1
Fixes made to visualizers, PostgreSQL connection, and some components. Megaladata Desktop Edition performance improved.