Data Analytics blog category.

Dimensionality Reduction Algorithms in Data Analysis

In the modern landscape of big data, datasets often contain hundreds or even thousands of variables, making them complex and costly to analyze. Dimensionality reduction is a powerful technique that simplifies these datasets by reducing the number of variables while preserving essential information. This process not only makes data easier to visualize and interpret but also improves the performance of machine learning algorithms by reducing noise and redundancy. 
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Algorithms in Data Analysis

Data analysis is a cornerstone of modern decision-making, and algorithms are the tools that make it possible. Without algorithms, analyzing the sheer volume of data generated daily would be an impossible task. From processing raw datasets to identifying hidden patterns, algorithms enable you to interpret data efficiently. 
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Big Data in Government Projects

In today’s digital world, big data has become an invaluable resource for government agencies. By processing and analyzing vast datasets, governments can make informed decisions, improve public services, and tackle complex challenges. From urban planning and public health to national security and economic policy, big data is transforming how agencies operate and deliver value. 
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The Practical Power of Graphs

When most people think about graphs, they often imagine simple diagrams from a math class, like bar graphs or pie charts. However, in the world of computer science and mathematics, "graph" refers to a structure of nodes (vertices) connected by edges. This concept, studied in graph theory, serves as the foundation for solving complex problems in many diverse industries, such as transportation, telecommunications, social networks, and bioinformatics.
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Time Complexity and Efficiency in Software Engineering 

In the realm of software engineering, efficiency isn't just a goal—it's a necessity. As applications become more complex and data grows exponentially, the demand for efficient algorithms that can handle large-scale operations efficiently becomes crucial. One of the fundamental concepts associated with this need is time complexity, a theoretical measure of the execution time required by an algorithm as a function of the length of the input. 

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Where Does the Money Come From When User Data is Harvested?

As Antonio García Martínez writes for Wired, user data is often referred to as the “new oil” due to its immense value in the digital world. Understanding how data becomes valuable is key for our operations at Onyx, and for parsing this metaphor. Read more from this interesting article to see why this characterization is not as apt as it may seem and gain a more nuanced understanding of how your information makes money for tech giants.

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Data Analytics and the Government: What Could Be Gained?

As data analytics abilities become more refined, parties in the public and private sectors alike have much to gain from using such tools. Recent research carried out by Harvard Kennedy School’s Ash Center for Democratic Governance and Innovation explores the benefits of data analytics implementation and reasons for which the field is swiftly becoming a key area of interest and investment for government leaders. Find out more about recent research into the benefits of data analytics.

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