Data science

DATA SCIENCE

Anastasiya Pavlenko

Faculty of Informatics and Computer Engineering

National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Every day, each of us interacts with big data, often without realizing it. Social networking algorithms, advertising offers, instant messengers, marketplaces, and familiar mobile services — all shaped by big data. By 2025, the world is projected to generate over 180 zettabytes of data. However, structured information doesn’t experience such rapid growth. Over 90% of data generated today is unstructured.[5] The bulk of the increase in information volumes comprises unstructured or semi-structured data. Consequently, due to these significant volumes, specialized hardware and software are necessary for processing and storing data. Conversely, classical data processing algorithms prove ineffective when dealing with unstructured data.

To address these issues, several of the world's largest companies in the information technology industry have initiated the development of entirely new approaches to process and store information. This endeavor aims to derive valuable insights from vast and poorly structured data, leading to the creation of a new system of tools and methods for analysis—commonly referred to as 'Big Data'.

The concept of Big Data was introduced by Clifford Lynch, a computer science doctor at the University of California, Berkeley, in 2008. The works of V. Mayer-Schönberger and Kenneth Cukier delve into fundamental research in the Big Data field. Data Science, the science of working with data, isn't merely a new buzzword in the realm of IT technologies. It's poised to revolutionize the worlds of programming, business, and consumerism much like the personal computer did in its time. In fact, Data Science is already effecting change, as evidenced by the proliferation of startups focused on artificial intelligence and big data [2, p.81].

Data Science comprises specific disciplines from various fields tasked with analyzing data and deriving optimal solutions from it. Initially, this realm was primarily within the domain of mathematical statistics. However, the integration of machine learning and artificial intelligence expanded the methodologies for data analysis beyond mathematical statistics, incorporating optimization techniques and computer science into the field.

Traditionally, computers gained new capabilities through programming — humans devised explicit operating algorithms for machines, yielding anticipated results. However, this approach is now considered outdated due to the evolution of technologies like machine learning and artificial intelligence. These advancements enable systems to learn and adapt without explicit programming, allowing them to derive solutions and make decisions based on patterns and data analysis rather than solely relying on predetermined instructions.

To work effectively with big data, another essential component is machine learning. In this scenario, a person provides the computer with inputs, yet the outcomes of such algorithms are not predetermined by a person. Humans dictate how the machine learns, but the machine itself acquires knowledge, arrives at specific conclusions, and analyzes information autonomously. This process resembles how we study. Machine learning isn't solely about artificial intelligence; it encompasses genetic and evolutionary algorithms, as well as simpler tasks like cluster analysis, for example.

Cognitive Science is an interdisciplinary field that explores the mechanisms of cognition and thought processes. The findings from this research serve as the cornerstone for various approaches in the development of artificial intelligence.

Data Science is intricately connected with Big Data. It's important to note that a substantial portion of this data originates from an extensive network of robotic devices that interact with other data networks, such as sensors and smart devices.

The rapid growth in global data volumes is expected to drive a tenfold increase in both virtual and physical servers, primarily due to the expansion and establishment of new data centers. Consequently, there's a growing imperative to efficiently leverage and monetize this data. Given that harnessing Big Data in business demands substantial investment, enhancing business efficiency becomes pivotal through cost reduction and/or increased sales volumes.