data science

data science

Advancements in Data Science: Techniques, Applications, and Impact

1. Introduction to Data Science

In this chapter, we briefly summarize the journey of data science development, as well as glance at the current status of its techniques, applications, and impacts.

For example, data science not only accommodates a broad class of problems targeted at those enduring challenges within different specific research areas but also includes multiple but related technologies such as scalable computation, data modeling, machine learning, and optimization. While various methodologies have been developed over the past decade from statistics and engineering, there is an ongoing demand for adapting traditional approaches dealing with simple and small data to handle complex and massive data.

At a fundamental level, data science is a cross-disciplinary research area, situated at the interface of mathematical and computational sciences, statistics, as well as science and engineering disciplines, such as physical and life sciences, business, and social sciences. As a result, it necessitates various interdisciplinary research efforts.

In order to support a large and broad group of stakeholders in different fields and at different development stages, data science must rely on a synergistic combination of techniques and methodologies from different research areas.

There has been an unprecedented increase in the sizes and complexity of datasets being collected and analyzed in the scientific, business, and other domains. As easy access to data and faster computation have become more readily available, data science has become increasingly important. It also leads to rapid technological advancements and has several critical impacts on our society.

The term “Data Science” was first mentioned by William S. Cleveland in a 2001 report to the National Research Council of the National Academies. However, the field of statistics has highly influenced many new techniques that have been developed for data science. It has its roots in the field of statistics, data analysis, and machine learning, as well as in the application domain to interpret results and solve problems.

2. Key Techniques and Algorithms in Data Science

The development of huge data resources poses many challenging problems for data science with respect to the technology infrastructure that stores, processes, and retrieves this data. A range of algorithms designed to handle large-scale data has been developed from the sub-fields of machine learning, statistical data analysis, and information retrieval. The development of these is not based on ad hoc procedures, but rather rely on the theoretical foundation of applied mathematics. Due to the massive scale of available data in recent years, the high-performance computing platforms developed for these problems have also undergone rapid development, resulting in a large number of high-efficiency implementations of these algorithms. These implementations, in turn, have led to high-efficiency and high-performance software systems. Some machine learning algorithms require the efficiency provided by this infrastructure.

Efficient Algorithms for Large-Scale Data Processing

Data science views information as the primary form of competitive advantage that cutting-edge data each can bring to the tremendous success of business and society at large. Within the scope of data science and according to data science’s procedures, there exist several essential techniques that are among the facilities in data scientist’s toolboxes: 1) methods for classification and regression, 2) clustering methods, 3) dimension reduction methods, 4) association rule analysis algorithms, and finally, 5) methods for outlier detection.

Data Science-Based Machine Learning Techniques and Approaches

In recent years, as the core of data science, machine learning has undergone rapid development, mainly due to its combination of applied mathematics with skills concerning large-scale, high-performance computation. Here, we provide an overview of several key techniques and algorithms in machine learning. These algorithms and techniques constitute the necessary foundation of modern data science technology tools.

3. Applications of Data Science in Various Industries

The advancement and rapid increase of digital data promise to revolutionize the way clinicians measure and develop insights into the mind and offer new opportunities in neuroimaging for early diagnosis and treatment in psychiatric and neurodegenerative diseases. Neuroscience has always been data-rich and is often considered a big data science. The largest-scale current efforts to map brain functions present big opportunities for neuroimaging and data science techniques. Researchers generated massive amounts of neuroimaging data and relied on computational methods for processing, analysis, and visualization to extract information. Data science also exerts a big impact on connectomics, the comprehensive mapping of neural connections in an organism, and other fields of neuroscience and medicine, like neurogenetics, neural network modeling, and pharmaceuticals.

Advanced data science techniques can be used to understand and manage the continuously increasing volume, velocity, and variety of these data, but have challenges of their own. Large-scale medical diagnostic sharing from various sources presents a big opportunity for data science to improve its social and economic impact. With the abundance of available image data and sophisticated computational tools, computer vision techniques have become progressively important in assisting dermatologists in predictive modeling, clinical decision support, and diagnostic procedures. In this chapter, we will review computer vision techniques such as deep learning and convolutional neural networks (CNN), and summarize the applications and current impact of vision technology in medicine, such as image classification, pixel-level semantic segmentation, object detection, and instance-level semantics.

Data science is no longer just an industry buzzword. It has become an essential technology to solve complex problems in different domains. Data science is a combination of data analysis, machine learning, statistics, and related methods. It extracts knowledge, formulates the actual problem, and verifies unexpected results. In this chapter, we investigate data science techniques and applications that are currently used in many domains, including computer vision, biology, and medical diagnostics. Data science has become progressively essential in many important scientific studies through recent advancements in genomics and proteomics applications. By integrating various types of data, such as expression and other molecular state measurements, interactions, activities, and environmental conditions, we can achieve a comprehensive picture of cells, organisms, and even ecosystems.

4. Ethical Considerations and Impact of Data Science

All industries are susceptible to hacking attacks, but sectors that hold data concentrated in few resources tend to be at the greatest risk. In 2015, personal and credit information from 4 million current and former employees from the US federal government, including social security numbers, was accessed by an unauthorized intruder. However, the most updated and alarming case of data theft and espionage took place in 2017 when the WannaCry ransomware attack targeted computers through the Microsoft Windows operating system. The most significant consequence took place when the United Kingdom was forced to cancel scheduled surgeries and emergency patients in several hospitals were turned away. These events make it clear that one has to be cautious not to depend too much on data-driven decisions, as if on a fly-by-wire plane and its pilots. We tried to emphasize that every gain has associated risks and limitations, but the importance of studying and understanding data science is not diminished. We are not facing a dilemma of morality. It is not a choice between good or bad outcome, but instead we seek to comprehend the potential gains and losses that come along with data-driven advancements.

In many countries, the data representing citizens is a public property, yet several examples of misuse or mismanagement of data put into question the initial objective and public-interest reasoning behind data availability. One of the first examples of the use of data for the common good is the London death map of the cholera outbreaks in 1854 by Dr. John Snow. Nowadays, even though we are able to tackle complex issues with new data management tools, datafication presents significant hurdles as it affects not only personal privacy but also national and international security. Governments, organizations, and companies handle large amounts of citizen data but only a few have experienced legal or economic penalties for data security failures.

5. Future Trends and Innovations in Data Science

This chapter presents a general overview of future trends in data science, including challenges, opportunities, and innovative projects, and offers recommendations on how to shape a future world where data can be empowering and not dystopian. For the purpose of this exercise, we draw from the insights and interpretations about AI Ethics, AI Policy and Future of Work policy tracks that emerged from the AI4EU project, and enrich it with additional analyses found in technology and digital policy literature. In short, this chapter connects work on the future trends of AI, big data, and the general data economy, while also taking the spread of engine chatbots into account. The next sections explain these concepts and provide some concrete examples.

This chapter generalizes the findings presented in the previous sections and derives some conclusions on the key trends and innovations that are likely to shape future developments in the data science area. Important highlights are that, despite well-justified concerns about the “big brother is watching” 1984 future, data science has enormous potential for benefiting humankind and the planet. Data science applications are growing at a fast pace, particularly in fields that need to find solutions to highly complex problems, such as environmental sustainability or medicine. Making the most of such potential will require an equally ambitious effort in upskilling in data literacy, spreading it around cities and villages, and integrating it into the decision-making processes of society and public administration.