1. Introduction

Data science is a multifaceted discipline that encompasses data engineering, data analytics, data protection, and data ethics, which collectively form the foundational pillars of the field (Tamer Özsu, 2023). While data analysis is often the focal point, the scope of data science extends to the integration of various technologies, tools, algorithms, and methodologies to address complex challenges across different application domains (Rossi, 2022). Moreover, the deployment of data science applications is heavily influenced by the existing social and policy context, which shapes both the core technologies and the application deployments.

The significance of data in data science cannot be overstated, especially in the context of "big data" and the associated challenges in managing, processing, and analyzing such large and diverse datasets. This necessitates the development of new systems, methodologies, and approaches tailored to the unique characteristics of big data, thus highlighting the evolving nature of data science as it continues to adapt to the demands of modern decision making.

2. Foundations of Data Science

Foundations of data science encompass a wide array of interdisciplinary principles and concepts, extending beyond mere data analysis. As (Tamer Özsu, 2023) highlights, data science draws from various capabilities such as data engineering, data analytics, data protection, and data ethics. It involves the management of big data, which differs significantly from traditional data management systems, necessitating new methodologies and systems. Additionally, data science platforms must grapple with multi-modal data from diverse sources, posing a challenge for unified access and integration. Furthermore, (Cao, 2020) emphasizes that data science is a trans-disciplinary field that synthesizes relevant disciplines like statistics, informatics, computing, communication, management, and sociology. This underscores the systematic and inter-disciplinary nature of data science, positioning it as the next-generation of statistics, extending beyond data mining and machine learning.

3. Data Collection and Preprocessing

Data collection and preprocessing are crucial stages in the data science process, significantly influencing the success of artificial intelligence applications. According to Lim et al. (C. Siang et al., 2022) , acquiring and preparing data is a significant portion of the effort in advanced process control, process analytics, and machine learning. The authors emphasize the importance of best practices for acquiring and preparing operating data, particularly in industrial processes, to pursue data-driven modeling and control opportunities. Additionally, Yedla and Dorius (Yedla & Dorius, 2017) highlight the significance of data collection and data cleaning/curation in the initial stages of the data pipeline. Their literature review identifies salient data science terms and emphasizes the importance of the collection, cleaning, and curation of social scientific data in research activities. These insights underscore the critical role of meticulous data collection and preprocessing in the data science workflow.

4. Data Analysis and Modeling

Data analysis and modeling are fundamental components of data science, playing a crucial role in extracting valuable insights and making predictions based on data. In the context of data science, data analysis involves the process of inspecting, cleansing, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making (Tamer Özsu, 2023). This process often encompasses exploratory data analysis, descriptive statistics, and inferential statistics, allowing data scientists to identify patterns, trends, and relationships within the data. Furthermore, modeling in data science involves the development of mathematical or computational models to represent and understand complex systems, as well as to make predictions or optimize outcomes based on the available data (Rossi, 2022).

In the realm of data science, the analysis and modeling of data are critical for uncovering actionable insights and supporting informed decision-making processes. These processes enable data scientists to derive meaningful conclusions, make predictions, and optimize outcomes based on the available data, thereby contributing to the advancement of modern decision-making practices.

5. Data Visualization

Data visualization is a fundamental component of data science, enabling the communication of complex insights in a clear and impactful manner. It involves the use of various graphical elements and tools to represent data, making it easier for decision-makers to understand patterns, trends, and relationships within the data. Effective data visualization enhances the decision-making process by providing a visual context for analysis and interpretation (Li, 2020). By leveraging techniques such as charts, graphs, maps, and dashboards, data scientists can transform raw data into compelling visual narratives that aid in understanding and deriving actionable insights from the data.

Furthermore, the history of data visualization highlights its evolution from simple charts to sophisticated interactive visualizations, emphasizing its significance in modern decision-making processes. As such, the use of data visualization tools and techniques is crucial for organizations seeking to harness the power of data in making informed decisions.

6. Applications of Data Science in Decision Making

Data science finds wide-ranging applications in decision making across various domains, showcasing its versatility and practical significance. In healthcare, data science is utilized to analyze patient data and medical records to identify patterns and predict potential health issues, enabling proactive and personalized patient care (Tamer Özsu, 2023). Moreover, in finance, data science techniques are employed for fraud detection, risk assessment, and algorithmic trading, thereby enhancing the accuracy and efficiency of financial decision making processes (Rossi, 2022). These examples illustrate how data science methodologies are instrumental in informing and improving decision making across diverse practical scenarios. Incorporating data science in decision making not only enhances the accuracy and efficiency of processes but also contributes to evidence-based policies and societal impact. The interdisciplinary nature of data science, drawing on data engineering, analytics, protection, and ethics, underscores its wide-ranging influence on decision making processes. Furthermore, the emergence of data science programs at various educational levels reflects the increasing recognition of the value of data for decision making and the growing demand for skilled data science professionals. These real-world applications underscore the vital role of data science in shaping modern decision making across industries and sectors.

References:

Tamer Özsu, M. (2023). Foundations and Scoping of Data Science. [PDF]

Rossi, R. (2022). Data Science in Perspective. [PDF]

Cao, L. (2020). Data Science: Challenges and Directions. [PDF]

C. Siang, L., Elnawawi, S., D. Rippon, L., L. O'Connor, D., & Bhushan Gopaluni, R. (2022). Data Quality Over Quantity: Pitfalls and Guidelines for Process Analytics. [PDF]

Yedla, A. & Dorius, S. (2017). An introductory guide to Data science: The terminological landscape. [PDF]

Li, Q. (2020). Overview of Data Visualization. ncbi.nlm.nih.gov