Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Abstract

The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science" including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Keywords: Advanced analytics; Data science; Data science applications; Decision-making; Deep learning; Machine learning; Predictive analytics; Smart computing.

Fig. 1

The worldwide popularity score of data science comparing with relevant  areas in a range of 0 (min) to 100 (max) over time where x-axis represents the timestamp information and y-axis represents the corresponding score

Fig. 2

An example of data science modeling from real-world data to data-driven system and decision making

Fig. 3

A general structure of a machine learning based predictive model considering both the training and testing phase

Fig. 4

An example of a random forest structure considering multiple decision trees

Fig. 5

An example of producing aggregate time segments from initial time slices based on similar behavioral characteristics

Fig. 6

A structure of an artificial neural network modeling with multiple processing layers