Introduction
Data science is progressing at a rapid pace. To understand the different realities of this progress, we look at the most challenging areas of research in this field. Since data science stands at the intersection of the fields like computer science, statistics, and business intelligence, it is difficult to develop a good understanding of this field without enrolling in an advanced data science course.
Causal reasoning
One of the most powerful tools that examine large data sets in terms of association and correlation is machine learning. The most potential research area is the examination of causal inference. To make casual reasoning techniques more instrumental, data scientists are collaborating with economists to work on diverse data sets. The result is an exploration of multiple causal inferences related to real-world problems.
Data experiments
Data is the oxygen of research. Let us probe this in deeper detail. Firstly, data that is to be used in a research problem may be scattered. It is a herculean task to collect it for analysis. Secondly, there will be noise in this data. So, the authenticity of data needs to be established. Thirdly, the data should be reliable as well as valid. After the data set has met all the above requirements, it can be used for various research experiments. These experiments range from the Large Hadron Collider to the ice cube neutrino detector. This gives importance associated with the data sets that are gathered at the initial stage. In addition to this, we may incur a lot of expenses during the collection of real data. For instance, the collection of particle data of the Large Hadron Collider will incur a lot of expenses. Moreover, we may require novel data science methods and models to process this data.
Trusting the AI systems
Over the last few years, we have observed the application of artificial intelligence systems in the fields of traffic management, criminal detection, healthcare systems, and Public Service Delivery. These critical domains call for a trustworthy setup of data governance. Even if decisions are to be taken with the aid of artificial intelligence, it needs to be ensured that the social elements of these decisions are properly taken care of. There is also an emerging demand for trustworthy computing systems as the majority of these systems are powered by Artificial Intelligence and machine learning. So, the research for building a framework of trustworthy artificial intelligence models needs to be fast-tracked.
Data ethics is the way ahead
The field of data science is coming under the lens of ethical examination. Data ethics looks into three prime matters. Firstly, it looks into how data is generated, processed, and used. Secondly, the ethics of algorithms is analyzed. Lastly, the ethics of applications is put to holistic examination. If all the ethical principles are followed, a particular data science technology is given a go-ahead. Upon violation of particular norms, that technology may be temporarily frozen. One of the most prominent fields in which data ethics is employed is the examination of privacy laws. Data ethics makes sure that all the privacy laws conform to the highest ethical standards framed for them.
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