The Key Questions in Data Sciences and Machine Learning - A Literature Review

Aman Kumar, Nidhi Upadhyay, Ankita Singh, Ankit Raj

Data Sciences, Machine Learning, Big Data, Statistics, Medical Sciences, Business Intelligence

Amongst one of the most emerging fields of computation and sciences, data sciences have proved their metal in the world today. With the blending and convergence of multiple technological sectors and human life, the amount of data generated have increased exponentially today, making data the new oil and amongst one of the most exquisite resources available. With problems ranging from medical sciences to addressing problems of business intelligence, the application of data sciences in various domains is accepted as a major factor for decision making which is widely accepted now. This multi-disciplinary field has concepts overlaying with data-driven technologies like Big Data, Machine Learning, Statistical Inferences, Cloud computing and mathematics. Undoubtedly just not being a tool-oriented field, the multi-disciplinary field of data sciences, today has become a practical and practice-oriented field. It has been these features, which makes data science amongst the most demanded skills of the 21st century, with very high demands of skilled professionals. With analysis of the use of data science and its trends, this research paper highlights the various general and common terminologies, analogous to this field and throws light on some key areas where data-driven decision-making methods are used widely. With understanding the basic methodology of the decision-making policy, the paper also studies the availability of open-source tools that could be utilized effectively in this field. Since all of the areas of science where data analysis has been the priority of research and data is the centre of focus are evolving rapidly, it has moulded data science into a must to have skill. This not only is improving the decision-making capabilities but helping individuals and stakeholders to experience and answer the most challenging questions with the best data-based answers.

https://dx.doi.org/10.31873/IJEAS.8.8.09