Bridging Advanced Data Science, Machine Learning and Future of Accounting and Auditing: A Theoretical Review

The aim of this theoretical review is to provide a basic understanding of advanced data science, the process of data science, data science paradigm, tools for data science technologies such as are R-Programming, Python, Hadoop, Tableau, D3.js, Data Wrapper, SAS (Statistical Analysis Software), Apache Spark, BigML, MATLAB, Excel, ggplot2, Jupyter, Matplotlib, NLTK, Scikit-learn, TensorFlow, Weka, etc. This study also discusses various aspects of data science such as fundamental principles of data science, High-Dimensional Space, Best-Fit Subspaces, Singular Value Decomposition (SVD), Random Walks, and Markov Chains. After providing an overview of data science, this study theoretically discusses many issues of Machine Learning such as VC dimension, Deep learning, Regularization, Kernel functions, etc. Finally, this theoretical review points out the future of accounting and auditing in the age of data science, many aspects of block-chain, and the challenges and opportunities for professional accountants.