Research Areas that are rare and littered with plenty funding in US, UK, Canada, Europe and Asian Nations
for graduates of:
1. Accounting
2.Economics
3. Religion
4. Mathematics
5. Physics
6. Chemistry
7. Biology
8. Business management
9. Journalism
10. Communication
11. Philosophy
12. Law
13. Political Science.
1. Accounting:
– Data Analytics in Accounting: The use of data science and machine learning techniques to analyze accounting data for fraud detection, tax compliance, and other purposes.
– Predictive Accounting: The use of machine learning models to make predictions about future financial performance based on historical data.
2. Economics:
– Econometrics: The use of statistical methods and machine learning models to test economic theories and make predictions about economic trends.
– Data-Driven Macroeconomics: The use of large-scale data and machine learning techniques to analyze macroeconomic patterns and make predictions about the economy.
– Predictive Microeconomics
3.Religion
-Computational Theology: The use of data science and machine learning techniques to analyze religious texts and beliefs.
– Digital Humanities and Religion: The use of data science and machine learning techniques to analyze religious artifacts, such as historical manuscripts and art, and to study religious practices and rituals.
4. Mathematics
– Data Science in Mathematics: The application of data science and machine learning techniques to mathematical problems and models.
– Computational Mathematics: The use of computational techniques, including machine learning algorithms, to solve mathematical problems.
5. Physics
– Data-Driven Physics: The use of data science and machine learning techniques to analyze and understand physical phenomena, such as particle physics and astrophysics.
– Machine Learning in Material Science: The use of machine learning algorithms to predict material properties and develop new materials.
6.Chemistry
– Computational Chemistry: The use of data science and machine learning techniques to simulate chemical reactions and predict chemical properties.
– Predictive Toxicology: The use of machine learning models to predict the toxic effects of chemicals on living organisms.
7. Biology
– Bioinformatics: The use of data science and machine learning techniques to analyze biological data, including DNA sequences and protein structures.
– Computational Biology: The use of data science and machine learning techniques to model biological systems and predict biological outcomes.
8. Business Management
– Predictive Management: The use of machine learning models to make predictions about future business performance based on historical data.
– Customer Analytics: The use of data science and machine learning techniques to analyze customer data and inform marketing and sales strategies.
9. Journalism
– Data-Driven Journalism: The use of data science and machine learning techniques to gather, analyze, and present news and information.
– Computational Investigative Journalism: The use of data science and machine learning techniques to investigate and uncover facts.
– Predictive News Analytics: Using machine learning algorithms to analyze news events and trends to make predictions and provide insights.
– Digital Fact-Checking: Using data science and
machine learning to automate and improve the accuracy and efficiency of facts.
10.Communication
– Data-Driven Communication: The use of data science and machine learning techniques to analyze and understand communication patterns and behaviors.
– Natural Language Processing: The use of machine learning algorithms to process and analyze human language.
11. Philosophy:
– Data-Driven Ethics: The use of data science and machine learning techniques to analyze ethical issues and make ethical decisions.
– Computational Epistemology: The use of data science and machine learning techniques to study knowledge and belief.
12. Law:
– Legal Analytics: The use of data science and machine learning techniques to analyze legal data and inform legal decision making.
– Predictive Law: The use of machine learning models to make predictions about legal outcomes based on historical data.
