Introduction
The learning in the information system and business analytics program assumes reflection to have taken place at the beginning of the journey, into the previous academic experiences and professional experiences. Thus, reflections not only help to throw light on interests and strengths but also guide a way to chalk out the movement of aspirations ahead. This paper discusses some of the methodological and application areas that excite me the most and then articulates the goals and aspirations that have pushed me to proceed with the program.
Methodological Area of Interest
My passion lays more toward predictive analytics: the area of methods that deal with the application of historical data for anticipation of future outcomes. It is a blend of statistics, and machine learning, together with skills from data analysis. Among the methods and models in this domain, regression analysis and neural networks felt much brighter for me (Chapter 8. Regression Basics – Introductory Business Statistics with Interactive Spreadsheets – 1st Canadian Edition, 2019). Prediction avails itself of two general tools: regression analysis, one of the widely used areas of statistics to spell out the relationships between dependent and independent variables, and neural network analysis, which is a continuum of weighted layered architecture well applied in business for practical or realization of insight in complex nets of nonlinear relationships.
The major attractive side of predictive analytics is that it is so versatile, starting from business to health and everything. It may find your mind uniquely challenged therein since the field is in constant dynamics due to the continuous retreat in it by artificial intelligence and machine learning (Alkhaldi, 2021). I want to be a maestro of methods and master the sense of applying them creatively, effectively, and in full APK understanding. Most importantly, I would expect not only the theoretical underpinning but also the way to pave in developing a practical skill set, enabling a person to apply these models effectively.
Application Areas
Of particular interest to me and found to be quite intriguing, even so, is a field like predictive analytics and may be versatile to apply to many different areas, but in the same breath; my interest is applied to the area of financial services, particularly concerning the assessment of credit risk. As an illustration, I found the historical appraisal of financial data and the estimation of the possibility that an applicant for a loan will default to be very informative and interesting. This kind of application, apart from being critical for financial organizations to keep off risks, ensures economic stability. For example, logistic regression models find their application in scoring and categorizing applicants according to the risk level they possess. Such models give a quantitative scorecard on the assessment of credit risk, hence a guide in the decision for lending through analysis of variables that include credit history, level of income, and employment (Debabrata Dansana et al., 2023). This area is one of the cases of a full meeting of methodological aspirations and practice application aspirations about predictive analytics applied to the assessment of credit risk. The answer can only consist of highly developed methods of analytics being applied to a real problem, providing a way for more enlightened and fair financial decisions.
Conclusion
In conclusion, reflection on my interest area in methodology and applied use to the financial area, in particular, did not just strengthen my basics for possible usage of business analytics but improved the quality of these aspirations. In this regard, I am greatly aspiring to get acquainted with predictive analytics, especially the detailed consideration of regression analysis and neural networks with their application to the area of credit risk assessment. I will be dedicated to making the most of this opportunity, aspiring to be a mover and shaker of the field of Analytics by setting challenging but achievable goals.
References
Alkhaldi, N. (2021, June 18). Predictive Analytics In Healthcare: 7 Examples and Risks. ITRex. https://itrexgroup.com/blog/predictive-analytics-in-healthcare-top-use-cases/
Chapter 8. Regression Basics – Introductory Business Statistics with Interactive Spreadsheets – 1st Canadian Edition. (2019). Opentextbc.ca. https://opentextbc.ca/introductorybusinessstatistics/chapter/regression-basics-2/
Debabrata Dansana, Patro, K., Brojo Kishore Mishra, Vivek Kumar Prasad, Abdul Razak Kaladgi, & Anteneh Wogasso Wodajo. (2023). Analyzing the impact of loan features on bank loan prediction using the Random Forest algorithm. Engineering Reports. https://doi.org/10.1002/eng2.12707
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