The importance of Big Data technologies for modern businesses

Victoria Polyakova

Bachelor's thesis

Abstract:

This bachelor thesis explores the intersection of big data analytics and predictive modeling in the context of airline passenger satisfaction. The theoretical framework delves into the definition and significance of big data analytics, elucidating various types of analytics and the tools utilized in the field. It places predictive analytics techniques such as business intelligence, statistics, and data mining, highlighting their pivotal roles in uncovering insights from vast datasets. Additionally, the study examines the benefits and challenges associated with big data analytics, underlining its potential across diverse industry sectors. Practical application involves leveraging IBM SPSS Statistics to conduct logistic regression and clustering analysis on a dataset concerning airline passenger satisfaction. This thesis utilizes logistic regression to identify key predictors of passenger satisfaction, while clustering analysis uncovers distinct customer groups. Through theoretical insights and practical application, the study enhances understanding of big data analytics’ role in the aviation industry. It emphasizes the significance of advanced analytics tools in extracting actionable insights for strategic decision-making, ultimately fostering customer-centric approaches.

Keywords

Big Data, Machine Learning, IBM SPSS Statistics, Technology, Analytics, Satisfaction, Tools, Regression, Clustering, Correlation

Language used: English

Date on which the thesis was submitted / produced: 29. 5. 2024

Thesis defence

• Date of defence: 30. 5. 2024
• Supervisor: Ing. Zuzana Pacáková, Ph.D.
• Reader: Tomáš Hlavsa, Ph.D.

Czech University of Life Sciences Prague

Faculty of Economics and Management

Bachelor programme:
Business Administration