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Table of Contents:

Chapter 1: Introduction
– Background of the Study
– Statement of the Problem
– Research Objectives
– Research Questions
– Significance of the Study
– Definition of Terms

Chapter 2: Literature Review
– Overview of Metric Geometry
– Applications of Metric Geometry in Data Analysis
– Applications of Metric Geometry in Visualization
– Machine Learning Techniques
– Data Science Applications
– Applied Topology in Data Analysis

Chapter 3: Research Methodology
– Research Design
– Data Collection Methods
– Data Analysis Techniques
– Sampling Techniques
– Ethical Considerations

Chapter 4: Discussion of Findings
– Analysis of Data Collected
– Comparison of Results with Existing Literature
– Interpretation of Findings
– Implications for Future Research

Chapter 5: Conclusion and Summary
– Summary of Key Findings
– Conclusion
– Recommendations for Future Research
– Contributions to the Field

Brief Overview of the Thesis:
The thesis on “Metric geometry and its applications in data analysis and visualization in machine learning in data science in applied topology” focuses on exploring the use of metric geometry in the field of data science and machine learning. The study aims to investigate how metric geometry can be applied to analyze and visualize data, with a specific focus on its applications in machine learning techniques. Additionally, the thesis explores the role of applied topology in data analysis and its significance in understanding complex data structures. Through a comprehensive literature review, research methodology, and discussion of findings, the thesis aims to contribute to the advancement of knowledge in the field of data science and machine learning.

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