I am a master’s student in Data Science with programming experience in Python. I am not interested in machine learning, but I want to explore topics related to data visualization, data quality, reproducible workflows, or open data

1. Introduction 1.1 Background and Motivation 1.2 Problem Statement 1.3 Objectives of the Study 1.4 Methodology Overview 1.5 Structure of the Thesis 2. Data Visualization Techniques 2.1 Overview of Data Visualization 2.2 Tools for Data Visualization 2.3 Best Practices in Visualization 2.4 Case Studies in Data Visualization 3. Ensuring Data Quality 3.1 Definition of Data Quality 3.2 Measures for Data Integrity 3.3 Data Cleaning Techniques 3.4 Impact of Data Quality on Analysis 4. Reproducible Workflows in Data Science 4.1 Importance of Reproducibility 4.2 Tools for Reproducible Workflows 4.3 Challenges in Reproducing Analysis 4.4 Case Studies on Reproducibility 5. The Role of Open Data 5.1 Understanding Open Data 5.2 Benefits of Open Data Initiatives 5.3 Challenges of Using Open Data 5.4 Examples of Open Data Projects 6. Integrating Data Visualization and Quality 6.1 Importance of Quality in Visualization 6.2 Methods to Represent Data Quality 6.3 Examples of Integrated Approaches 7. Developing Reproducible Workflows 7.1 Designing a Workflow Framework 7.2 Tools for Workflow Implementation 7.3 Evaluation of Workflow Efficiency 8. Conclusion and Future Work 8.1 Summary of Key Findings 8.2 Implications for Data Science 8.3 Future Research Directions 8.4 Final Thoughts and Recommendations