Assessing the Impact of Large Language Models on Data Science Workflow Optimization

1. Introduction 1.1 Background of the Study 1.2 Purpose and Objectives 1.3 Research Questions 1.4 Structure of the Paper 2. Literature Review 2.1 Overview of Large Language Models 2.2 Data Science Workflow Processes 2.3 Previous Studies on Workflow Optimization 2.4 Role of Artificial Intelligence in Data Science 3. Methodology 3.1 Research Design and Approach 3.2 Data Collection Techniques 3.3 Analysis Methods 3.4 Limitations and Assumptions 4. Large Language Models Overview 4.1 Development and Evolution 4.2 Capabilities and Features 4.3 Comparisons with Traditional Models 5. Applying LLMs in Data Science 5.1 Automating Data Cleaning 5.2 Enhancing Data Analysis 5.3 Optimizing Model Selection 6. Case Studies 6.1 Case Study Methodology 6.2 Case Study: Industry Application 6.3 Lessons Learned 7. Impact on Workflow Optimization 7.1 Efficiency Improvements 7.2 Accuracy and Precision Enhancement 7.3 Cost-Benefit Analysis 8. Conclusion and Recommendations 8.1 Summary of Findings 8.2 Practical Implications 8.3 Future Research Directions 8.4 Final Thoughts