1: Introduction to AI in Data Science

1.1 Overview of AI and Its Role in Data Science
1.2 Benefits of Integrating AI into Analytical Workflows
1.3 Key AI Techniques and Tools for Data Science

2: Data Preparation and Cleaning with AI

2.1 Automating Data Cleaning and Transformation
2.2 Handling Missing Values and Outliers Using AI
2.3 AI Techniques for Feature Engineering and Selection

3: Predictive Modeling and AI Techniques

3.1 Applying AI Algorithms for Predictive Analytics
3.2 Comparing Traditional and AI-based Models
3.3 Advanced AI Techniques: Ensemble Methods, Hyperparameter Tuning

4: Enhancing Data Visualization with AI

4.1 AI-driven Visualization Techniques for Complex Data
4.2 Integrating AI with Visualization Tools: Tableau, Power BI
4.3 Automated Insights and Reporting Using AI

5: AI in Data Analysis and Interpretation

5.1 Using AI for Advanced Data Analysis: Clustering, Dimensionality Reduction
5.2 Automated Pattern Recognition and Anomaly Detection
5.3 Interpreting AI Models and Results in Data Science(Ref: Data Science Insights with Hyperintelligence by MicroStratergy)

6: Model Deployment and Automation

6.1 Deploying AI Models in Data Science Projects
6.2 Automating Model Training and Evaluation
6.3 Building and Integrating AI Pipelines for Continuous Analysis

7: Case Studies and Practical Applications

7.1 Case Study 1: Enhancing Sales Forecasting with AI
7.2 Case Study 2: Optimizing Customer Segmentation Using AI
7.3 Case Study 3: Automating Financial Reporting with AI

8: Ethical Considerations and Best Practices

8.1 Ethical Implications of Using AI in Data Science
8.2 Ensuring Fairness and Transparency in AI Models
8.3 Best Practices for Responsible AI Implementation

9: Hands-on Projects

9.1 Project 1: Automating Data Preparation and Feature Engineering
9.2 Project 2: Building and Evaluating Predictive Models with AI
9.3 Project 3: Creating AI-enhanced Visualizations and Reports

10: Future Trends and Innovations

10.1 Emerging Trends in AI for Data Science
10.2 Innovations in Analytical Workflows and AI Technologies
10.3 Preparing for Future Developments in AI and Data Science

11: Conclusion and Resources

11.1 Recap of Key AI Techniques and Their Impact on Data Science
11.2 Resources for Continued Learning and Professional Development
11.3 Next Steps for Advancing AI Skills in Data Science

Reference