To start building a portfolio of data science projects to showcase your skills to potential employers, you can follow a structured approach, leveraging the "Python for Data Science" course at the "RoboticView" bootcamp. Here's a step-by-step guide:

1. Enroll in the Python for Data Science Course: Begin by enrolling in the "Python for Data Science" course at the "RoboticView" bootcamp. This course will provide you with a strong foundation in Python programming and data science tools, making it an excellent starting point for your journey.

2. Learn the Fundamentals: Pay close attention to the fundamentals of Python programming, data manipulation, and data visualization. Understand libraries like NumPy, Pandas, and Matplotlib, as these are essential tools for data analysis and visualization.

3. Choose a Focus Area: Data science is a broad field. Decide on your area of interest within data science, such as machine learning, natural language processing, or computer vision. Your portfolio should reflect your passion and expertise in this specific area.

4. Work on Guided Projects: While taking the course, complete guided projects and exercises provided by "RoboticView." These projects will help you apply what you've learned in a practical context and build a foundation for more complex projects.

5. Personal Projects: Once you've gained confidence in your Python and data science skills through guided projects, start working on your personal data science projects. These projects should align with your chosen focus area and be driven by your curiosity.

6. Use Real Data: Whenever possible, use real-world datasets for your projects. This will demonstrate your ability to work with messy and complex data, a crucial skill in data science.

7. Document Your Work: As you work on your projects, document your progress, methodology, and insights. Create Jupyter notebooks or reports that explain your data exploration, analysis, and modeling steps clearly.

8. Implement Machine Learning: If your focus area involves machine learning, implement machine learning algorithms and models in your projects. Experiment with different algorithms and techniques to demonstrate your proficiency.

9. Visualize and Communicate: Create informative and visually appealing data visualizations to communicate your findings effectively. Employ libraries like Seaborn and Plotly for advanced visualization.

10. GitHub Repository: Create a GitHub repository dedicated to your data science portfolio. Upload your project code, notebooks, and README files describing the projects, datasets used, and your approach.

11. Portfolio Website: Consider building a personal website or blog to showcase your portfolio projects. Include project descriptions, visualizations, and links to your GitHub repository. This website can serve as a central hub for potential employers to review your work.

12. Networking and Sharing: Share your projects on platforms like LinkedIn, GitHub, and relevant data science forums or communities. Engage in discussions, collaborate with others, and seek feedback to improve your skills and projects.

13. Continuous Learning: Data science is a rapidly evolving field. Stay updated with the latest trends and technologies by taking additional courses, attending webinars, and reading relevant books and research papers.

By following these steps and leveraging the resources provided by the "Python for Data Science" course at "RoboticView," you'll be well on your way to building a compelling data science portfolio that will impress potential employers and demonstrate your skills and passion for the field.