That’s awesome! Thank you for checking out the vid! ☺️

Haha, chị cảm ơn em!

Really appreciate this! Thanks 🙌

T cám ơn nhé 💕🤗

Hi Vivuviuu Good evening . I hope my comment didn't sound as a form of privacy invasion your comment tells of a wonderful Woman with a beautiful heart which led me to comment I don't normally write in the comment section but I think you deserve this complement. If you don’t mind can we be friends? Thanks God bless you….

Thank you! I don’t know of similar tools in R but I’m sure there are

Haha awesome! Thank you Faruq 🤩

Aw that's too nice of you! 💖

I have a roadmap to learn data science, which is my goal. I will turn 15 on August 26th and I am a sophomore in high school. This is my roadmap " 1. Python Basics: Ensure you have a solid understanding of Python fundamentals. If you're comfortable with what you listed earlier ("Printing to console with print(), Variables, Data types, Math operations, If-else statements, Loops, Functions, Strings, Reading/Writing files, Dictionaries, Try/except blocks"), you're off to a good start. If not, consider going through Python beginner tutorials. 2. Data Manipulation with Pandas: Start by learning how to manipulate and analyze data using Pandas. Focus on concepts like DataFrame creation, data cleaning, filtering, and aggregation. 3. Data Visualization with Matplotlib and Seaborn: Begin exploring data visualization libraries like Matplotlib and Seaborn. Learn how to create various types of plots and charts to visualize data effectively. 4. Statistics and Probability: Continue to deepen your understanding of statistics and probability, especially as they relate to data analysis. Practice hypothesis testing, probability distributions, and statistical modeling. 5. Machine Learning: Start applying machine learning techniques to your data. Begin with simple models using Scikit-Learn and gradually move to more complex algorithms. Focus on preprocessing data, feature engineering, model selection, and evaluation. 6. Data Visualization: Since you've already covered basic data visualization, explore more advanced visualization techniques. Learn about interactive visualizations, customizing plots, and using advanced libraries like Plotly for more sophisticated data representations. 7. Advanced Pandas Techniques: Dive deeper into Pandas by exploring advanced techniques like multi-indexing, pivot tables, and time series analysis. These skills will be valuable for handling complex datasets. 8. Feature Engineering: Master the art of feature engineering. It's a critical skill in data science that involves creating new features from existing data to improve model performance. 9. Machine Learning Projects: Start working on machine learning projects that involve real-world datasets. Apply what you've learned to solve practical problems. 10. Online Courses and Specializations: Consider enrolling in more specialized online data science courses or specializations that go deeper into specific areas of data science, such as natural language processing (NLP), computer vision, or time series analysis. 11. Collaboration and Portfolio Building: Collaborate with others on data science projects or join data science communities to gain insights and network with professionals. Continue to build your data science portfolio with diverse projects. 12. Stay Updated: Data science is a rapidly evolving field. Stay updated with the latest trends, tools, and techniques by reading blogs, research papers, and following industry news. 13. Advanced Libraries (Optional): Depending on your interests, you might explore more specialized Python libraries like spaCy for NLP or TensorFlow/Keras for deep learning. 14. Machine Learning Competitions (Optional): Participate in machine learning competitions on platforms like Kaggle to sharpen your skills and compete with data scientists from around the world.

Any suggestions? ❤❤

Haha yay! 🙌

Thx