I realize that it is not easy to immediately change our hats from practicing the old way of handling data by looking at it with the naked eye, to the new way of handling data with our eyes closed, in order to become a responsible data scientist
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….
Hello! I've just discovered your channel and it's been my favorite one I've stumbled onto in my research about data analytics. Your videos are very thoughtful and intelligent; I like how carefully and succinctly you explain ideas.. I think one video idea that might be fun to do, is you maybe explaining why you find data interesting? What drew you into this field? It'd be fun to see you get nerdy about why you like data, this field. 🤓I think in the book '21 Lessons for the 21st Century,' the author talks about how.. Phenomenal? Extraordinary? AI capabilities will become, how much data can be gathered by the smallest things... Imagine if one day AI can make a song PERFECTLY suited to your taste, based on physiological reactions, brain activity, the patterns of the music you like, etc... Or if we can collect such thorough data that the smallest change in a biomarker could detect cancer in the earliest of stages. Data is very fascinating! (((Sorry if you've gone into this in a different video I haven't seen yet)))
Thanks for the video) Federated Learning becomes more and more popular, nice to see it because when I was starting to implement it in smart production during my PhD a few years ago it was not so popular 😅 and it was hard to explain the value of it for some proffesors😁
Em ở Việt Nam, cũng đang có hướng làm trái nghành sang Data . Tình cờ biết được kênh của chị. Công nhận chị nói tiếng Anh hay thật. Hơn hẳn những kênh em thường nghe😅
Mam as a ms Excel export please clear my doubt, copilot 365 will available in ms Excel and other ai tools, so as beginner should I learn ms Excel deeply all functions and advance formulas Or I can do all ms works by using copilot and other ai tools??? What should I do
Hello mam, I am working as a data analyst in a startup of just 2 and half months / 3 months, due to some major problem i have to leave the organisation. As I am a fresher so please guide me what can I say when i will give interview for another companies, when the HR will ask me that why you leave the company in so short span of time? Please guide me sir , i desperately needed proper reason so that it will not impact my impression. Looking forward to hear from you. Thank 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.
I realize that it is not easy to immediately change our hats from practicing the old way of handling data by looking at it with the naked eye, to the new way of handling data with our eyes closed, in order to become a responsible data scientist
em đang làm first portfolio project, chỉ có thể chỉ e step by step, hoặc nguồn nào nào để dựa theo làm được không, đội ơn chị đẹp.
Was the Adult Pop data in the last example in a Secure Enclave?
Oh. Tớ mới nghe mọi người kể về kênh của Thu, đỉnh lắm luôn ấy
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….
Hello! I've just discovered your channel and it's been my favorite one I've stumbled onto in my research about data analytics. Your videos are very thoughtful and intelligent; I like how carefully and succinctly you explain ideas.. I think one video idea that might be fun to do, is you maybe explaining why you find data interesting? What drew you into this field? It'd be fun to see you get nerdy about why you like data, this field. 🤓I think in the book '21 Lessons for the 21st Century,' the author talks about how.. Phenomenal? Extraordinary? AI capabilities will become, how much data can be gathered by the smallest things... Imagine if one day AI can make a song PERFECTLY suited to your taste, based on physiological reactions, brain activity, the patterns of the music you like, etc... Or if we can collect such thorough data that the smallest change in a biomarker could detect cancer in the earliest of stages. Data is very fascinating! (((Sorry if you've gone into this in a different video I haven't seen yet)))
Thanks for the video) Federated Learning becomes more and more popular, nice to see it because when I was starting to implement it in smart production during my PhD a few years ago it was not so popular 😅 and it was hard to explain the value of it for some proffesors😁
YT's notification chimes in
Me: Thu vu?
No problem! ❤
Haha awesome! Thank you Faruq 🤩
Em ở Việt Nam, cũng đang có hướng làm trái nghành sang Data . Tình cờ biết được kênh của chị. Công nhận chị nói tiếng Anh hay thật. Hơn hẳn những kênh em thường nghe😅
Haha, chị cảm ơn em!
Mam as a ms Excel export please clear my doubt, copilot 365 will available in ms Excel and other ai tools, so as beginner should I learn ms Excel deeply all functions and advance formulas
Or I can do all ms works by using copilot and other ai tools???
What should I do
Thank you for sharing this helpful thing. 💙
That’s awesome! Thank you for checking out the vid! ☺️
Hello mam, I am working as a data analyst in a startup of just 2 and half months / 3 months, due to some major problem i have to leave the organisation. As I am a fresher so please guide me what can I say when i will give interview for another companies, when the HR will ask me that why you leave the company in so short span of time?
Please guide me sir , i desperately needed proper reason so that it will not impact my impression.
Looking forward to hear from you.
Thank you.
Very interesting video, is there an equivalent in R language?
Thank you! I don’t know of similar tools in R but I’m sure there are
Which subject should I choose after 12 for bsc and msc to become a good date scientist
the video thumbnail caught my attention. Shes cute and sexy voice at the same time.
First comment 😂❤
Haha yay! 🙌
She deserves nore then this!! ❤❤
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? ❤❤
Amazing content!
Really appreciate this! Thanks 🙌
So... 41 seconds into the video, the answer from experience is de-identifying data.
Thx
Hi
Can you help to prepare ATS resume for Data Analyst experienced