The video is presented by Natasha, a data scientist, who shares her journey in learning data science and how she overcame the "tutorial trap" - the difficulty in comprehending complex concepts despite taking numerous online courses. She emphasizes the importance of coding and practical application of knowledge, using the example of Chat GPT, to learn programming and data science.
Natasha recommends starting with Python, suggesting courses like "Introduction to Python" by 365DearScience or "Free Code Camps Python for Beginners". She advises against getting stuck in the tutorial trap by continuously taking more courses after mastering the basics. Instead, she encourages practice through Chat GPT, which provides beginner to intermediate level Python programming practice questions.
Once the foundation in Python is established, Natasha suggests learning data science and machine learning through online courses like the "Python for Data Science and Machine Learning Bootcamp" on Udemy. She emphasizes the use of Chat GPT for practice and feedback on data science concepts.
She also introduces the Chat GPT code interpreter plugin, a tool that allows running code and building machine learning models within the Chat GPT interface. This is used to practice data manipulation skills using the pandas library.
Natasha stresses the importance of building data science projects to apply and consolidate the knowledge learned from online courses. She provides examples of such projects using Kaggle datasets like the Titanic survival prediction and Boston house pricing datasets. She recommends using Chat GPT as a guide, not just a source of code, to truly learn and improve.
Finally, she highlights the importance of creating a data science portfolio with personal projects to showcase to potential employers and land a job in the field. She plans to release a video soon discussing what kind of projects to build for this purpose.
1. The speaker, Natasha, is a data scientist and shares her journey of learning data science.
2. Natasha initially struggled with coding despite studying computer science and taking numerous online courses.
3. She was stuck in a cycle of taking online courses without actually learning anything, a phenomenon she refers to as the "tutorial trap".
4. Natasha took a break from online courses and sought advice from professionals in the field.
5. She consolidated the knowledge she received and created her own roadmap to learn data science.
6. This roadmap helped her secure her first job in the field in just six months.
7. Natasha currently uses a revised version of her roadmap to teach data science.
8. She recommends using Chat GPT to practice coding and data science skills.
9. Natasha suggests starting with Python for coding skills, as it's easy to use and versatile.
10. She recommends taking the "Introduction to Python" course by 365 Data Science or the free Code Camps Python for beginners course.
11. She advises against falling into the "tutorial trap" by taking more Python courses after the introductory one.
12. She suggests practicing Python programming skills by asking Chat GPT for practice questions.
13. Natasha recommends spending about three to four hours daily practicing with Chat GPT for about a month to improve programming skills.
14. She advises learning data science and machine learning after mastering coding.
15. She recommends the "Python for Data Science and Machine Learning Bootcamp" on Udemy for data science learning.
16. She suggests using Chat GPT to practice data science skills after learning new concepts in the online course.
17. She recommends using the Code Interpreter plugin of Chat GPT to run code and build machine learning models within the Chat GPT interface.
18. She advises practicing data manipulation skills with datasets generated by Chat GPT.
19. She suggests asking Chat GPT for guidance if stuck during data analysis or manipulation.
20. She advises not to simply copy-paste code generated by Chat GPT, but to use it as a guide.
21. She recommends building data science projects to apply learned skills and increase chances of landing a job in the field.
22. She emphasizes the importance of creating a portfolio website and a GitHub repository to showcase these projects.
23. She plans to release a video in the following weeks discussing the types of data science projects to build for job applications.