Summary
Keywords
Full Transcript
Welcome to Part 2 of our comprehensive Python for Data Science course! In this installment, we dive even deeper into the world of data science with over 15 hours of in-depth tutorials, covering more than 5 essential Python libraries. Python for Data Science Full Course Part 1 - https://youtu.be/LgW6Vla0kXA?si=oeW3QjifAGjoALHr In this Video 00:00 - Intro 05:10 - How To Download Notes 09:41 - Lecture No. 14 : List in Python Part 1 48:06 - Lecture No. 15 : List in Python Part 2 1:21:42 - Lecture No. 16 : Special Class 1:52:24 - Lecture No. 17 : Tuples in Python part 1 2:20:13 - Lecture No. 18 : Tuples in Python part 2 2:39:03 - Lecture No. 19 : Tuples in Python part 3 3:01:45 - Lecture No. 20 : Sets in Python Part 1 3:26:36 - Lecture No. 21 : Sets in Python Part 2 3:47:24 - Lecture No. 22 : Sets in Python Part 3 4:12:35 - Lecture No. 23 : Dictionary in Python part 1 4:34:50 - Lecture No. 24 : Dictionary in Python part 2 4:55:39 - Lecture No. 25 : Dictionary in Python part 3 5:11:33 - Lecture No. 26 : Dictionary in Python part 4 5:38:01 - Lecture No. 27 : Dictionary in Python part 5 5:54:11 - Lecture No. 28 : OOPS in Python part 1 6:29:20 - Lecture No. 29 : OOPS in Python part 2 6:57:03 - Lecture No. 30 : OOPS in Python part 3 Python Libraries : Numpy, Pandas , Matplotlib, Seaborn, Plotly 7:24:08 - Numpy Part 1 8:05:36 - Numpy Part 2 8:56:17 - Pandas Part 1 9:54:33 - Pandas Part 2 10:51:40 - Matplotlib part 1 11:46:08 - Matplotlib part 2 12:39:00 - Seaborn Part 1 13:26:09 - Seaborn Part 2 14:10:33 - Plotly Special Project 15:04:37 - Zomato Data Exploration ✳️Download the Notes & Data Set - https://github.com/TheiScale/YouTube-Video-Notes/tree/main/Zomato_Python_Project 📚 Course Content: Advanced NumPy for numerical computations Pandas for data manipulation and analysis Matplotlib and Seaborn for data visualization Scikit-learn for machine learning Statsmodels for statistical modeling 💻 Hands-On Projects: Apply your knowledge with real-world projects that will help you master these libraries and gain practical experience. 🔍 Deep Dives: We’ll go beyond the basics, exploring advanced techniques and best practices in each library. 📊 Data Science Workflows: Learn how to efficiently combine these libraries to create powerful data science workflows. 🌟 Additional Resources: Access further resources and materials to enhance your learning experience. ✨Lecture 14 to 30 Notes - https://www.theiscale.com/course-details/free-data-science-course ➖➖➖➖➖➖ 📱 For Any Further Queries or Doubts? Contact- 7880-113-112 (Student Helpline Number) For any query connect in WhatsApp with us: https://wa.me/917880113112 ➖➖➖➖➖➖ ✳️ Join Telegram Channel- https://t.me/TheiScale ✳️ Join WhatsApp Channel- https://whatsapp.com/channel/0029VaB5ekEKQuJQV572vi2c ✳️Instagram https://www.instagram.com/theiscale?igsh=MWZ3anc0YTFhZ2lybw== ✳️Instagram https://www.instagram.com/theiscale.founders?igsh=bDJjaWJkaWx5eXJk ➖➖➖➖➖➖ ❇️ Explore our Job Oriented Courses: https://www.theiscale.com/explore-course ➖➖➖➖➖➖ 🔗 Download App Google Play: https://play.google.com/store/apps/details?id=com.logixhunt.ihhpet&pli=1 Whether you're continuing from Part 1 or just starting out, this course is designed to elevate your data science skills and prepare you for real-world challenges. Don't forget to like, comment, and subscribe for updates on the next parts of this series! #Python #DataScience #FullCourse #NumPy #Pandas #Matplotlib #Seaborn #ScikitLearn #Statsmodels #DataVisualization #MachineLearning #LearnPython #TechEducation #DataScienceProjects
Continue this lesson in the app
Install CourseHive on Android or iOS to keep learning while you move.
