Numerical Computing with Numpy | Data Analysis with Python (3/6) | Free Certification
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Numerical Computing with Numpy & Working and operating on files with Python. This lecture is Part 3/6 of the Data Analysis with Python Free Certification Course. Visit https://jovian.ai/learn/data-analysis... for the detailed structure of this lecture.
Time Breaks
00:00 - Course Introduction
02:52 - Numerical Computing with Numpy
11:23 - From Python Lists to Numpy Arrays
14:26 - Operating on Numpy Arrays
16:17 - Benefits of Numpy Arrays
19:59 - Multidimensional Numpy Arrays
50:11 - Array Indexing and Slicing
58:30 - Creating Numpy Arrays (Alternative method)
1:04:33 - Exercises and Further Reading
1:07:33 - Assignment 2 - Numpy Array Operations
1:13:40 - Jovian Forum
1:16:36 - 100 Numpy Exercises
1:18:56 - Reading from and Writing to Files using Python
1:51:19 - What to do next?
1:52:58 - Mentorship Program
Links to resources used in the lecture
🔗 Numerical computing with Numpy: https://jovian.ai/aakashns/python-num...
🔗 100 Numpy exercises: https://jovian.ai/aakashns/100-numpy-...
🔗 Working with OS & files: https://jovian.ai/aakashns/python-os-...
🔗 Assignment 2 - Numpy Array Operations: https://jovian.ai/learn/data-analysis...
🔗 Discussion forum: https://jovian.ai/forum/t/lecture-3-n...
Topics covered
⌨️ Going from Python lists to Numpy arrays
⌨️ Working with multi-dimensional arrays
⌨️ Array operations, slicing and broadcasting
⌨️ Working with CSV data files
Data Analysis with Python: Zero to Pandas, a self-based course offered by Jovian and is a practical, beginner-friendly, and coding-focused introduction to data analysis covering the basics of Python, Numpy, Pandas, data visualization and exploratory data analysis for beginners. All projects and assignments as part of this course can be executed on interactive Jupyter notebooks running on the cloud.
You can learn more and register for a Free Certificate of Accomplishment at http://zerotopandas.com.
This course is taught by Aakash N S, co-founder & CEO of Jovian - a platform for sharing, showcasing and collaborating on data science projects online
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