Python Numpy For Data Science (2025)

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Python Numpy For Data Science (2025) (Size: 856.6 MB)
  1 -Course_Python_Numpy_For_Data_Science.ipynb 895.5 KB
  1 -Dealing with missing values and basic imputation techniques.mp4 44.8 MB
  1 -Inferential Statistics and Universal Functions.mp4 51.1 MB
  1 -Introduction.mp4 9.8 MB
  1 -Major Ideas from Linear Algebra.mp4 113.1 MB
  1 -Numpy Arrays and Manipulation.mp4 106.1 MB
  1 -Numpy Dates and Set Operations.mp4 55.1 MB
  1 -Numpy and its use in Machine Learning and Data Science Applications.mp4 79.1 MB
  1 -Performance Optimization For loops vs Vectorization.mp4 26.7 MB
  1 -Plotting with Numpy.mp4 30.5 MB
  1 -Sequences and Random Numbers arange, linspace, and random number generation.mp4 79.1 MB
  1 -Vectorized Operations.mp4 92.7 MB
  2 -Array Indexing and Slicing.mp4 99.3 MB
  2 -Vectorized Operations Continued.mp4 48.2 MB
  3 -Comparing Numpy Arrays and Numpy Special Values.mp4 20.3 MB
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 17 total files

Description


Python Numpy For Data Science (2025)

https://WebToolTip.com

Published 4/2025
Created by Daniel Yoo
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 14 Lectures ( 2h 23m ) | Size: 856 MB

Mastering Numerical Computing Foundations for Efficient Data Analysis

What you'll learn
Master the core features of NumPy, including arrays, indexing, slicing, reshaping, and broadcasting.
Write efficient, vectorized Python code for numerical and data-driven tasks, avoiding slow loops.
Apply NumPy to real-world data science workflows, including descriptive statistics, simulations, and linear algebra operations
Build a strong foundation for advanced data science libraries like pandas, scikit-learn, and TensorFlow by understanding NumPy under the hood.

Requirements
No Programming Experience Needed: You will learn everything you need to know throughout the course.

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