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| User | Reason | Date |
|---|
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| File | Size |
|---|---|
| Get Bonus Downloads Here.url | 204 bytes |
| 1. Create Your First NumPy Arrays.html | 5.0 KB |
| 1. Introduction (Description).html | 1.1 KB |
| 1. Introduction.mp4 | 56.8 MB |
| 1. NumPy_for_Beginners.mp4 | 15.5 MB |
| 2. Create and Inspect Array-Like Data.html | 6.5 KB |
| 2. Creating and Inspecting Array Structures (Description).html | 1.9 KB |
| 2. Creating and Inspecting Array Structures.mp4 | 29.6 MB |
| 3. Indexing & Slicing Arrays.mp4 | 27.3 MB |
| 3. Select Values, Rows, Columns, and Slices.html | 8.5 KB |
| 4. Calculate Profit, Margins, and Basic Statistics.html | 9.8 KB |
| 4. Math Operations and Statistics with Array-Like Data.mp4 | 100.7 MB |
| 5. Boolean Filtering and CSV Mini Project (Description).html | 1.1 KB |
| 5. Boolean Filtering and CSV Mini Project.mp4 | 26.7 MB |
| 5. Filter Sales Data and Calculate Business Metrics.html | 10.9 KB |
| Bonus Resources.txt | 102 bytes |
| Uploaded by freecoursewb | Size 256.8 MB | Health [ 9 /8 ] | Added 2026-06-03 |
| Uploaded by freecoursewb | Size 870.2 MB | Health [ 0 /0 ] | Added 2023-10-26 |
| Uploaded by freecoursewb | Size 344.1 MB | Health [ 0 /3 ] | Added 2023-06-02 |
| Uploaded by freecoursewb | Size 197.2 MB | Health [ 19 /1 ] | Added 2023-06-02 |
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SOURCE: Udemy - NumPy for Beginners - Practical Data Analysis with Python
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MEDIAINFO
NumPy for Beginners: Practical Data Analysis with Python
https://WebToolTip.com
Published 5/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 37m | Size: 256.76 MB
Learn NumPy arrays, slicing, statistics, filtering, and CSV analysis with practical Python exercises.
What you'll learn
Create one-dimensional and two-dimensional NumPy arrays for numerical data analysis.
Inspect NumPy arrays using shape, size, dtype, astype, and tolist.
Select values, rows, columns, and data ranges using indexing and slicing.
Perform scalar math, vector math, and basic statistical calculations with NumPy.
Filter data using Boolean conditions and multiple logical operators.
Load and analyze CSV data using NumPy through a practical sales analysis project.
Requirements
Basic Python knowledge is recommended, such as variables, lists, functions, and simple arithmetic.
No previous NumPy, data science, Pandas, or machine learning experience is required.
Students should be able to run Python code using Google Colab, Jupyter Notebook, or VS Code.
