**NumPy Data Science Essential Training**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3h 54m | 606 MB

NumPy Data Science Essential Training introduces the beginning to intermediate data scientist to NumPy, the Python library that supports numerical, scientific, and statistical programming, including machine learning. The library supports several aspects of data science, providing multidimensional array objects, derived objects (matrixes and masked arrays), and routines for math, logic, sorting, statistics, and random number generation. Here Charles Kelly shows how to work with NumPy and Python within Jupyter Notebook, a browser-based tool for creating interactive documents with live code, annotations, and even visualizations such as plots. Learn how to create NumPy arrays, use NumPy statements and snippets, and index, slice, iterate, and otherwise manipulate arrays. Plus, learn how to plot data and combine NumPy arrays with Python classes, and get examples of NumPy in action: solving linear equations, finding patterns, performing statistics, generating magic cubes, and more.

Topics include:

- Using Jupyter Notebook
- Creating NumPy arrays from Python structures
- Slicing arrays
- Using Boolean masking and broadcasting techniques
- Plotting in Jupyter notebooks
- Joining and splitting arrays
- Rearranging array elements
- Creating universal functions
- Finding patterns
- Building magic squares and magic cubes with NumPy and Python

1 Welcome

2 What you should know

3 NumPy, data science, IMQAV

4 How to use the exercise files

5 Install software

6 Introduction to Jupyter Notebook

7 Notebook basics

8 Markdown

9 Beautiful mathematics typesetting

10 Launch Jupyter Notebook

11 Create arrays from Python structures

12 Intrinsic creation using NumPy methods

13 linspace, zeros, ones, data types

14 Slice arrays

15 Boolean mask arrays

16 Broadcasting

17 Structured and record arrays

18 Inline plotting

19 Figures and subplots

20 Multiple lines, single plot

21 Tick marks, labels, and grids

22 Plot annotations

23 Pie charts and bar charts

24 Beautiful plots, the gallery

25 Views and copies

26 Array attributes

27 Add and remove elements

28 Join and split arrays

29 Array shape manipulation

30 Rearrange array elements

31 Transpose like operations

32 Tiling arrays

33 Universal functions

34 Pythagorean triangles

35 Linear algebra

36 Finding patterns

37 Statistics

38 Brain teasers

39 Magic squares and NumPy

40 Adjacency matrix

41 Magic characteristics

42 Build magic cubes

43 Next steps

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