NumPy for Data Scientist
4.7 (5 ratings)
90 students enrolled

# NumPy for Data Scientist

Numeric python for Everyone.
New
4.7 (5 ratings)
90 students enrolled
Created by Sumit Khandelwal
Last updated 6/2020
English
Current price: \$13.99 Original price: \$19.99 Discount: 30% off
5 hours left at this price!
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This course includes
• 2.5 hours on-demand video
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll learn
• Multi-dimension array creation and perform the processing on multidimensional array
• Indexing of array
• Statistical functions
• Linear Algebra
• Matrix Operations and many more
Requirements
• Basic knowledge of Python Programming
Description

NumPy stands for numeric python which is a python package for the computation and processing of the multidimensional and single dimensional array elements.

With the revolution of data science, data analysis libraries like NumPy, SciPy, Pandas, etc. have seen a lot of growth. With a much easier syntax than other programming languages, python is the first choice language for the data scientist.

NumPy provides a convenient and efficient way to handle the vast amount of data. NumPy is also very convenient with Matrix multiplication and data reshaping. NumPy is fast which makes it reasonable to work with a large set of data.

There are the following advantages of using NumPy for data analysis.

1. NumPy performs array-oriented computing.

2. It efficiently implements the multidimensional arrays.

3. It performs scientific computations.

4. It is capable of performing Fourier Transform and reshaping the data stored in multidimensional arrays.

5. NumPy provides the in-built functions for linear algebra and random number generation.

Nowadays, NumPy in combination with SciPy and Matplotlib is used as the replacement to MATLAB as Python is more complete and easier programming language than MATLAB.

Who this course is for:
• Want to Learn Data Science
• Beginner Python Developer
Course content
Expand all 30 lectures 02:33:16
+ Array Creation and Processing
11 lectures 32:20
NumPy Ndarray
06:50
Finding the dimensions of the Array
03:10
Finding the data type of each array item
01:56
Finding the shape and size of the array
02:16
Reshaping the array objects
03:47
Slicing in the Array
01:45
Linspace
01:09
Finding square root and standard deviation
01:54
Arithmetic operations on the array
02:59
Array Concatenation
03:08
+ NumPy Datatypes and Array Creations
6 lectures 29:39
NumPy Datatypes
02:50
Numpy Array Creation
07:43
Numpy array from existing data_1
08:06
Numpy array from existing data_2
02:05
05:26
NumPy Array Iteration
03:29
+ Bitwise Operators and String Function in Numpy
2 lectures 25:24
NumPy Bitwise Operators
12:47
NumPy String Functions
12:37
+ Mathematical and statistical Functions
3 lectures 22:55
NumPy Mathematical Functions
09:26
Numpy statistical functions
09:53
Numpy statistical functions
03:36
+ NumPy Sorting, Searching , Copies and Views
3 lectures 13:34
NumPy Sorting
04:29
Numpy Searching
01:48
NumPy Copies and Views
07:17
+ NumPy Matrix Library and Linear Algebra
2 lectures 22:42
NumPy Matrix Library
08:08
NumPy Linear Algebra
14:34
+ Save function
2 lectures 04:21
Numpy Save Function
03:57
End Note
00:24