Python NumPy Programming and Project Development

Python NumPy Programming and Project Development, available at $19.99, has an average rating of 4.4, with 94 lectures, based on 26 reviews, and has 6067 subscribers.

You will learn about Advanced Python programming with NumPy concepts and its application NumPy Module Projects – 6 full tutorials on project implementation using NumPy NumPy – Ndarray Object NumPy – Array Attributes NumPy – Array Creation Routines NumPy – Array from Numerical Ranges NumPy – Advanced Indexing NumPy – Broadcasting NumPy – Iterating over Array NumPy – Array Manipulation NumPy – Binary Operators NumPy – String Functions NumPy – Mathematical Functions NumPy – Arithmetic Operations NumPy – Statistical Functions NumPy – Sort, Search & Counting Functions NumPy – Copies & Views NumPy – Matrix Library NumPy – Linear Algebra This course is ideal for individuals who are Python Developers and Python Developers or Software Engineers Python or Data Scientists and Data Engineers or Anyone interested to make a career in programming, analytics, data science, machine learning or Solution Architects or Software Developers and Analysts or Application Developers – web and app or High Performance Application Python Developers or Cloud Computing Engineers or Data Consultants & Analysts or Senior Programmers or Individuals wishing to go beyond the basics of Python to develop sophisticated applications or Data Analytics Professionals or Full Stack Python Developers or Web Developers or Principal Statistical Programmers It is particularly useful for Python Developers and Python Developers or Software Engineers Python or Data Scientists and Data Engineers or Anyone interested to make a career in programming, analytics, data science, machine learning or Solution Architects or Software Developers and Analysts or Application Developers – web and app or High Performance Application Python Developers or Cloud Computing Engineers or Data Consultants & Analysts or Senior Programmers or Individuals wishing to go beyond the basics of Python to develop sophisticated applications or Data Analytics Professionals or Full Stack Python Developers or Web Developers or Principal Statistical Programmers.

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Summary

Title: Python NumPy Programming and Project Development

Price: $19.99

Average Rating: 4.4

Number of Lectures: 94

Number of Published Lectures: 94

Number of Curriculum Items: 94

Number of Published Curriculum Objects: 94

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Advanced Python programming with NumPy concepts and its application

NumPy Module Projects – 6 full tutorials on project implementation using NumPy

NumPy – Ndarray Object

NumPy – Array Attributes

NumPy – Array Creation Routines

NumPy – Array from Numerical Ranges

NumPy – Advanced Indexing

NumPy – Broadcasting

NumPy – Iterating over Array

NumPy – Array Manipulation

NumPy – Binary Operators

NumPy – String Functions

NumPy – Mathematical Functions

NumPy – Arithmetic Operations

NumPy – Statistical Functions

NumPy – Sort, Search & Counting Functions

NumPy – Copies & Views

NumPy – Matrix Library

NumPy – Linear Algebra

Who Should Attend

Python Developers and Python Developers

Software Engineers Python

Data Scientists and Data Engineers

Anyone interested to make a career in programming, analytics, data science, machine learning

Solution Architects

Software Developers and Analysts

Application Developers – web and app

High Performance Application Python Developers

Cloud Computing Engineers

Data Consultants & Analysts

Senior Programmers

Individuals wishing to go beyond the basics of Python to develop sophisticated applications

Data Analytics Professionals

Full Stack Python Developers

Web Developers

Principal Statistical Programmers

Target Audiences

Python Developers and Python Developers

Software Engineers Python

Data Scientists and Data Engineers

Anyone interested to make a career in programming, analytics, data science, machine learning

Solution Architects

Software Developers and Analysts

Application Developers – web and app

High Performance Application Python Developers

Cloud Computing Engineers

Data Consultants & Analysts

Senior Programmers

Individuals wishing to go beyond the basics of Python to develop sophisticated applications

Data Analytics Professionals

Full Stack Python Developers

Web Developers

Principal Statistical Programmers

A warm welcome to the Python NumPy Programming and Project Developmentcourse by Uplatz.

NumPy stands for Numerical Python and it is a core scientific computing library in Python. NumPy provides efficient multi-dimensional array objects and various operations to work with these array objects.

NumPy is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices. NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely. NumPy is written partially in Python, but most of the parts that require fast computation are written in C or C++.

Purpose of using NumPy

In Python we have lists that serve the purpose of arrays, but they are slow to process. NumPy aims to provide an array object that is up to 50x faster than traditional Python lists. The array object in NumPy is called ndarray, it provides a lot of supporting functions that make working with ndarray very easy. Arrays are very frequently used in data science, where speed and resources are very important.

NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently. This behavior is called locality of reference in computer science. This is the main reason why NumPy is faster than lists. Also it is optimized to work with latest CPU architectures.

NumPy is essentially a library consisting of multidimensional array objects and a collection of routines for processing those arrays. Using NumPy, mathematical and logical operations on arrays can be performed.

NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:

Extract, Transform, Load: Pandas, Intake, PyJanitor

Exploratory analysis: Jupyter, Seaborn, Matplotlib, Altair

Model and evaluate: scikit-learn, statsmodels, PyMC3, spaCy

Report in a dashboard: Dash, Panel, Voila

Features of NumPy

1. POWERFUL N-DIMENSIONAL ARRAYS

2. Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.

3. NUMERICAL COMPUTING TOOLS

4. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

5. INTEROPERABLE

6. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

7. PERFORMANT

8. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.

9. EASY TO USE

10. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.

11. OPEN SOURCE

12. Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

Using NumPy, a developer can perform the following operations ?

Mathematical and logical operations on arrays.

Fourier transforms and routines for shape manipulation.

Operations related to linear algebra. NumPy has in-built functions for linear algebra and random number generation.

Uplatzprovides this in-depth training on Python programming using NumPy. This NumPy course explains the concepts & structure of NumPy including its architecture and environment. The course discusses the various array functions, types of indexing, etc. and moves on to using NumPy for creating and managing multi-dimensional arrays with functions and operations. This Python NumPy course also discusses the practical implementation of NumPy to develop prediction models & projects.

NumPy Python Programming and Project Development – Course Syllabus

1. INTRODUCTION TO NUMPY

2. NUMPY TUTORIAL BASICS

3. NUMPY ATTRIBUTES AND FUNCTIONS

4. CREATING ARRAYS FROM EXISTING DATA

5. CREATING ARRAYS FROM RANGES

6. INDEXING AND SLICING IN NUMPY

7. ADVANCED SLICING IN NUMPY

8. APPEND AND RESIZE FUNCTIONS

9. NDITER AND BROADCASTING

10. NUMPY BROADCASTING

11. NDITER FUNCTION

12. ARRAY MANIPULATION FUNCTIONS

13. NUMPY UNIQUE()

14. NUMPY DELETE()

15. NUMPY INSERT FUNCTION

16. NUMPY RAVEL AND SWAPAXES()

17. SPLIT FUNCTION

18. HSPLIT FUNCTION

19. VSPLIT FUNCTION

20. LEFTSHIFT AND RIGHTSHIFT FUNCTIONS

21. NUMPY TRIGONOMETRIC FUNCTIONS

22. NUMPY ROUND FUNCTIONS

23. NUMPY ARITHMATIC FUNCTIONS

24. NUMPY POWER AND RECIPROCAL FUNCTIONS

25. NUMPY MOD FUNCTION

26. NUMPY IMAG() AND REAL() FUNCTIONS

27. NUMPY CONCATENATE()

28. NUMPY STATISTICAL FUNCTIONS

29. STATISTICAL FUNCTIONS

30. NUMPY AVERAGE FUNCTION

31. NUMPY SEARCH SORT FUNCTIONS

32. SORT FUNCTION

33. NUMPY SORT FUNCTION

34. NUMPY ARGSORT()

35. NONZERO AND WHERE FUNCTIONS

36. EXTRACT FUNCTION

37. NUMPY ARGMAX ARGMIN()

38. BYTESWAP COPIES AND VIEWS

39. NUMPY STRING FUNCTIONS

40. NUMPY CENTER FUNCTION

41. CAPITALIZE AND CENTER()

42. NUMPY TITLE FUNCTION

43. STRING FUNCTIONS

44. NUMPY MATRIX LIBRARY

45. NUMPY JOIN ARRAYS

46. LINEAR ALGEBRA

47. RANDOM MODULE

48. SECRETS MODULE

49. RANDOM MODULE UNIFORM FUNCTION

50. RANDOM MODULE GENERATE NUMBER EXCEPT K

51. SECRETSMODULE GENERATE TOKENS

52. RANDOM MODULE GENERATE BINARY STRING

53. NUMPY MODULE REVISE

54. NUMPY INDEXING

55. NUMPY BASIC OPERATIONS

56. NUMPY UNARY OPERATORS

57. BINARY OPERATORS IN NUMPY

58. NUMPY UNIVERSAL FUNCTIONS

59. NUMPY FILTER ARRAYS

60. NUMPY MODULE PROJECTS

Course Curriculum

Chapter 1: Introduction to NumPy

Lecture 1: Introduction to NumPy

Chapter 2: NumPy Tutorial Basics

Lecture 1: NumPy Tutorial Basics

Chapter 3: NumPy Attributes and Functions

Lecture 1: NumPy Attributes and Functions

Chapter 4: Creating Arrays

Lecture 1: Creating Arrays from Existing Data

Lecture 2: Creating Array from Ranges

Chapter 5: Indexing and Slicing in NumPy

Lecture 1: Indexing and Slicing in NumPy

Lecture 2: Advanced Slicing in NumPy

Chapter 6: Append and Resize Functions

Lecture 1: Append and Resize Functions

Chapter 7: Nditer Function and Broadcasting

Lecture 1: Nditer Function and Broadcasting

Chapter 8: NumPy Broadcasting

Lecture 1: NumPy Broadcasting – part 1

Lecture 2: NumPy Broadcasting – part 2

Lecture 3: NumPy Broadcasting – part 3

Chapter 9: Nditer Function

Lecture 1: Nditer Function

Chapter 10: NumPy Functions

Lecture 1: Array Manipulation Functions

Lecture 2: NumPy Unique()

Lecture 3: NumPy Delete() – part 1

Lecture 4: NumPy Delete() – part 2

Lecture 5: NumPy Insert Function

Lecture 6: Numpy RAVEL() SWAPAXES()

Lecture 7: SPLIT Function

Lecture 8: HSPLIT()

Lecture 9: VSPLIT()

Lecture 10: LEFT Shift and RIGHT Shift Functions

Lecture 11: NumPy Trigonometric Functions

Lecture 12: NumPy Round Functions

Lecture 13: NumPy Arithmetic Functions

Lecture 14: NumPy Power and Reciprocal Functions

Lecture 15: NumPy Power and Mod Functions

Lecture 16: NumPy IMAG() REAL()

Chapter 11: NumPy CONCATENATE()

Lecture 1: NumPy CONCATENATE()

Chapter 12: NumPy Statistical Functions

Lecture 1: NumPy Statistical Functions – AMIN and AMAX

Lecture 2: Statistical Functions – MEAN, MEDIAN, PTP()

Lecture 3: NumPy AVERAGE Function

Chapter 13: NumPy Search and Sort

Lecture 1: NumPy Sort, Search, Counting Functions

Lecture 2: NumPy Sort Function

Lecture 3: NumPy ARGSORT()

Lecture 4: Nonzero Where

Lecture 5: Extract

Lecture 6: ARGMAX() and ARGMIN()

Chapter 14: Byteswap Copies and Views

Lecture 1: Byteswap Copies and Views

Chapter 15: String Functions in NumPy

Lecture 1: STRFUNCTIONS in NumPy

Lecture 2: String Function in NumPy ADD() and MULTIPLY()

Lecture 3: NumPy CENTER()

Lecture 4: CAPITALIZE() CENTER() in NumPy

Lecture 5: String Functions 1

Lecture 6: String Functions 2

Chapter 16: NumPy Matrix Library

Lecture 1: NumPy Matrix Library

Chapter 17: NumPy Joining Arrays

Lecture 1: NumPy Joining Arrays

Chapter 18: Linear Algebra

Lecture 1: Linear Algebra – part 1

Lecture 2: Linear Algebra – part 2

Lecture 3: Linear Algebra – part 3

Lecture 4: Linear Algebra – part 4

Lecture 5: Linear Algebra – part 5

Lecture 6: Linear Algebra – part 6

Lecture 7: Linear Algebra – part 7

Chapter 19: Random Module and Secrets Module

Lecture 1: Random Module – part 1

Lecture 2: Random Module – part 2

Lecture 3: Random Module – part 3

Lecture 4: Random Module – part 4

Lecture 5: Random Module – part 5

Lecture 6: Random Module – part 6

Lecture 7: Random Module – part 7

Lecture 8: Random Module – part 8

Lecture 9: Random Module – part 9

Lecture 10: Random Module – part 10

Lecture 11: Random Module – part 11

Lecture 12: Random Module – part 12

Lecture 13: Random Module – part 13

Lecture 14: Random Module – part 14

Lecture 15: Random Module – part 15

Lecture 16: Random Module – part 16

Lecture 17: Random Module – part 17

Lecture 18: Random Module – part 18

Lecture 19: Random Module – part 19

Lecture 20: Secrets Module – part 1

Lecture 21: Secrets Module – part 2

Lecture 22: Random Module Uniform Function

Lecture 23: Random Module Generate Number Except K

Lecture 24: Secrets Module Generate Tokens

Lecture 25: Random Module Generate Binary String

Chapter 20: NumPy Module Revision

Instructors

Uplatz Training
Fastest growing global Technology & Cloud Training Provider

Rating Distribution

1 stars: 1 votes

2 stars: 2 votes

3 stars: 3 votes

4 stars: 15 votes

5 stars: 5 votes

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