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.

Enroll now: Python NumPy Programming and Project Development

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

POWERFUL N-DIMENSIONAL ARRAYS
- Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.
NUMERICAL COMPUTING TOOLS
- NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.
INTEROPERABLE
- NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.
PERFORMANT
- The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.
EASY TO USE
- NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.
OPEN SOURCE
- 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

INTRODUCTION TO NUMPY
NUMPY TUTORIAL BASICS
NUMPY ATTRIBUTES AND FUNCTIONS
CREATING ARRAYS FROM EXISTING DATA
CREATING ARRAYS FROM RANGES
INDEXING AND SLICING IN NUMPY
ADVANCED SLICING IN NUMPY
APPEND AND RESIZE FUNCTIONS
NDITER AND BROADCASTING
NUMPY BROADCASTING
NDITER FUNCTION
ARRAY MANIPULATION FUNCTIONS
NUMPY UNIQUE()
NUMPY DELETE()
NUMPY INSERT FUNCTION
NUMPY RAVEL AND SWAPAXES()
SPLIT FUNCTION
HSPLIT FUNCTION
VSPLIT FUNCTION
LEFTSHIFT AND RIGHTSHIFT FUNCTIONS
NUMPY TRIGONOMETRIC FUNCTIONS
NUMPY ROUND FUNCTIONS
NUMPY ARITHMATIC FUNCTIONS
NUMPY POWER AND RECIPROCAL FUNCTIONS
NUMPY MOD FUNCTION
NUMPY IMAG() AND REAL() FUNCTIONS
NUMPY CONCATENATE()
NUMPY STATISTICAL FUNCTIONS
STATISTICAL FUNCTIONS
NUMPY AVERAGE FUNCTION
NUMPY SEARCH SORT FUNCTIONS
SORT FUNCTION
NUMPY SORT FUNCTION
NUMPY ARGSORT()
NONZERO AND WHERE FUNCTIONS
EXTRACT FUNCTION
NUMPY ARGMAX ARGMIN()
BYTESWAP COPIES AND VIEWS
NUMPY STRING FUNCTIONS
NUMPY CENTER FUNCTION
CAPITALIZE AND CENTER()
NUMPY TITLE FUNCTION
STRING FUNCTIONS
NUMPY MATRIX LIBRARY
NUMPY JOIN ARRAYS
LINEAR ALGEBRA
RANDOM MODULE
SECRETS MODULE
RANDOM MODULE UNIFORM FUNCTION
RANDOM MODULE GENERATE NUMBER EXCEPT K
SECRETSMODULE GENERATE TOKENS
RANDOM MODULE GENERATE BINARY STRING
NUMPY MODULE REVISE
NUMPY INDEXING
NUMPY BASIC OPERATIONS
NUMPY UNARY OPERATORS
BINARY OPERATORS IN NUMPY
NUMPY UNIVERSAL FUNCTIONS
NUMPY FILTER ARRAYS
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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