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Numerical Computing in Python with NumPy

Self-paced videos, Lifetime access, Study material, Certification prep, Technical support, Course Completion Certificate

Provided by Uplatz

Summary

Price
£19 inc VAT
Study method
Online, On Demand What's this?
Duration
24.5 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed courses certificate of completion - Free
  • Uplatz Certificate of Completion - Free
Additional info
  • Tutor is available to students
3 students purchased this course

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Overview

Uplatz offers this comprehensive course on Numerical Computing in Python with NumPy. It is a self-paced video course consisting of recorded lectures. You will be awarded Course Completion Certificate at the end of the course.

Python is a popular object-oriented programming language that is simple to learn and use. It can operate on a variety of operating systems, including Windows, Linux, and Mac, making it a popular choice in the Data Analytics field. You may work in the Data Science, Machine Learning, Building real-world applications with Python, Big Data Hadoop environment, AI applications, and analytics after completing this Advanced Python Programming course.

NumPy is a fundamental library for numerical computing in Python. It stands for "Numerical Python" and provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. NumPy forms the foundation for many other Python data science libraries and is a core tool for scientific computing and data analysis.

Key features of NumPy:

  1. N-dimensional Arrays: NumPy provides an array object called ndarray, which is a multi-dimensional container for homogenous data. These arrays can be 1-dimensional, 2-dimensional, or even more dimensions, and they allow efficient storage and manipulation of large datasets.

  2. Array Operations: NumPy supports a wide range of element-wise array operations, making it easy to perform mathematical and logical operations on entire arrays without the need for explicit loops. This feature, known as vectorization, results in faster and more concise code.

  3. Broadcasting: NumPy allows broadcasting, which is a mechanism to perform operations on arrays of different shapes. It automatically extends the smaller array to match the shape of the larger array, making it possible to perform element-wise operations on arrays of different dimensions.

  4. Mathematical Functions: NumPy provides a vast collection of mathematical functions for array computations, including basic arithmetic, trigonometric, logarithmic, statistical, and linear algebra functions.

  5. Integration with Other Libraries: NumPy integrates seamlessly with other data science and numerical computing libraries in Python, such as SciPy, Matplotlib, and pandas, creating a powerful ecosystem for data analysis, visualization, and scientific computing.

Certificates

Reed courses certificate of completion

Digital certificate - Included

Will be downloadable when all lectures have been completed

Uplatz Certificate of Completion

Digital certificate - Included

Course Completion Certificate by Uplatz

Curriculum

1
section
96
lectures
24h 31m
total

    • 1: 1.1 INTRODUCTION-TO-NUMPY 22:20
    • 2: 1.2 NUMPY TUTORIAL BASICS 17:06
    • 3: 1.3 NUMPY ATTRIBUTES AND FUNCTIONS 24:43
    • 4: 1.4 CREATING ARRAYS FROM EXISTING DATA 24:52
    • 5: 1.5 CREATING ARRAY FROM RANGES 28:44
    • 6: 1.6 INDEXING AND SLICING IN NUMPY 15:39
    • 7: 1.7 ADVANCED SLICING IN NUMPY 29:55
    • 8: 1.8 APPEND AND RESIZE FUNCTIONS 25:20
    • 9: 1.9.1 NDITER FUNCTION AND BROADCASTING 24:23
    • 10: 1.9.2 NUMPY BROADCASTING - part 1 10:42
    • 11: 1.9.3 NUMPY BROADCASTING - part 2 07:41
    • 12: 1.9.4 NUMPY BROADCASTING - part 3 07:12
    • 13: 1.10 NDITER FUNCTION 26:17
    • 14: 2.1 ARRAY MANIPULATION FUNCTIONS 29:19
    • 15: 2.2 NUMPY UNIQUE() 16:53
    • 16: 2.3.1 NUMPY DELETE() - part 1 10:24
    • 17: 2.3.2 NUMPY DELETE() - part 2 05:45
    • 18: 2.4 NUMPY INSERT FUNCTION 10:23
    • 19: 2.5 NUMPY RAVEL() SWAPAXES() 14:44
    • 20: 2.6.1 SPLIT FUNCTION 11:54
    • 21: 2.6.2 HSPLIT() 12:18
    • 22: 2.6.3 VSPLIT() 07:10
    • 23: 2.7 LEFT SHIFT AND RIGHT SHIFT FUNCTIONS 11:44
    • 24: 2.8 NUMPY TRIGONOMETRIC FUNCTIONS 14:40
    • 25: 2.9 NUMPY ROUND FUNCTIONS 14:16
    • 26: 2.10 NUMPY ARITHMETIC FUNCTIONS 07:43
    • 27: 2.11 NUMPY POWER AND RECIPROCAL FUNCTIONS 07:51
    • 28: 2.12 NUMPY POWER AND MOD FUNCTIONS 06:36
    • 29: 2.13 NUMPY IMAG() REAL() 08:07
    • 30: 3. NUMPY CONCATENATE() 07:51
    • 31: 4.1 NUMPY STATISTICAL FUNCTIONS - AMIN AND AMAX 06:22
    • 32: 4.2 NUMPY STATISTICAL FUNCTIONS - MEAN MEDIAN PTP() 22:43
    • 33: 4.3 NUMPY AVERAGE FUNCTION 21:25
    • 34: 5.0 NUMPY SORT SEARCH COUNTING FUNCTIONS 20:10
    • 35: 5.1 NUMPY SEARCH SORT ALGORITHMS 06:55
    • 36: 5.2 SORT() 06:11
    • 37: 5.3 NUMPY SORT FUNCTION 16:40
    • 38: 5.4 NUMPY ARGSORT() 07:12
    • 39: 5.5 NONZERO WHERE 14:24
    • 40: 5.6 EXTRACT 06:32
    • 41: 5.7 ARGMAX() AND ARGMIN() 07:35
    • 42: 6. BYTESWAP COPIES AND VIEWS 25:00
    • 43: 7.1 STRFUNCTIONS IN NUMPY 13:32
    • 44: 7.2 STRING FUNCTION IN NUMPY ADD() MULTIPLY() 05:59
    • 45: 7.3 NUMPY CENTER() 08:19
    • 46: 7.4 CAPITALIZE() CENTER() IN NUMPY 12:17
    • 47: 7.5 STRING FUNCTIONS 1 17:37
    • 48: 7.6 STRING FUNCTIONS 2 08:27
    • 49: 8. NUMPY MATRIX LIBRARY 18:25
    • 50: 9. NUMPY JOINING ARRAYS 21:01
    • 51: 10.1 LINEAR ALGEBRA 1 13:55
    • 52: 10.2 LINEAR ALGEBRA 2 13:39
    • 53: 10.3 LINEAR ALGEBRA 3 11:21
    • 54: 10.4 LINEAR ALGEBRA 4 06:46
    • 55: 10.5 LINEAR ALGEBRA 5 08:40
    • 56: 10.6 LINEAR ALGEBRA 6 16:45
    • 57: 10.7 LINEAR ALGEBRA 7 16:32
    • 58: 11.1 RANDOM MODULE 1 14:49
    • 59: 11.2 RANDOM MODULE 2 19:02
    • 60: 11.3 RANDOM MODULE 3 22:42
    • 61: 11.4 RANDOM MODULE 4 07:14
    • 62: 11.5 RANDOM MODULE 5 13:49
    • 63: 11.6 RANDOM MODULE 6 12:56
    • 64: 11.7 RANDOM MODULE 7 11:18
    • 65: 11.8 RANDOM MODULE 8 11:22
    • 66: 11.9 RANDOM MODULE 9 15:03
    • 67: 11.10 RANDOM MODULE 10 10:35
    • 68: 11.11 RANDOM MODULE 11 10:04
    • 69: 11.12 RANDOM MODULE 12 09:21
    • 70: 11.13 RANDOM MODULE 13 43:00
    • 71: 11.14 RANDOM MODULE 14 21:26
    • 72: 11.15 RANDOM MODULE 15 22:47
    • 73: 11.16 RANDOM MODULE 16 09:20
    • 74: 11.17 RANDOM MODULE 17 20:18
    • 75: 11.18 RANDOM MODULE 18 30:27
    • 76: 11.19 RANDOM MODULE 19 27:58
    • 77: 11.20 SECRETS MODULE 1 21:42
    • 78: 11.21 SECRETS MODULE 2 16:29
    • 79: 11.22 RANDOM MODULE UNIFORM FUNCTION 22:14
    • 80: 11.23 RANDOM MODULE GENERATE NUMBER EXCEPT K 13:57
    • 81: 11.24 SECRETS MODULE GENERATE TOKENS 08:29
    • 82: 11.25 RANDOM MODULE GENERATE BINARY STRING 21:46
    • 83: 12.1 NUMPY MODULE REVISE 1 15:53
    • 84: 12.2 NUMPY MODULE REVISE 2 12:36
    • 85: 12.3 NUMPY INDEXING 14:56
    • 86: 12.4 NUMPY BASIC OPERATIONS 14:41
    • 87: 12.5 UNARY OPERATORS IN NUMPY 08:41
    • 88: 12.6 BINARY OPERATORS IN NUMPY 11:24
    • 89: 12.7 UNIVERSAL OPERATORS IN NUMPY 11:57
    • 90: 12.8 NUMPY FILTER ARRAYS 16:35
    • 91: 13.1 NUMPY MODULE PROJECTS_1 21:24
    • 92: 13.2 NUMPY MODULE PROJECTS_2 19:26
    • 93: 13.3 NUMPY MODULE PROJECTS_3 19:44
    • 94: 13.4 NUMPY MODULE PROJECTS_4 10:07
    • 95: 13.5 NUMPY MODULE PROJECTS_5 15:37
    • 96: 13.6 NUMPY MODULE PROJECTS_6 22:12

Course media

Description

Numerical Computing in Python with NumPy - Course Syllabus

Module 1: Getting Started with NumPy

  • Introduction to NumPy and its features
  • Installing NumPy and setting up the Python environment
  • NumPy arrays: Creating, indexing, and slicing arrays
  • Broadcasting: Understanding how NumPy handles array shapes
  • Basic array operations: Arithmetic, aggregation, and element-wise functions

Module 2: Working with Multi-dimensional Arrays

  • Multi-dimensional arrays and their properties
  • Array reshaping and stacking
  • Universal functions (ufuncs): Applying functions element-wise
  • Array broadcasting: Understanding how broadcasting works
  • Masked arrays: Handling missing or invalid data

Module 3: Advanced NumPy Operations

  • Array manipulation: Concatenation, splitting, and resizing arrays
  • Advanced array indexing and slicing techniques
  • Fancy indexing: Selecting specific elements or subsets from arrays
  • Linear algebra operations with NumPy: Dot products, matrix operations
  • Statistical computing with NumPy: Mean, median, variance, and more

Module 4: Data Processing and Visualization with NumPy

  • Reading and writing data using NumPy
  • Introduction to NumPy's subpackage 'numpy.random'
  • Simulation and sampling using random numbers
  • Vectorized computation: Benefits of using NumPy over loops
  • Basic data visualization with NumPy and Matplotlib

Who is this course for?

Everyone

Requirements

Passion and determination to achieve your goals!

Career path

  • Python Developer
  • Lead Python Developer
  • Data Scientist
  • Machine Learning Engineer
  • Python Developer API AWS
  • Web Developer - Python
  • Data Engineer - Python, Spark, Pyspark, Data​/ETL Pipelines, Git
  • Python Developer - Technologist
  • Cloud Engineer - Go/Python
  • Data Analyst
  • Data Engineer
  • Senior Software Engineer (MongoDB/Python)
  • Embedded Python Developer
  • DevOps Engineer
  • DevOps CI/CD Automation Engineer

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Provider

Uplatz

Uplatz is leading global provider of IT & Technology training.

We have a strong network of qualified and experienced tutors. Uplatz provides training on cutting-edge technologies such as Data Science, Machine Learning, AWS, Microsoft Azure, Google Cloud, IBM Cloud, Data Engineering, Python, R, Java, SAP, Oracle, SAS, Salesforce, Web Development Stack, JavaScript, ReactJS, AngularJS, NodeJS, JSP & Java Servlets, MongoDB, BI Tools such as Tableau, Spotfire, Power BI, DW & ETL tools such as Informatica, IBM DataStage, Talend, DevOps, Project Management, Software Testing, and many more. 

We provide training courses in both online formats - 1) live tutor-led, 2) self-paced videos

We feel proud to say that we are making learning affordable by keeping prices of all our courses very low. Our course prices are listed at almost 90% discounted rate from average market price.

 

Our Vision

  • To become a global leader in the learning sector by providing training on job-oriented technologies 

 

Our Mission

  • To provide high-quality training on industry-demanded technologies
  • To make learning affordable for the masses by keeping our prices extremely low
  • To help our talented students get a high-paying job in the market

 

Will I get a Certificate of Course Completion?

Course Completion Certificate is awarded by Uplatz

 

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Will I be provided study material, tutor notes and practice assignments?

Uplatz provides tutor notes, practice assignments, practice sessions, and a lot of useful study material for free. This will help you in job interviews and certification exams.

 

In case of tutor-led online training, will I have access to the recorded sessions? 

Yes. All live classes get recorded and life-time access on the recordings is provided.

 

Course Search and Enrollment

Simply search the course of your choice! We have a portfolio of more than 1000 courses with the premium ones highlighted clearly. Just search for the course that you want to take or simply use our online course finding tool to help you choose the best courses as per your needs and market demand.

 

Uplatz differentiates itself by providing extremely affordable learning to all and that too in the comfort of their homes.
Just contact us for a customized quote, your preferable timings, your affordability, and we'll work out the best course for you that will provide you not only a great return on investment but also to help you get a job with a premium salary. We'll also get you prepared for the certification exams.

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Legal information

This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
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