NumPy refers to an open source library that is used for the Python programming language. It is one of the tools used by Python for scientific computing and data analysis purposes. It is mainly used in matrix computing and is used to perform mathematical and logical operations, fourier transform, shape manipulation, linear algebra and random number generation. It was created by Travis Oliphant in 2005 and currently has many contributors for it. A typical NumPY package includes a powerful N-dimensional array, sophisticated functions and tools for integrating C/C++.
NumPy is an essential tool for other scientific Python packages such as scikit-learn, skiPy and OpenCV. As a programmer, it is crucial to be acquainted with all of the Python computing libraries in order to be smart enough to know when to choose one over the other. This Python NumPy: Scientific Computing with Python course provides a thorough understanding of NumPy’s features and when to use them. This course will walk you through the importance of NumPy and to develop an understanding of the scenarios in which NumPy is most useful. You will also be able to get grips on topics such as matrices, deviations, Eigen values, and covariance through this course. This certificate can be that additional qualification that paves way for you to land a rewarding job in the IT or programming field. Therefore, mastering the features of NumPy will help you in kick starting a career in programming and to improve your existing expertise in programming.
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The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course. The course is self-paced and you can complete it in stages, revisiting the lectures at any time.
Learners must be age 16 or over and should have a basic understanding of the English Language, numeracy, literacy, and ICT.
At the end of the course, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed the course.
Those who successfully complete the course will be awarded the Python NumPy: Scientific Computing with Python certificate by CPD & iAP. The qualification will make you valuable to employers, and your motivation at gaining new skills will be recognised.
Brett Romero is a software engineer and entrepreneur. He has started several businesses, including Bitesize Business School, where he writes about applying business principles to the real world and solving technology issues that every online business faces. His first business, Cygen, was a web design consulting business, from which he also created his first product – a JavaScript-based chatroom web application.
Brett has written desktop applications in languages running from Delphi to C# winforms/WPF, as well as built ASP.NET webforms and MVC based applications. When the iOS SDK was first introduced, he bought a Macbook and began building apps for the iPhone. He currently has seven apps in the iTunes App Store.
Brett holds an MBA from ASU (’14). His undergraduate major was in mathematics. Before graduating, he decided to get a business degree. He continued taking mathematics courses at the University of Washington after completing his undergraduate degree.
This certificate in Python NumPy: Scientific computing with Python can be used as an additional qualification and help improve your chances of landing a high paying job position in the IT or programming field. This course can also be used to prove your eligibility for winning progressive opportunities such as job promotions and salary increments from your organisation. Few of the job positions you can land of with this course, along with the average UK salary per annum according to https://www.payscale.com/career-path-planner, is given below,
1: Introduction | |||
Introduction | |||
2: What is Numpy | |||
What is Numpy | |||
3: Basic Mathematics | |||
Basic Mathematics | |||
4: Tour of Numpy I | |||
Tour of Numpy I | |||
5: Tour of Numpy II | |||
Tour of Numpy II | |||
6: Matrices | |||
Matrices | |||
7: Deviations | |||
Deviations | |||
8: Eigen Values | |||
Eigen Values | |||
9: Covariance | |||
Covariance | |||
10: Project (Determinants) | |||
Project (Determinants) | |||
11: Summary | |||
Summary |
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