Scientific Programming Using Python

About This Course: Using Python programming language, this course teaches you to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions that are drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introductory course moves from the basics to advanced concepts, enabling learners to quickly gain proficiency. It covers general programming concepts such as loops and functions, and moves onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation. This course represents a powerful introduction for those requiring a solid foundation in Python programming.

 

Training Cost: ₦ 55,000.00    

 

Background on Python:  Python is general-purpose and powerful programming language developed by Guido van Rossum in 1989. It is a high-level programming language that has a rich variety of native data structures such as lists, tuples, sets and dictionaries. Some of the main advantages of Python programming language include the following:

• It uses simple syntax, which makes writing Python programs fast and lowers the opportunities for bugs to creep in.

• It’s free – Python is free of cost and open source, unlike commercial offerings such as Mathematica or MATLAB.

• Cross-platform support: Python is available for commonly available computer operating systems, including Windows, Unix, Linux and Mac OS X.

• Python has a large library of modules and packages that are available as part of its “standard library” that extends its functionality. Others modules and packages can be downloaded separately at no cost, including the NumPy, SciPy and Matplotlib libraries used in scientific computing.

• Python is relatively easy to learn.

• Python is flexible: It contains the best features from the procedural, object-oriented and functional programming paradigms.

 

Success Stories: Some Science and Engineering Companies and Institutions around the World Using Python

Biology: Simulating Bio-molecules with Python

Bioinformatics: AstraZeneca Uses Python for Collaborative Drug Discovery

GIS and Mapping: Python in The Blind Audio Tactile mapping System

Energy Efficiency: Carmanah Lights the Way with Python

Marine: Maritime Industry Increases Efficiency with Python

Simulation: IronPython at Resolver Systems

 

Course Dates: 
Monday 11 December 2017 – Tuesday 12 December 2017

 

Course Time: 9 am to 5 pm

 

 

Venue: 

Slingshot Tech Limited, 35 Moloney Street, Lagos Island, Lagos.

 

Prerequisites: Basic IT skills. A first year undergraduate-level mathematics or background in engineering, physics, mathematics, economics, finance or another applied science with some mathematical content will be helpful. No previous programming experience is required.

 

MAKING A BOOKING

Information on how to book one or more of our courses can be found here .

 

Some Applications of Programming in Python

   •     In Physics: Magnetic Field Lines

 

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   •     In Finance: Simulation of Financial Models

 

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   •     In Finance: Investment Portfolio Optimisation

 

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   •     In Neuroscience: Neural Data Analysis

 

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   •     In Fluid Dynamics: Magnetic Field Lines

 

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   •     In Graphical Visualization: Magnetic Field Lines

 

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Availability Type: 
AVAILABLE COURSES
Key Features
Assumes no prior knowledge or experience of programming
Using scientifically relevant and practical examples throughout enables students to quickly put their knowledge into practice
Several examples relevant to mathematics, physics, chemistry, biology, statistics, geographic information systems
Several examples relevant to engineering, science and economics
Lessons
Introduction
Values and Variables
Expressions and Arithmetic
Conditional Execution
Iteration
Tuplets, Lists, and Dictionaries
Using and Writing Functions and Modules
Packages and Python Installation Considerations: IPython and Anaconda
Plotting with Matplotlib module
Using the NumPy