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Python training course

A comprehensive introduction to Python - a relatively simple and therefore popular language widely used for rapid application development, testing and data analytics

4.8 out of 5 average

(see feedback)
  • Train a team
  • Customised content
  • Free tasters
  • Next on 22 Nov
  • £2,000.00+vat
  • Live / Virtual

JBI training course London UK

  • Gain an introduction to Python from its origins 
  • Learn about Python techniques and features 
  • Apply Basic Data types to integers, floating-points and strings
  • Learn how to use Python aggregated types to manage large data sets
  • Gain an understanding of Flow Control for code layout and clarity
  • Discover how to make decisions with the IF statement 
  • Use Python functions for parameters and variables 
  • Acquire knowledge in functional programming          
  • Modularise code to write and use larger Python programs
  • Master how to improve your code robustness by handling exceptions    
  • Take advantage of file handling to manipulate text and binary files 
  • Implement Agile and test driven development methods to write clean and readable Python code                      
  • Apply powerful text processing with regular expressions            
  • Gain an overview of Object-Oriented Programming with classes
  • Acquire knowledge on classes to help with dynamic typing and code re-usability

A taster video for Python Remote Training


Our Python training course will show you why, in programming terms, Python has every angle covered. It is used extensively in the cloud, and is one of the first languages to support Google App Engine. Python is popular among scientific communities through the SciPy package. Its simplicity makes it easy enough for beginners who use a Raspberry Pi, which was originally targeted to run Python. System administrators, looking for more than shell scripts, also take to Python, given the extensive library support available.

Python is a dynamic language, object-oriented and has features enabling its use as a functional language.  It also supports meta-programming structures and aspects of Lisp and Haskell.

Python can be used very effectively for rapid test script development. Through hands-on practicals, you will see why Python is simple enough to be used to teach young children to program,  and advanced enough to be used by MIT to teach computer science. It is perhaps the most widely used dynamic language with many high quality, open source libraries and frameworks.  

JBI training course London UK
JBI training course London UK

Quants, Data Scientists, Data Analysts, Mathematicians, System Testers and Shell Scripters who are new to Python


Python In action : We all know YouTube as the place to upload cat videos and fails. As one of the most popular websites in existence, it provides us with endless hours of video entertainment. The Python programming language powers it and the features we love.


4.8 out of 5 average

"The course was very comprehensive with clear examples and good exercises. It was a good to understand the capabilities and basics of python. The material is very clear. I like the fact that additional material has been shared for practice."

EW, Electronic Engineer, Python, January 2021

JBI training course London UK

Python Features

  •     Ease and economy of development
  •     Scalability
  •     Extensibility
  •     Adoption by major users

Introduction to Python

  •     Python history
  •     Interactive and scripted execution
  •     Dynamic typing examples and uses

Basic Data Types         

  •     Arithmetic on integers and longs
  •     Overflow-free arithmetic
  •     Using floating point for fractional values
  •     Using Decimal for precise decimal calculations
  •     Strings: indexing, slicing and formatting

Python aggregated types

  •     Lists and tuples: accessing information by position
  •     Modifying and appending to lists by index or slice
  •     Operations on lists: comparison and sorting
  •     List comprehensions for more compact code
  •     Managing large data sets with generators

Flow Control    

  •     Making decisions with the if statement
  •     Python code layout and clarity
  •     Iterating with the for and while constructs
  •     Writing your own iterators and generators



  •     Parameters: positional, named and default arguments
  •     Variable length argument lists
  •     Functional programming: functions as arguments and return values
  •     Using lambda functions to simplify code

Larger Programs and Modularisation

  •     Writing Python modules to modularise code
  •     Using the import statement to use Python modules
  •     Customising the import search path
  •     Grouping modules into packages

Improving code robustness by handling exceptions 

  •     The importance of avoiding unhandled errors
  •     Using the try/except/else and finally construct
  •     Raising exceptions
  •     Using custom exceptions for a better user experience

File handling    

  •     Opening files for read and/or write
  •     Managing file handles correctly
  •     Reading and writing text and binary files
  •     Performing random access


Agile and Test Driven development   

  •     Improving code quality and delivery with unit testing
  •     Th Python unit testing libraries
  •     Using unittest, PyTest, Doctest
  •     Using umbrella test classes to integrate different testing approaches

Powerful text processing with regular expressions    

  •     Expressing powerful abstract text patterns with metacharacters
  •     Using capturing to extract patterns from text
  •     Substituting text patterns with fixed or dynamic replacement patterns

Object oriented programming with classes    

  •     Understanding the power of OOP using abstract data types
  •     Defining abstract data types using classes
  •     Writing class member and static functions
  •     Understanding the class and object structure
  •     Exploiting Python’s dynamic class and object behaviour

More on classes    

  •     Using inheritance for code reusability
  •     Further enhancing reusability through polymorphism
  •     Using Python dynamic typing to change types at run time
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