Exceptional training for PYTHON DEVELOPERS

Python Training Course

Learn Python for use in Data Analysis and rapid Application Development

17 Sep London
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Capita Marks and Spencer Telefonica Cisco BBC Lloyds Sony

Python training course (code: PYTHONTEST)


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. It's 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 and through hands-on practicals, you will see why Python is simple enough to be used to teach young children to program,  advanced enough to be used by M.I.T. to teach computer science and is perhaps the most widely used dynamic language with many high quality, open source libraries and frameworks.  


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.



Python Features

    Ease and economy of development
    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
    The 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 object oriented programming 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


Python Features
Introduction to Python
Basic Data Types         
Python aggregated types
Flow Control      
Larger Programs and Modularisation
Improving code robustness by handling exceptions         
File handling    
Agile and Test Driven development                        
Powerful text processing with regular expressions            
Object oriented programming with classes                
More on classes 

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17th Sep 2019 - 3 days £1500

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