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Home » Technology » Big Data & Data Science » Intermediate Python

Course Skill Level:

Intermediate

Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    INTEPYS21J12

Who should attend & recommended skills:

Those with basic Python experience

Who should attend & recommended skills

  • Those seeking to apply their Python knowledge to daily programming needs
  • Python: Ability to write simple Python scripts using basic data types, program structures, and the standard Python library required

About this course

Once students have mastered the basics of Python via our introductory Python course or their own work, it’s time to move on to applying Python to daily programming needs. This course picks up where Python 1 leaves off, covering some topics in more detail, and adding many new ones, with a focus on enterprise development.
This is a hands-on programming class. All concepts are reinforced by informal practice during the lecture followed by lab exercises. Many labs build on earlier labs, which helps students retain the earlier material.

Skills acquired & topics covered

  • All attendees will learn to use Python to:
  • Leverage OS services
  • Code graphical interfaces for applications
  • Create modules
  • Create and run unit tests
  • Define classes
  • Interact with network services
  • Query databases
  • Process XML and JSON data

Course breakdown / modules

  • Data types
  • Sequences
  • Mapping types
  • Program structure
  • Files and console I/O
  • Conditionals
  • Loops
  • Builtins

  • The os module
  • Environment variables
  • Launching external commands
  • Walking directory trees
  • Paths, directories, and filenames
  • Working with file systems
  • Dates and times

  • The Zen of Python
  • Common idioms
  • Named tuples
  • Useful types from collections
  • Sorting
  • Lambda functions
  • List comprehensions
  • Generator expressions
  • String formatting

  • Initialization code
  • Namespaces
  • Executing modules as scripts
  • Documentation
  • Packages and name resolution
  • Naming conventions
  • Using imports
  • Distributing modules

  • Defining classes
  • Instance methods and data
  • Initializers
  • Class methods
  • Static methods
  • Inheritance
  • Multiple inheritance

  • Implicit properties
  • globals() and locals()
  • Attributes
  • The inspect module
  • Callable classes
  • Decorators
  • Monkey patching

  • Analyzing programs with pylint
  • unittest overview
  • Simple unit tests
  • Creating and running test suites
  • Profilng and Benchmarking

  • The DB API
  • Available Interfaces
  • Connecting to a server
  • Creating and executing a cursor
  • Fetching data
  • Parameterized statements
  • Metadata
  • Transaction control

  • Overview
  • Qt Architecture
  • Using designer
  • Standard widgets
  • Event handling
  • Extras

  • Using requests
  • Sending email
  • Working with binary data
  • Consuming RESTful services

  • The threading module
  • Sharing variables
  • The queue module
  • The multiprocessing module
  • Creating pools
  • About async programming

  • Working with XML
  • XML modules in Python
  • Getting started with ElementTree
  • Parsing XML
  • Updating an XML tree
  • Creating a new document
  • About JSON
  • Reading JSON
  • Writing JSON
  • Reading/writing CSV files

  • About non-Python modules
  • Using ctypes
  • Setting paramter/return types
  • About Cython and Numba

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