Custom Advanced Python Programming 2019.10.16

Target Audience

• This is an advanced Python course suitable for people who already have Python programming background.

Prerequisites

• Beginner Python course
• Experience with the basics of Python

Objectives

• To master the rich set of Python libraries and modules.
• Use Object Oriented programming techniques.
• Use various advanced programming techniques available in Python.

Course Format

• Duration of the course is 40 academic hours. (5 days 9:00-17:00).
• The course includes approximately 40% hands on lab work.

Syllabus

1. Collection types and operators
   • Lists
   • List slices
   • Tuples
   • Dictionaries (hashes)
   • Sets
   • Sorting
   • Queues
   • Stack
   • Collections
   • Mutable and immutable
2. Data types advanced programming techniques
   • Advanced uses of built-in data types (including slices on sequences).
   • Functional programming (lambda, map, reduce, filter, zip).
   • Comprehensions (list, dictionary, and set).
3. Object Oriented Programming in Python
   • Objects in Python
   • Classes
   • Instances
   • Scoping issues
   • Class methods
   • Instance methods
   • Properties
   • Overloading (with and without the operator module).
4. Iterators and Generators
   • Iterators (including sorted and reversed, and use of itertools)
   • Generators (including generator comprehensions and pipelines)
   • Decorators
5. Advanced programming techniques
   • Unit Test Framework (brief introduction)
   • The 'with' statement
   • Design patterns
6. Optimizations
   • Optimizing Python code
   • Parallel processing
   • Forks to processes and threads
   • Asynchronous programming in Python
   • Memory usage
   • Complexity Analyzis
   • Profiling code to find the bottleneck
   • Case Study: Reducing run-time from 24 hours to 5 minutes
7. Error and Exception handling
   • Creating non-fatal warnings
   • Catching exceptions
   • Handling exceptions
   • Throwing a new exception
   • The final block
   • Creating your own exception
8. Integration with .NET
   • Using .NET classes from Python
   • Parameter passing to .NET methods
9. Distributing Python-based applications
   • Distributable package in source code
   • Distributable compiled Python libraries
   • Stand-alone executable binaries
10. The Scientific libraries
    • NumPy (Array, Transformations)
    • Pandas (Series, Data Frames, Panels, API)
    • SciPy
    • Visualization and Plotting including 3D with Matplotlib and Seaborn
    • Comparing with Matlab and R
    • Integrating Python with Matlab

Contact

Contact: Gabor Szabo gabor@hostlocal.com
Phone: +972-54-4624648