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Course Outline

Introduction

  • Versatility of Python for tasks ranging from data analysis to automated web extraction

Data Structures and Operations in Python

  • Integers and floating-point numbers
  • String and byte handling
  • Tuples and lists
  • Dictionaries and ordered dictionaries
  • Sets and immutable sets
  • Data frames (pandas)
  • Type conversions

Object-Oriented Programming Principles in Python

  • Inheritance mechanisms
  • Polymorphism
  • Static classes
  • Static methods
  • Decorators
  • Additional concepts

Data Analysis Using Pandas

  • Data cleansing techniques
  • Leveraging vectorized operations in pandas
  • Data manipulation
  • Sorting and filtering datasets
  • Aggregate functions
  • Time series analysis

Data Visualization Techniques

  • Generating plots with matplotlib
  • Integrating matplotlib within pandas
  • Creating high-quality visualizations
  • Rendering data in Jupyter notebooks
  • Alternative Python visualization libraries for government reporting

Data Vectorization with Numpy

  • Constructing Numpy arrays
  • Standard matrix operations
  • Utilizing universal functions (ufuncs)
  • Array views and broadcasting
  • Performance optimization by eliminating loops
  • Profiling performance using cProfile

Processing Large-Scale Data with Python

  • Developing and maintaining distributed applications in Python for federal systems
  • Data management: Interacting with SQL and NoSQL databases
  • Distributed computing via Hadoop and Spark
  • Application scalability strategies

Interoperability with Other Languages

  • C# integration
  • Java integration
  • C++ integration
  • Perl integration
  • Other language bindings

Multi-Threaded Programming in Python

  • Module management
  • Synchronization techniques
  • Thread prioritization

Data Serialization Standards

  • Serializing Python objects using Pickle

User Interface Development with Python

  • GUI framework options for Python applications
    • Tkinter
    • Pyqt

Maintenance Scripting in Python

  • Proper exception handling and raising protocols
  • Structuring code into modules and packages for government projects
  • Understanding symbol tables and programmatic access
  • Selecting testing frameworks and implementing Test-Driven Development (TDD) in Python

Web Technologies in Python

  • Packages for web service processing
  • Automated web scraping
  • Parsing HTML and XML data structures
  • Automating web form submission

Summary and Future Directions

Requirements

  • Familiarity with programming fundamentals through intermediate proficiency
  • Fundamental understanding of mathematical and statistical principles
  • Comprehension of core database architecture

Target Audience

  • Software engineers and developers
 28 Hours

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