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Course Outline

Introduction

  • Python versatility: spanning from data analysis to web crawling

Python Data Structures and Operations

  • Integers and floating-point numbers
  • Strings and byte objects
  • Tuples and lists
  • Dictionaries and ordered dictionaries
  • Sets and frozensets
  • DataFrames (using pandas)
  • Data type conversions

Object-Oriented Programming with Python

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

Data Analysis with Pandas

  • Data cleaning techniques
  • Utilizing vectorized data in pandas
  • Data wrangling
  • Sorting and filtering datasets
  • Performing aggregate operations
  • Analyzing time series data

Data Visualization

  • Creating plots with matplotlib
  • Integrating matplotlib within pandas
  • Generating high-quality visualizations
  • Visualizing data in Jupyter notebooks
  • Exploring other Python visualization libraries

Vectorizing Data with NumPy

  • Creating NumPy arrays
  • Executing common matrix operations
  • Utilizing universal functions (ufuncs)
  • Working with views and broadcasting in NumPy arrays
  • Optimizing performance by eliminating loops
  • Performance optimization using cProfile

Processing Big Data with Python

  • Developing and supporting distributed applications using Python
  • Data storage strategies: Working with SQL and NoSQL databases
  • Distributed processing frameworks: Hadoop and Spark
  • Scaling applications for high performance

Extending Python with Other Languages (and Vice Versa)

  • C#
  • Java
  • C++
  • Perl
  • Other integrations

Multi-Threaded Programming in Python

  • Working with modules
  • Synchronization techniques
  • Thread prioritization

Data Serialization

  • Serializing Python objects using Pickle

UI Programming with Python

  • GUI framework options for Python
    • Tkinter
    • PyQt

Maintenance Scripting in Python

  • Properly raising and handling exceptions
  • Structuring code into modules and packages
  • Understanding and accessing symbol tables within code
  • Selecting a testing framework and applying Test-Driven Development (TDD) in Python

Python for Web Development

  • Web processing packages
  • Web crawling techniques
  • Parsing HTML and XML documents
  • Automating web form submissions

Summary and Next Steps

Requirements

  • Beginner to intermediate programming experience
  • Fundamental knowledge of mathematics and statistics
  • Understanding of database concepts

Target Audience

  • Software Developers
 28 Hours

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