Basic Python

• Python Fundamentals

  • Introduction to Python

  • Core elements of Python programming

  • Configuring the Python development environment

• Understanding Data Types in Python

  • Numbers: Integer, Float, and Complex types

  • Strings: Character processing in Python

  • Boolean Values: True/False evaluations

  • The None Type: Usage and implications

• Collections in Python

  • Lists: Ordered and mutable collection of items

  • Tuples: Immutable sequences of values

  • Dictionaries: Key-value pairs for efficient data lookup

  • Sets: Unordered collections for unique elements

• Control Flow Mechanisms

  • Conditional statements with if...else

  • Iteration with for...in loops

  • Pythonic data processing with list comprehensions

  • Loop control with while, continue, and break

  • Interfacing with Textual Data

  • Gathering user input

  • String formatting for output

Intermediate Python

• Functionality Enhancement with Functions

  • Crafting user-defined functions

  • Utilizing *args and **kwargs for variable arguments

  • Leveraging built-in function capabilities

  • Streamlining code with lambda expressions

• Debugging and Error Handling

  • Identifying and fixing code errors

  • Managing exceptions for enhanced code reliability

• Managing Text Files

  • Reading from and writing to text files

• Object-Oriented Programming (OOP) Concepts

  • Introduction to classes and objects in Python

  • OOP Concepts and patterns

• The Extensive Standard Library

  • Module importation techniques

  • Working with datetime for time-sensitive operations

Python Tools for Data Analysis

• Delving into Data Analysis with pandas

  • Harnessing the power of NumPy for array operations and broadcasting

  • Creating compelling data visualizations with matplotlib

    • Introduction to matplotlib objects and functionalities

    • Diverse plotting techniques for data representation

    • Advanced visualization with Seaborn enhancements

  • Mastering pandas for sophisticated data handling

    • Exploring Series and Index structures

    • Manipulating DataFrames for complex data operations

    • Efficient data aggregation with GroupBy

    • Generating insightful visualizations within pandas

  • Practical applications and use cases

• Utility Tools for Data Analysts

  • Parsing patterns with regular expressions (re)

  • Working with JSON for web data interchang

  • Establishing connections with APIs and data resources