Overview of Python

Importance in data and business analytics.

Installation and setup of Python and Jupyter Notebook.

Python Basics

Syntax, indentation, and writing your first Python script.

Data types: integers, floats, strings, booleans.

Variables and type casting.

Lists

Creation, indexing, slicing, and common operations.

List comprehensions for concise operations.

Tuples

Immutable sequences and use cases in analytics.

Dictionaries

Key-value pairs for mapping and lookup operations.

Dictionary comprehensions and nested dictionaries.

Sets

Unique elements and set operations (union, intersection, difference).

Conditional Statements

if, elif, else: Handling decision-making scenarios.

Nested conditions for complex logic.

Loops

for and while loops for repetitive tasks.

Using break, continue, and pass statements.

Practical Examples

Iterating through datasets and extracting information.

Understanding Errors

SyntaxError, TypeError, ValueError, and their causes.

Try-Except Blocks

Handling runtime errors gracefully.

Using finally for cleanup operations.

Raising Exceptions

Custom error messages for debugging.

Functions

Defining and calling functions with def.

Using parameters, arguments, and return statements.

Lambda Functions

Creating anonymous functions for one-time use.

Use cases in data filtering and mapping.

Modules and Packages

Importing built-in modules like math and random.

Creating and using custom modules.

Classes and Objects

Defining classes and creating objects.

Attributes and methods.

OOP Principles

Inheritance, polymorphism, encapsulation, and abstraction.

Practical examples in analytics projects.

Introduction to NumPy

Arrays vs. lists: Benefits and performance.

Creating arrays: zeros, ones, arange, and linspace.

Array Operations

Element-wise operations, indexing, and slicing.

Aggregations: sum, mean, std, and min/max.

Broadcasting and Reshaping

Reshaping arrays for analytical tasks.

Stacking and splitting arrays.