Python Programming Course: Basics to Advanced

Overview of Python: history, features, and use cases.

Installing Python and setting up the development environment.

Basic syntax: variables, data types, and simple operations.

Hands-on: Writing a simple Python program to understand basic concepts.

Introduction to sequences: lists, tuples, sets, and dictionaries

File handling: reading and writing files in Python

Intro to Mathematical Concepts: Basic probability—how to generate random numbers and use them to simulate random events

Hands-on: A program simulating coin tosses or dice rolls to explore simple probability

Defining and using functions: arguments, return values, and scope

Object-oriented concepts: classes, objects, inheritance, and polymorphism

Hands-on: Writing classes to represent basic statistical concepts (e.g., creating a class for a random variable or distributions)

Importing and using Python modules

Exception handling: try, except, and finally blocks

Hands-on: Use Python’s built-in statistics module for basic statistical operations (mean, median, variance)

Introduction to NumPy and its applications

Creating and manipulating arrays

Mathematical Concepts: Introduction to descriptive statistics—mean, median, mode, variance, and standard deviation using NumPy

Probability distributions (e.g., normal distribution) using NumPy’s random module

Hands-on: Use NumPy to calculate statistical measures from data arrays, simulate random data based on distributions

Basic Pandas operations: importing/exporting data, Series, and DataFrames

Cleaning and transforming data

Mathematical Concepts: Exploring data distributions (frequency distribution, data dispersion)

Hands-on: Using Pandas to compute statistics (mean, standard deviation) from real datasets

Merging, joining, and reshaping data

GroupBy, pivot tables, and handling time-series data

Mathematical Concepts: Correlation and covariance between datasets

Hands-on: Use Pandas to explore relationships between datasets using correlation and apply statistics to grouped data

Basic visualization: line plots, bar charts, and scatter plots

Mathematical Concepts: Visualizing statistical distributions (histograms, box plots) and relationships between variables (scatter plots)

Hands-on: Plotting histograms, scatter plots, and box plots to visualize data distributions and compare variables

Creating subplots, multiple axes, and advanced customizations in Matplotlib

Statistical visualizations in Seaborn (e.g., pair plots, heatmaps)

Mathematical Concepts: Visualizing probability distributions, regression analysis, and statistical relationships between variables (pair plots, correlation heatmaps)

Hands-on: Plotting probability density functions and visualizing relationships between multiple variables using Seaborn

Introduction to web scraping: HTML parsing and scraping static pages with BeautifulSoup

Using Selenium for scraping dynamic websites

Hands-on: Scraping data from websites and performing simple data analysis/statistical insights from the scraped data