Data Science Using Python - Garima

📘 Python Programming (Beginner to Advanced)

A complete step-by-step learning path.

• Introduction to Python

• Installing Python & IDE Setup

• Variables, Data Types, Operators

• Conditional Statements & Loops

• Functions, Modules & Packages

• File Handling

• Exception Handling

• Object-Oriented Programming (OOP)

• Python Standard Libraries

• Working with Virtual Environments

• Python Coding Best Practices

• Practice Problems & Mini Projects

📙 Python for Data Science

Essential skills for analytics & ML.

• Introduction to Data Science

• Data Collection & Data Cleaning

• Exploratory Data Analysis (EDA)

• Working with CSV, Excel, JSON & APIs

• Handling Missing Values

• Data Transformation Techniques

• Statistical Concepts for Data Science

• Data Wrangling Techniques

• Real Case Studies & Hands-on Assignments

📗 NumPy, Pandas & Matplotlib

Core libraries for real-world data handling.

NumPy

• N-Dimensional Arrays

• Vectorized Operations

• Mathematical & Statistical Functions

• Indexing, Slicing, Reshaping

Pandas

• Series & DataFrames

• Data Merging, Joining & GroupBy

• Data Cleaning & Preprocessing

• Time-Series Data

• Data Import/Export Techniques

Matplotlib

• Line, Bar, Pie, Scatter Plots

• Histograms & Distribution Plots

• Customizing Visualizations

• Multi-Plot Layouts

• Dashboard-Like Visuals

📙 Full Python Course (Mastery Track)

Designed for complete Python development.

• Python Core + Advanced Concepts

• Data Structures & Algorithms (Basics)

• Working with APIs

• Web Scraping

• Automation Scripts

• Basic Flask/Django Introduction (Optional)

• Portfolio Building With Projects