Python Programming and its applications in Data Science (In Hindi)

Week 1: Introduction to Python Programming

• Overview of Python, Installation, and IDEs

• Data Types

• Input and Output

• Operators

• Control Statements

• Functions

• Modules

• Arrays

• Strings

Week 2: Built-in Data Structures

• Lists

• Tuples

• Dictionaries

• Sets

Week 3: File Handling

• Text and binary files

• Error handling and exceptions

• Working with CSV and JSON formats

Week 4: Introduction to Data Science and Python Libraries for Data Science

• Data Science essentials

• NumPy – arrays, indexing, operations

• Pandas – Series, DataFrames

Week 5: Data Acquisition and Cleaning

• Data Collection and Integration

• Handling Missing Values

• Detecting and treating duplicates

• Dealing with inconsistent data (case sensitivity, formatting issues, whitespace)

Week 6: Data Transformation

• Feature scaling (normalization, standardization)

• Feature Selection

• Feature encoding (label encoding, one-hot encoding)

• Binning / discretization of continuous variables

Week 7: Outlier Detection & Treatment

• Identifying outliers using statistical methods (Z-score, IQR)

• Visual detection of outliers (boxplots, scatterplots)

• Handling/removing outliers

• Handling Imbalanced Data

Week 8: Data Visualisation and Exploratory Data Analysis

• Visualisation with Matplotlib, Seaborn

• Plotting techniques: line, bar, histogram, scatter plot, heatmap

• Exploratory Data Analysis (EDA) – descriptive statistics, correlation