Python Data Science

Duration: 4 months

Module 1: Python Prerequisite

  • Variables & Data Types

  • Lists, Tuples, Sets, Dictionaries

  • Conditional Statements

  • Loops

  • Functions (Lambda, Map, Filter)

  • File Handling

  • Exception Handling

  • Object-Oriented Programming Basics

Module 2: NumPy (Numerical Computing)

  • Introduction to NumPy

  • Creating Arrays

  • Array Indexing & Slicing

  • Reshaping Arrays

  • Mathematical Operations

  • Broadcasting

  • Aggregation Functions (sum, mean, std)

  • Random Module

  • Linear Algebra Basics


Module 3: SQL for Data Science

  • DBMS & RDBMS concepts

  • SELECT, WHERE

  • GROUP BY, HAVING

  • Aggregations

  • JOINS

  • Subqueries

Module 4: Pandas (Data Analysis)

  • Introduction to Pandas

  • Series

  • Data Frames

  • Reading CSV, Excel Files

  • Data Inspection (head, tail, info, describe)

  • Handling Missing Values

  • Filtering & Sorting

  • GroupBy Operations

  • Merging & Joining

  • Pivot Tables

  • Date & Time Handling

  • Data Cleaning Techniques


Module 5: Data Visualization

1. Matplotlib

  • Line Chart

  • Bar Chart

  • Pie Chart

  • Histogram

  • Subplots

  • Customization

2. Seaborn

  • Statistical Plots

  • Heatmaps

  • Boxplots

  • Pair plot

  • Correlation Matrix


Module 6: Statistics for Data Science

  • Mean, Median, Mode

  • Variance & Standard Deviation

  • Probability Basics

  • Normal Distribution

  • Z-Score

  • Correlation & Covariance

  • Hypothesis Testing

  • P-Value Concept


Module 7: Data Preprocessing

  • Feature Selection

  • Encoding (Label & One Hot Encoding)

  • Feature Scaling (Standardization, Normalization)

  • Train-Test Split

  • Handling Outliers


Module 8: Machine Learning (Scikit-Learn)

  • Supervised Learning

    • Linear Regression

    • Multiple Linear Regression

    • Logistic Regression

    • KNN

    • Decision Tree

    • Random Forest

    • Support Vector Machine

  • Unsupervised Learning

    • K-Means Clustering

    • Hierarchical Clustering

    • PCA (Dimensionality Reduction)

Module 9: Model Evaluation
  • Confusion Matrix

  • Accuracy, Precision, Recall

  • ROC Curve

  • R² Score

  • Cross Validation

Module 10: Real-Time Projects

  • Sales Data Analysis

  • IPL Data Analysis

  • Movie Recommendation System

  • Customer Segmentation

  • Loan Prediction

  • Power BI + Python Integration

Module 11: Deployment

  • Saving Models (Pickle)

  • Creating Simple Web App using Flask

  • Deploying on Render/Hostinger

  • Connecting Model with Frontend