Python Data Science & Machine Learning

Transform raw data into actionable insights with Python, pandas, scikit-learn, and advanced ML algorithms

🐍 Python 📊 Pandas 🤖 Scikit-learn 📈 Data Visualization

Course Overview

Master the complete data science pipeline from data collection and cleaning to advanced machine learning model deployment. This hands-on course covers everything you need to become a professional data scientist.

Real-world Projects

Work on industry-standard datasets and solve real business problems.

Industry Tools

Master Python, Jupyter, pandas, NumPy, scikit-learn, and TensorFlow.

High Demand

Data Scientists are among the highest-paid professionals with 35% job growth.

Expert Mentorship

Learn from industry experts with years of data science experience.

Course Details
  • Duration: 16 Weeks
  • Format: Online/Hybrid
  • Level: Beginner to Advanced
  • Prerequisites: Basic Programming
  • Projects: 8+ Industry Projects
  • Certification: Industry Recognized
$130K

Average Data Scientist Salary

35%

Job Growth Rate

2.5M

Data Science Jobs by 2025

95%

Job Placement Rate

Technologies You'll Master

Python

Core programming language for data science

Pandas & NumPy

Data manipulation and numerical computing

Matplotlib & Seaborn

Data visualization and statistical plotting

Scikit-learn

Machine learning algorithms and tools

Course Curriculum

Module 1: Python Fundamentals

  • Python Programming Basics
  • Data Structures and Control Flow
  • Object-Oriented Programming
  • Working with Libraries

Module 2: Data Manipulation

  • NumPy for Numerical Computing
  • Pandas for Data Analysis
  • Data Cleaning and Preprocessing
  • Handling Missing Data

Module 3: Data Visualization

  • Matplotlib Fundamentals
  • Seaborn for Statistical Plots
  • Plotly for Interactive Visualizations
  • Dashboard Creation

Module 4: Statistics & Probability

  • Descriptive Statistics
  • Probability Distributions
  • Hypothesis Testing
  • A/B Testing

Module 5: Machine Learning Basics

  • Introduction to ML
  • Supervised vs Unsupervised Learning
  • Model Evaluation Metrics
  • Cross-validation Techniques

Module 6: Supervised Learning

  • Linear and Logistic Regression
  • Decision Trees and Random Forest
  • Support Vector Machines
  • Ensemble Methods

Module 7: Unsupervised Learning

  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis
  • Association Rules

Module 8: Advanced Topics

  • Deep Learning with TensorFlow
  • Natural Language Processing
  • Time Series Analysis
  • Model Deployment

Career Opportunities

Data Scientist

Average Salary: $120K - $160K

Data Analyst

Average Salary: $80K - $120K

ML Engineer

Average Salary: $130K - $180K

Real-world Projects

House Price Prediction

Build a regression model to predict real estate prices using multiple features.

Credit Card Fraud Detection

Develop a classification model to identify fraudulent transactions.

Customer Segmentation

Use clustering algorithms to segment customers for targeted marketing.