Complete Python for Data Science and Cloud Computing Course Syllabus

Overview: This course provides a comprehensive introduction to Python programming, data science, and cloud computing fundamentals, designed for beginners aiming to enter modern tech fields. The curriculum blends theoretical knowledge with hands-on practice, covering data exploration, statistical analysis, machine learning, model optimization, visualization, and advanced analytics. With a total time commitment of approximately 14–20 hours, learners engage in interactive labs, real-world case studies, quizzes, and project-based assessments to build practical skills applicable to cloud-based data projects.

Module 1: Data Exploration & Preprocessing

Estimated time: 2.5 hours

• Introduction to key concepts in data exploration & preprocessing
• Review of tools and frameworks commonly used in practice
• Discussion of best practices and industry standards
• Assessment: Quiz and peer-reviewed assignment

Module 2: Statistical Analysis & Probability

Estimated time: 2 hours

• Review of tools and frameworks commonly used in practice
• Case study analysis with real-world examples
• Discussion of best practices and industry standards
• Guided project work with instructor feedback

Module 3: Machine Learning Fundamentals

Estimated time: 3 hours

• Hands-on exercises applying machine learning fundamentals techniques
• Interactive lab: Building practical solutions
• Case study analysis with real-world examples
• Assessment: Quiz and peer-reviewed assignment

Module 4: Model Evaluation & Optimization

Estimated time: 1.5 hours

• Hands-on exercises applying model evaluation & optimization techniques
• Case study analysis with real-world examples
• Guided project work with instructor feedback

Module 5: Data Visualization & Storytelling

Estimated time: 4 hours

• Introduction to key concepts in data visualization & storytelling
• Review of tools and frameworks commonly used in practice
• Interactive lab: Building practical solutions
• Case study analysis with real-world examples

Module 6: Advanced Analytics & Feature Engineering

Estimated time: 3.5 hours

• Hands-on exercises applying advanced analytics & feature engineering techniques
• Review of tools and frameworks commonly used in practice
• Discussion of best practices and industry standards
• Interactive lab: Building practical solutions

Prerequisites

• Basic computer literacy
• No prior programming experience required
• Access to a computer with internet connection

What You'll Be Able to Do After

• Apply statistical methods to extract insights from complex data
• Implement data preprocessing and feature engineering techniques
• Create data visualizations that communicate findings effectively
• Work with large-scale datasets using industry-standard tools
• Design end-to-end data science pipelines for production environments