# Data Science & AI Masters 2026 - From Python To Gen AI Course Syllabus — Curriculum & Modules | Course

> Detailed syllabus and module breakdown for Data Science & AI Masters 2026 - From Python To Gen AI Course. See what you'll learn, estimated hours per module, prerequisites, and outcomes.

# Data Science & AI Masters 2026 - From Python To Gen AI Course Syllabus

Full curriculum breakdown — modules, lessons, estimated time, and outcomes.

Overview: This comprehensive course guides learners from foundational Python programming to advanced AI and generative AI concepts, blending theory with hands-on practice. The curriculum spans six core modules, totaling approximately 16–21 hours of content, featuring quizzes, labs, peer-reviewed assignments, and real-world projects. Designed for intermediate learners, it emphasizes practical skills in data science, machine learning, and modern AI technologies, preparing students for real-world applications in high-demand fields.

### Module 1: Data Exploration & Preprocessing

Estimated time: 2 hours

- Case study analysis with real-world examples

- Discussion of best practices and industry standards

- Review of tools and frameworks commonly used in practice

- Hands-on data cleaning and transformation exercises

### Module 2: Statistical Analysis & Probability

Estimated time: 4 hours

- Introduction to key concepts in statistical analysis & probability

- Interactive lab: Building practical solutions

- Case study analysis with real-world examples

- Application of statistical methods to extract insights

### Module 3: Machine Learning Fundamentals

Estimated time: 3 hours

- Hands-on exercises applying machine learning fundamentals

- Guided project work with instructor feedback

- Understanding supervised and unsupervised learning algorithms

- Building and evaluating models on real-world datasets

### Module 4: Model Evaluation & Optimization

Estimated time: 3 hours

- Introduction to key concepts in model evaluation & optimization

- Case study analysis with real-world examples

- Discussion of best practices and industry standards

- Techniques for improving model performance and generalization

### Module 5: Data Visualization & Storytelling

Estimated time: 4 hours

- Interactive lab: Building practical solutions

- Review of tools and frameworks commonly used in practice

- Creating compelling data visualizations

- Communicating insights through data storytelling

### Module 6: Advanced Analytics & Feature Engineering

Estimated time: 2 hours

- Introduction to key concepts in advanced analytics & feature engineering

- Hands-on exercises applying advanced analytics & feature engineering techniques

- Guided project work with instructor feedback

- Implementing feature selection and transformation pipelines

## Prerequisites

- Basic understanding of programming concepts

- Familiarity with Python fundamentals

- Interest in data science and AI applications

## What You'll Be Able to Do After

- Work with large-scale datasets using industry-standard tools

- Design end-to-end data science pipelines for production environments

- Build and evaluate machine learning models using real-world datasets

- Apply statistical methods and feature engineering to complex data

- Implement data visualization and storytelling techniques effectively

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