Advanced Python Data Science Testing Distribution Lo094032 Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Data Exploration & Preprocessing
Estimated time: 3 hours
- Review of tools and frameworks commonly used in practice
- Discussion of best practices and industry standards
- Guided project work with instructor feedback
Module 2: Statistical Analysis & Probability
Estimated time: 3-4 hours
- Review of tools and frameworks commonly used in practice
- Discussion of best practices and industry standards
- Assessment: Quiz and peer-reviewed assignment
Module 3: Machine Learning Fundamentals
Estimated time: 1-2 hours
- Introduction to key concepts in machine learning fundamentals
- Discussion of best practices and industry standards
- Case study analysis with real-world examples
Module 4: Model Evaluation & Optimization
Estimated time: 2 hours
- Hands-on exercises applying model evaluation & optimization techniques
- Discussion of best practices and industry standards
- Review of tools and frameworks commonly used in practice
Module 5: Data Visualization & Storytelling
Estimated time: 2-3 hours
- Introduction to key concepts in data visualization & storytelling
- Hands-on exercises applying data visualization & storytelling techniques
- Review of tools and frameworks commonly used in practice
- Assessment: Quiz and peer-reviewed assignment
Module 6: Advanced Analytics & Feature Engineering
Estimated time: 4 hours
- Interactive lab: Building practical solutions
- Assessment: Quiz and peer-reviewed assignment
- Discussion of best practices and industry standards
Prerequisites
- Strong foundation in Python programming
- Experience with data science concepts and workflows
- Familiarity with machine learning and data analysis libraries
What You'll Be Able to Do After
- Design end-to-end data science pipelines for production environments
- Implement data preprocessing and feature engineering techniques
- Create data visualizations that communicate findings effectively
- Master exploratory data analysis workflows and best practices
- Work with large-scale datasets using industry-standard tools