Data Science Foundations
Welcome to CDI Data Science Foundations in Python
What You Will Learn
Course Structure
Free Track (Lessons 01–06)
Premium Track (Lessons 07–19)
How to Use This Guide
Lesson Workflow
Data and Project Structure
About This Guide
1
Essential Tools and Basic Requirements
1.1
Microbiome Data Analysis (CDI)
1.2
Why basic requirements matter
1.3
R environment check
1.4
Core packages (introduced gradually)
1.5
Project structure
1.6
Role of QIIME2 in this guide
1.7
Lesson 01 checkpoint
2
Microbiome Data and Metadata Fundamentals
2.1
Learning objectives
2.2
What constitutes microbiome “data”?
2.3
QIIME 2 as the upstream reference
2.3.1
File paths used in this lesson
2.4
Import QIIME 2 artifacts into a phyloseq object
2.5
Sanity checks
2.6
Validate identifiers
2.7
Save an analysis-ready object
2.8
Key takeaways
3
Exploring and Summarizing Microbiome Feature Tables
3.1
Learning objectives
3.2
Real-world dataset:
dietswap
3.3
Inspecting the data object
3.4
Inspect taxonomic labels
3.5
Sequencing depth per sample
3.6
Sparsity of microbiome data
3.7
Dominant taxa
3.8
Raw vs relative abundance
3.9
Saving intermediate data for downstream lessons
3.10
Key takeaways
4
Visualizing Microbiome Composition
4.1
Learning objectives
4.2
Load intermediate data (from Lesson 03)
4.3
Sequencing depth per sample
4.4
Stacked bar plot of top genera
4.5
Heatmap of taxa abundance
4.6
Key takeaways
Completing the Free Track
References
Explore More at Complex Data Insights
Microbiome Data Analysis
References