Integrating all R skills into a professional project
This is your chance to demonstrate complete mastery of R data science.
PROJECT GOALS:
CAPSTONE WORKFLOW:
WEEK 12 SCHEDULE:
MONDAY-TUESDAY (Days 1-2):
WEDNESDAY-THURSDAY (Days 3-4):
FRIDAY-SATURDAY (Days 5-6):
SUNDAY-MONDAY (Days 7-8):
DELIVERABLES CHECKLIST:
☑ CSV file with processed data ☑ Data dictionary (what each variable means) ☑ Documentation of cleaning decisions
☑ 01_load_explore.R - Data loading and EDA ☑ 02_clean.R - Cleaning code ☑ 03_analysis.R - Statistical analysis ☑ 04_models.R - ML models ☑ 05_report.R - Generate report ☑ README.md - How to run everything
☑ EDA Report (10-15 visualizations) ☑ Statistical Analysis Results ☑ Model Performance Comparisons ☑ Final Insights Document
☑ Executive Summary (1 page) ☑ Problem Statement ☑ Data Description ☑ Methodology ☑ Results and Findings ☑ Conclusions and Recommendations ☑ Appendix (code, tables)
☑ 10-15 minute presentation ☑ Slides with key findings ☑ Clear story arc ☑ Professional appearance ☑ Practice beforehand
PROJECT IDEAS:
Dataset Options:
YOUR REPOSITORY STRUCTURE:
capstone_project/ ├── README.md (Project overview) ├── data/ │ ├── raw/ │ │ └── dataset.csv (Original data) │ └── processed/ │ └── clean_data.csv (Cleaned data) ├── notebooks/ │ ├── 01_exploration.Rmd │ ├── 02_analysis.Rmd │ └── 03_models.Rmd ├── scripts/ │ ├── functions.R (Custom functions) │ └── config.R (Settings) ├── results/ │ ├── figures/ │ │ ├── distribution.png │ │ ├── correlation.png │ │ └── ... │ └── tables/ │ └── model_comparison.csv ├── report/ │ ├── final_report.docx │ ├── presentation.pptx │ └── data_dictionary.csv └── .gitignore BEST PRACTICES: Code Quality: ✓ Use meaningful variable names ✓ Comment complex logic ✓ Follow consistent style ✓ DRY principle (Don't Repeat Yourself) ✓ Use functions for reusable code ✓ Handle errors gracefully Reproducibility: ✓ Set seed: set.seed(42) ✓ Relative paths: "data/raw/file.csv" ✓ Document dependencies: library() calls ✓ Specify versions if needed ✓ No hardcoded file paths ✓ Can run entire analysis fresh Documentation: ✓ README explains purpose ✓ Code comments explain why ✓ Data dictionary explains what ✓ Report explains findings ✓ Presentation tells story GRADING RUBRIC (100 points): Data Preparation (15%): - Cleaning documented - Handling of missing data justified - Appropriate transformations - Final dataset quality Exploratory Analysis (15%): - Thorough visualization - Statistical summaries - Insights documented - Pattern identification Statistical Analysis (15%): - Appropriate tests chosen - Assumptions checked - Results interpreted correctly - Effect sizes reported Machine Learning (20%): - Multiple models compared - Evaluation metrics used - Model selection justified - Interpretable results Code Quality (15%): - Clean, readable code - Well organized - Reproducible - Commented appropriately Report & Presentation (20%): - Professional writing - Clear visualizations - Compelling story - Actionable recommendations - Effective delivery FINAL TIPS: 1. START EARLY - Don't wait until Sunday! 2. EXPLORE THOROUGHLY - You'll find better insights 3. CLEAN CAREFULLY - 70% of time here 4. TRY MULTIPLE MODELS - Compare, don't just use one 5. INTERPRET RESULTS - Numbers mean nothing without context 6. TELL A STORY - Guide reader through your analysis 7. ASK QUESTIONS - What surprises you in the data? 8. VISUALIZE WELL - Graphics communicate better than tables 9. DOCUMENT DECISIONS - Why you chose each approach 10. PRACTICE PRESENTATION - Rehearse beforehand COMMON MISTAKES TO AVOID: ✗ Using test data during training ✗ Ignoring class imbalance ✗ Over-interpreting results ✗ No cross-validation ✗ Forgetting to scale features ✗ Not checking model assumptions ✗ Perfect accuracy (sign of data leakage) ✗ Too much jargon in report ✗ Weak visualizations ✗ No business interpretation WHAT "EXCELLENT" LOOKS LIKE: 1. Insightful findings that surprise/inform 2. Multiple valid approaches compared 3. Clear decision trail documented 4. Professional presentation 5. Reproducible code 6. Actionable recommendations 7. Sophisticated analysis 8. Well-written report 9. Compelling presentation 10. Portfolio-ready quality You're ready! Good luck with your capstone project!