Capstone Project Presentations

CMSC 178DA - Week 12, Session 1

Department of Computer Science
University of the Philippines Cebu

Presentation Guidelines

Format

                            15 minutes presentation
5 minutes Q&A
All team members participate

                        

Evaluation

Problem definition: 15%
Methodology: 25%
Analysis quality: 30%
Visualization: 15%
Communication: 15%

Presentation Structure

                    Introduction (2 min)
                            Problem statement
Why it matters

Data & Methodology (4 min)
                            Data sources and preprocessing
Methods used

Results (5 min)
                            Key findings and visualizations
Model performance

Insights & Recommendations (3 min)
                            Actionable insights and limitations

Q&A (5 min)

Evaluation Rubric

Criterion	Excellent (4)	Good (3)	Satisfactory (2)	Needs Work (1)
Problem	Clear, relevant, well-scoped	Clear but could be better scoped	Somewhat unclear	Poorly defined
Methods	Appropriate, well-executed	Appropriate with minor issues	Some issues	Inappropriate
Analysis	Deep insights, rigorous	Good insights	Surface-level	Missing insights
Visuals	Professional, clear	Good but minor issues	Basic	Poor/missing
Communication	Engaging, clear	Clear but could engage more	Some confusion	Hard to follow

Team 1 Presentation

15 minutes + 5 minutes Q&A

Team 2 Presentation

15 minutes + 5 minutes Q&A

Team 3 Presentation

15 minutes + 5 minutes Q&A

Team 4 Presentation

15 minutes + 5 minutes Q&A

Peer Feedback Form

After each presentation, please provide:

What was the strongest aspect?
What could be improved?
One question you still have
Rating: 1-5 stars

Constructive feedback helps everyone grow!

Next Session Preview

                    Lecture 24: Remaining Presentations & Course Wrap-up
                    Final capstone presentations
Course summary and key takeaways
Career guidance in analytics
Q&A and course evaluation

                

Course Wrap-up & Future Directions

CMSC 178DA - Week 12, Session 2

Department of Computer Science
University of the Philippines Cebu

Team 5 Presentation

15 minutes + 5 minutes Q&A

Team 6 Presentation

15 minutes + 5 minutes Q&A

Team 7 Presentation

15 minutes + 5 minutes Q&A

Course Summary

What We Covered

Week	Topics
1-2	Foundations, Probability, Statistics
3-4	Data Wrangling, EDA
5-6	Visualization, Storytelling, Dashboards
7-8	Regression, Classification, Ensembles
9-10	Clustering, Time Series
11-12	Text Analytics, Ethics, Capstone

Key Skills Acquired

Technical Skills

                            Python for data analytics
SQL for data extraction
Statistical inference
Machine learning fundamentals
Data visualization

                        

Soft Skills

Data storytelling
Business communication
Problem framing
Ethical reasoning

Analytics Career Paths

Data Analyst → Senior Analyst → Lead Analyst → Analytics Manager
     ↓
Data Scientist → Senior DS → Principal DS → Chief Data Officer
     ↓
ML Engineer → Senior MLE → Staff MLE → VP of Engineering
     ↓
Analytics Consultant → Manager → Partner

Philippine Job Market

🇵🇭 Growing Sectors in the Philippines

Industries

Fintech (GCash, Maya, banks)
E-commerce (Lazada, Shopee)
BPO (analytics services)
Startups (various sectors)

Entry-Level Salaries

Data Analyst:
₱30,000-60,000/month
Data Scientist:
₱50,000-100,000/month

Building Your Portfolio

Essential Components

GitHub with clean code and documentation
3-5 completed projects with write-ups
LinkedIn with skills and endorsements
Kaggle profile with competitions/notebooks
Personal website/blog (optional but helpful)

Your capstone project is a great portfolio piece!

Continuous Learning

Free Resources

Kaggle Learn
Google Data Analytics Certificate
Harvard CS109 (online materials)

Advanced Topics

                            Deep learning (fast.ai, Coursera)
MLOps and deployment
Cloud certifications (AWS, GCP)
Domain specialization

                        

Emerging Trends in Analytics

                    What's Next
                    GenAI for analytics: Copilots, code generation
AutoML: Automated model building
Real-time analytics: Streaming data
Edge analytics: IoT and mobile
Responsible AI: Fairness, privacy by design

                

Final Words

To Succeed in Analytics

Stay curious - always ask "why?"
Practice constantly - do projects
Communicate clearly - insights must be shared
Think ethically - data affects real people
Never stop learning - field evolves rapidly

"The goal is to turn data into information, and information into insight."
— Carly Fiorina

Course Evaluation

Please complete the course evaluation:

What worked well?
What could be improved?
Suggestions for future offerings

Your feedback helps improve the course for future students!

Thank You!

CMSC 178DA: Data Analytics

University of the Philippines Cebu
Semester [X], AY 2025-2026

Keep in touch:

Email: [instructor email]
LinkedIn: [profile]

Best wishes on your analytics journey!