Data Science - Capstone Project (DSP391m)
Vietnamese title: Dự án KHDL
Credits: 3
Prerequisites: DSR301m, DBM302m, BDI302c

Description:
This capstone project course is designed to consolidate and apply students' knowledge in data science to tackle real-world challenges. The course emphasizes:
- The application of data science techniques to solve practical problems.
- The identification and adaptation of data science methodologies from current research for specific problems.
- The development of a comprehensive solution approach using data science.
- The evaluation and analysis of results from data science projects.
- Recognition of appropriate scenarios for data science applications.
- Selection of suitable metrics for project evaluation.
- Understanding the ethical and legal implications of data handling.
- Implementation and reflection on various data science algorithms including preprocessing, machine learning models, and advanced analytical techniques.
- Effective teamwork and project management.

Learning outcomes:
Identify relevant data science techniques for addressing Capstone Project topics.
Enhance oral presentation skills through participation in Team Meetings and Final Presentations.
Formulate clear problem statements and objectives in project proposals.
Plan and execute data collection and cleaning procedures effectively.
Conduct exploratory data analysis (EDA) to uncover patterns and insights in data.
Develop and deploy machine learning models tailored to project requirements.
Evaluate, refine, and optimize machine learning models for improved performance.
Interpret and visualize results effectively to communicate findings to stakeholders.
Summarize conclusions drawn from data analysis and provide actionable recommendations.
Compile comprehensive final project reports integrating insights and methodologies learned throughout the course.

Student tasks:
- Select a suitable project and form teams.
- Allocate and schedule tasks among team members.
- Develop a project output that meets the specified requirements and demonstrates individual contributions.
- Present and defend the project in front of a review

Tools:
- Programming Languages: Python, R
- Software Tools: Jupyter Notebook, TensorFlow, Scikit-Learn, Pandas
- Databases and Datasets: Kaggle, UCI Machine Learning Repository, Government databases
- Additional Resources: Google Scholar for research papers, GitHub for code repositories

Materials:
description: Data Mining Pipeline
author: University of Colorado Boulder
publisher: Coursera
note: https://www.coursera.org/learn/data-mining-pipeline?specialization=data-mining-foundations-practice
description: Data Visualization
author: University of Illinois at Urbana-Champaign
publisher: Coursera
note: https://www.coursera.org/learn/datavisualization?specialization=data-mining
description: Pattern Discovery in Data Mining
author: University of Illinois at Urbana-Champaign
publisher: Coursera
note: https://www.coursera.org/learn/data-patterns?specialization=data-mining
description: Cluster Analysis in Data Mining
author: University of Illinois at Urbana-Champaign
publisher: Coursera
note: https://www.coursera.org/learn/cluster-analysis?specialization=data-mining
description: Research Paper: Various research papers on machine learning algorithms
note: https://scholar.google.com/
description: Dataset: Publicly available datasets from Kaggle
note: https://www.kaggle.com/

Assessments:
category: Report 1: Project Proposal
type: on-going
weight: 10.0
duration: 225'
question_type: CLO1, CLO2
no_question: Home work, class
knowledge_and_skill: 1
grading_guide: - cover chapter 1,2
note: homework, class, by instructor
category: Report 2: Data Tasks [ Data Collection and Cleaning + Exploratory Data Analysis]
type: on-going
weight: 20.0
duration: 225'
question_type: CLO3, CLO4
no_question: Home work, class
knowledge_and_skill: 1
grading_guide: - cover chapter 3,4
note: homework, class, by instructor
category: Report 3: Model & Results[ Model Development + Model Evaluation and Fine-Tuning + Results Interpretation and Visualization + Conclusion and Recommendations]
type: on-going
weight: 40.0
duration: 225'
question_type: CLO5, CLO6, CLO7, CLO8
no_question: Home work, class
knowledge_and_skill: 1
grading_guide: - cover chapter 5,6,7,8
note: homework, class, by instructor
category: Report 4: Final Project Report
type: on-going
weight: 10.0
duration: 225'
question_type: CLO9
no_question: Home work, class
knowledge_and_skill: 1
grading_guide: - cover chapter 9
note: homework, class, by instructor
category: Final exam[ Oral group presentatation]
type: Final exam
weight: 20.0
duration: 30'
question_type: CLO10
no_question: Oral Group Presentation
knowledge_and_skill: 10
grading_guide: - cover chapter 1, 2, 3,4,5,6,7,8,9,10
note: class, by instructor

Schedule:
session: [Chapter 1: Course Introduction and Group Formation] Introduction to the course
learning_outcomes: TU
materials: mat1
session: Discussion on the Capstone Project (CP) topic
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session: Formation of teams/groups
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session: Discuss and choose a project topic
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session: [Chapter 2: Project Proposal ] Guide Report No.1: Project Proposal
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session: Report No.1: Project Proposal (Discussion)
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session: References related to Report 1
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session: Team Meeting: Project Proposal Writing
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session: Report No.1 Submission: Introduction & Problem Statement
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session: Team Meeting: Instructor's feedbacks for Project Proposal & Group Division
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session: Team Meeting: Project Proposal Improvement
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session: Report No.1 Final Submission
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session: [Chapter 3: Data Collection and Cleaning] Guide Report No.2: Data Tasks [Part 1: Data Collection and Cleaning]
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session: References related to Report No.2: Data Collection and Cleaning Plan
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session: Team Meeting: Data Collection(Present the issues discussed and agreed upon by the group)
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session: Team Meeting: Instructor's feedbacks for Data Collection
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session: Team Meeting: Data Collection Improvement
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session: Team Meeting: Data Cleaning (Present the issues discussed and agreed upon by the group)
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session: Team Meeting: Instructor's feedbacks for Data Cleaning
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session: Team Meeting: Data Cleaning Improvement
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session: [Chapter 4: Exploratory Data Analysis ] Guide Report No.2: Data Tasks [Part 2: Exploratory Data Analysis]
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session: References related to Report No.2: Exploratory Data Analysis
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session: Team Meeting: Exploratory Data Analysis (Present the issues discussed and agreed upon by the group)
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session: Team Meeting: Instructor's feedback for Exploratory Data Analysis
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session: Team Meeting: Exploratory Data Analysis Improvement
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session: Report No.2 Submission: Data Tasks
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session: Team Meeting: Instructor's feedbacks for Data Tasks all phase
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session: Team Meeting: Data Tasks Improvement
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session: [Chapter 5: Model Development] Guide Report No.3: Model & Result [ Part 1: Model Development]
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session: References related to Report No.3: Model Development
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session: Team Meeting: Model Development (Present the issues discussed and agreed upon by the group)
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session: Team Meeting: Instructor's feedback for Model Development
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session: Team Meeting: Model Development Improvement
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session: [Chapter 6:Model Evaluation and Fine-Tuning] Guide Report No.3: Model & Result [ Part 2: Model Evaluation and Fine-Tuning]
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session: References related to Report No.3: Model Evaluation and Fine-Tuning
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session: Team Meeting: Model Evaluation and Fine-Tuning (Present the issues discussed and agreed upon by the group)
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session: Team Meeting: Instructor's feedbacks for Model Evaluation and Fine-Tuning
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session: Team Meeting: Model Evaluation and Fine-Tuning Improvement
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session: [Chapter 7: Results Interpretation and Visualization
]Guide Report No.3: Model & Result [ Part 3: Results Interpretation and Visualization]
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session: References related to Report No.3: Results Interpretation and Visualization
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session: Team Meeting: Results Interpretation and Visualization (Present the issues discussed and agreed upon by the group)
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session: Team Meeting: Instructor's feedbacks for Results Interpretation and Visualization
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session: Team Meeting: Results Interpretation and Visualization Improvement
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session: [Chapter 8: Conclusion and Recommendations
]Guide Report No.3: Model & Result [ Part 4: Conclusion and Recommendations]
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session: References related to Report No.3: Conclusion and Recommendations
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session: Team Meeting: Conclusion and Recommendations (Present the issues discussed and agreed upon by the group)
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session: Team Meeting: Instructor's feedbacks for Conclusion and Recommendations
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session: Team Meeting: Conclusion and Recommendations Improvement
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session: Report No.3 Submission: Model & Results
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session: Team Meeting: Instructor's feedbacks for Model & Results all phase
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session: Team Meeting: Model & Results Improvement
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session: [Chapter 9: Finalization and Presentations] Guide Final Report
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session: References related to Final Report
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session: Team Meeting: Final Report (Present the issues discussed and agreed upon by the group)
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session: Team Meeting: Instructor's discussion for Final Report
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session: Team Meeting: Final Report Improvement
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session: Final Report Submission
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session: Team Meeting: Practice for Presentation of Capstone Project
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session: Team Meeting: Practice for Presentation of Capstone Project
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session: Team Meeting: Practice for Presentation of Capstone Project
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