IDC 6940 - Capstone Projects in Data Science

Project Instructions

Please read the following instructions:

You will need to select a specific study area to concentrate on, which may be a subject new to you. You will start the process by conducting an in-depth exploration of the chosen topic and then demonstrate the application of the new methodology using a real-world dataset that is both relevant and captivating. Completing the project requires submitting a comprehensive written paper alongside an engaging oral presentation supported by slides.

Important

GitHub Page Template Project: https://github.com/capstone4ds/capstone4ds_template

The methodologies to consider:

Regression methodologies:

• Quantile Regression
• Generalized Additive Models
• Normal Linear Mixed Models
• Generalized estimating equations
• LASSO or Ridge Regression
• Kernel Regression
• Bayesian Linear Regression
• Beta regression
• Cox proportional hazards model
• …

Clustering methodologies:

• K-means clustering
• Latent Class Analysis
• …

Machine Learning methodologies:

• Support Vector Machines
• Decision Trees /Random Forest
• XGBoost models
• Bayesian Networks
• KNN
• Long Short-Term Network (LSTM)
• Conventional Neural Network (CNN)
• Sentiment Analysis (NLP)
• …

Others:

• Missing Data and Imputation

• Bootstrapping and Jackknife Estimation

• Conformal Predictors*

• …

• indicate state of art in AI and Statistical/Mathematical modeling.

Note

You are free to propose a topic if there is something you are interested in but is missing from the list. The instructor must approve the methodology to ensure that it meets the expectations of a capstone project.

Useful links

• Data Sets: https://acohenstat.github.io/Datasets/