project.qmd

---
title: IDC 6940 - Capstone Projects in Data Science
subtitle: "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.

::: callout-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.

::: callout-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/>
<!-- -   GitHub Page for the project: <https://github.com/acohenstat/STA6257_Project> -->