Works for my Springboard Data Science Career Track, with a concentration with Advanced Machine Learning. The curriculum contains over 500 hours of hands-on materials while working with established industry experts, and the completion of two in-depth capstone projects.
The below list shows the projects I finished during the course, including my two capstone projects: 1) Good Book Classification; and 2) Facial Keypoints Detection Project.
| Chapter | Subject | File |
|---|---|---|
| Data Wrangling | SQL Practice | Link |
| API practice with Quandl API and analyzing financial market data | Link | |
| Statistical Methods for Data Analysis | Frequentist Statistics | Link |
| Hypothesis Testing & Permutation Test | Link | |
| Bayesian Inference | Link | |
| Data Storytelling: World Happiness Report | Link | |
| Machine Learning | Linear Regression using London Housing Data | Link |
| Linear Regression with Wine Data | Link | |
| Logistic Regression Predicting Gender | Link | |
| Decision Trees for Work with a Coffee Producer | Link | |
| COVID-19 with Random Forest | Link | |
| Time Series | Link | |
| Predicting Movie Ratings from Reviews using Naive Bayes | ||
| Customer Segmentation using Clustering | Link | |
| Data Science at Scale | PySpark using DataBricks | Link |
| Take-home Challenges | Ultimate Challenge, end-to-end DS Analysis | Link |
| Relax Challenge, Important factors for prediction | Link | |
| Capstone I | Predicting Good (well-rated) Books using Classification Models | Link |
| Capstone II | Detecting Facial Keypoints in an Image using CNN | Link |