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 |