The folders above contain projects completed through the Springboard Data Science Career Track. Each folder contains its own description of the contents as part of a readme file.
Some of the skills displayed in these notebooks include the following:
- Data wrangling pulled from the Quandi API
- Clustering with K-means
- Visualizing clusters using PCA
- Inferential statistics using three approaches: frequentist, bootstrap and bayesian
- Linear regression with the Boston housing dataset
- Exploratory analysis with matplotlib and seaborn
- Classification using scikit-learns LogisticRegression discriminative classifier
- Text classification with a Naive Bayes text classifier
- SQL profficiency with MYSQL
- Tests for statistical significance