Content
Exploratory thematic and spatial data analysis (with Python), statistical analytics, correlation, data manipulation and cleaning, feature extraction from geographical data, supervised vs. unsupervised learning, linear and polynomial regression, regularized linear models, logistic and multinomial logistic regression, cost functions, model training and fine-tuning, gradient descent, learning curves, performance measures, support vector machines, decision trees, random forests, ensemble learning, dimensionality reduction, segmentation and clustering (k-means, hierarchical clustering, DBSCAN), privacy and ethics in data science, Python numerical, scientific, and machine learning libraries (e.g. NumPy, SciPy, pandas, GeoPandas, scikit-learn).
