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Lab 2: Scraping, Pandas, Python, and viz

Lecture 4: Pandas, SQL, and the Grammar of Data

Lecture 5: Statistical Models

Lab 3: Probability, Distributions, and Frequentist Statistics

Lecture 6: Story Telling and Effective Communication

Lecture 7: Bias and Regression

Lab 4: Regression, Logistic Regression: in sklearn and statsmodels

Lecture 8: More Regression

Lecture 9: Classification. kNN. Cross Validation. Dimensionality Reduction. PCA. MDS.

Lab 5: Machine Learning

Lecture 10: SVM, Evaluation.

Lecture 11: Decision Trees and Random Forests

Lab 6: Machine Learning 2

Lecture 12: Ensemble Methods.

Lecture 13: Best Practices

Lab 7: Ensembles

Lecture 14: Best Practices, Recommendations and MapReduce

Lecture 15: MapReduce Combiners and Spark

Lab 8: Vagrant and VirtualBox, AWS, and Spark

Lecture 16: Bayes Theorem and Bayesian Methods

Lecture 17: Bayesian Methods Continued

Lab 9: Bayes

Lecture 18: Bayesian Methods Continued,Text Data

Lecture BONUS: Interactive Visualization

Lab 10: Text and Clustering

Lecture 19: Clustering

Lecture 20: Effective Presentations

Lab 10: Projects, and an example

Lecture 21: Experimental Design

Lecture 22: Deep Networks

Lecture 23: Guest Lecture: Building Data Science

Lecture 24: Wrapup, and where to go from here.