Data Science II



Chris Brinton, Asst. Professor Electrical & Computer Engineering

Course Outcome

Equipped with an introductory understanding of Python and data science topics from Data Science I, Data Science II will expose learners to more advanced models, techniques, and other important big data topics. Common machine learning models will be covered comprehensively from their mathematical formulation (e.g., least-squares equations for regression) and model training (e.g., stochastic gradient descent) to their practical implementation (e.g., regularized regression in Python) and evaluation on real datasets. More advanced Python topics (e.g., objects and classes) will also be covered for learners to understand how to build reusable modules implementing custom data science methods.


Introduction to Data Science; Python

Course Topics

Regression: Least squares equations, model training Python classes and objects Classification: Support vector machines, model training Natural language processing: tf-idf   
Cross-validation techniques Python iterators and generators Evaluation metrics Neural networks: Basic architectures, model training
Regression: Regularization, preventing overfitting Classification: Logistic regression, model training Python regular expressions

Deep learning: Recurrent neural networks, convolutional neural networks 

CEUs: 1.5

For further information and to register, please email