This course offers advanced modeling and analysis techniques, and is intended for students who completed the course on Data Science I.
A distinctive feature of this course is the opportunity to apply data-science skills on a semester long project based on real-world data.
After this course, students will be familiar with the most popular data science and machine learning techniques, ready to apply for a variety
of data-science jobs in industry.
Upon completion of this course, students will be able to: conduct a data analysis project using an analytical programming language (R/Python); visualize
and preprocess raw data in preparation for deeper forms of analysis; train a variety of machine learning models, including Decision Trees, Neural Networks,
and Support Vector Machines; test and fine-tune analytic models to produce high accuracy rates; model evaluation will be achieved using cross-validation,
learning and validation curves, grid search, and different performance metrics; use Ensemble Learning to improve model performance; avoid data overfitting by
working on the Bias-Variance trade off.
For more information visit the course on Blackboard.