This course provides students with deep knowledge of 
sophisticated data science techniques for making sense of data 
across domains. Students are instructed in how to process data 
that is incomplete or missing, how to use hybrid techniques to 
analyze such as semi-supervised learning, and are introduced to 
distributed programming using Hadoop and Spark. Furthermore, 
students are given the opportunity to explore just-in-time 
compilation, both in Python and in the new scientific computing 
language, Julia. The course is appropriate both for students 
preparing for research in data mining and machine learning, as 
well as Bioinformatics, Science and Engineering students who 
want to apply data mining techniques to solve problems in their 
fields of study.

Scientific programming with Python
Review of core statistics and probability
Information theory and uncertainty
Collection and integration of structured versus unstructured data
Decision trees and random forests
Randomized algorithms
Semi-supervised learning and label propagation
Spectral analytics
Out-of-core data processing
Just-in-time compilation and Julia
Data visualization
Introduction to distributed programming