Big Data Technologies

2.0 credits · Graduate

This course offers a broad overview of computational techniques and mathematical skills useful for data scientists. Topics include: unix shell, regular expressions, version control: (git), data structures and algorithms, working with databases, data analysis using Python and related libraries (Pandas, NumPy/Scipy, scikit-learn), parallel computing (Map-Reduce, Spark, Hadoop), basic finite-precision arithmetic, an overview of standard machine learning and optimization algorithms, and time-permitting, a guided tour of functional programming. Students are expected to be comfortable in at least one programming language; Python will be used for most of the course