Foundations of Computational Data Analysis

Title Foundations of Computational Data Analysis (53110)
Quarter Winter 2016
Instructor Geraldine Brady (


Foundations of Computational Data Analysis (MPCS 53110) covers mathematical prerequisites for Data Analytics Specialization courses in machine learning, data mining, and large-scale data analysis: i.e., basic statistics and linear algebra, and programming in R.  Topics in statistics include discrete and continuous random variables, discrete and continuous probability distributions, variance, covariance, correlation, sampling and distribution of the mean and standard deviation of a sample, central limit theorem, confidence intervals, maximum likelihood estimators, hypothesis testing, linear and multiple regression.  Topics in linear algebra include Gaussian elimination, matrix transpose and matrix inverse, eigenvectors and eigenvalues, singular value decomposition.

Prerequisites (Courses)

A B or above in MPCS 50103 (Discrete Mathematics) or passing score on mathematics placement exam.

Prerequisites (Other)

Core programming; good command of precalculus (powers, logarithms, and exponentials) and basic univariate calculus (differentiation and integration).


Data Analytics Specialization


Wednesdays 5:30 - 8:30


Ryerson 276