MPCS 53110 Foundations of Computational Data Analysis (Winter 2017)

Section 1
Instructor(s) Brady, Geraldine (gb52)
Location Ryerson 251
Meeting Times Tuesday 5:30pm - 8:30pm
Fulfills Elective Specialization - Data Analytics (DA-1)

Syllabus

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.

Course Prerequisites

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

Other Prerequisites

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

Overlapping Classes

This class is scheduled at a time that conflicts with these other classes:

  • MPCS 51300-1 -- Compilers