Section | 1 |
---|---|

Instructor(s) | Brady, Geraldine (gb52) |

Location | Ryerson 251 |

Meeting Times | Tuesday 5:30pm - 8:30pm |

Fulfills | Elective Specialization - Data Analytics (DA-1) |

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.

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

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

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

- MPCS 51300-1 -- Compilers