MPCS 53110 Foundations of Computational Data Analysis (Winter 2019)

Section 1
Instructor(s) Chaudhary, Amitabh (amitabh)
Location Ryerson 251
Meeting Times Saturday 10am - 1pm
Fulfills Elective Specialization - Data Analytics (DA-1)

Syllabus

Foundations of Computational Data Analysis covers mathematical prerequisites for the Data Analytics Specialization courses in machine learning, and large-scale data analytics (MPCS 53111 and 53112):  basic statistics and linear algebra.  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, and hypothesis testing.  Topics in linear algebra include Gaussian elimination, matrix transpose and matrix inverse, eigenvectors and eigenvalues, and singular value decompositions.  The languages Python and R will be used for implementation, analysis, and visualization.

Course Prerequisites

MPCS 50101 Math for Computer Science: Discrete Mathematics
MPCS 55001 Algorithms (completed or taking concurrently)
MPCS 51042 Python Programming (or Programming core requirement with prior knowledge of Python)

In all the above courses a grade of B+ or above is required. Please contact the instructor if you have, instead, equivalent courses or experience, or meet most but not all of the requirements.

Other Prerequisites

Core Programming; Univariate Calculus and Basic Multivariate Calculus (double integrals, partial derivatives).

Overlapping Classes

This class is scheduled at a time that does not conflict with any other classes this quarter.