MPCS 53110 Foundations of Computational Data Analysis (Winter 2023)

Section 1
Location None
Meeting Times
Fulfills Elective Specialization - Data Analytics (DA-1)


*Please note: This is the syllabus from the 2021/22 academic year and subject to change.*

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.  In some of the exercises we'll  use Python to compute and/or visualize data.

Course Prerequisites

B+ or above in MPCS 51042 Python Programming, B+ or better in any other Core Programming class with prior knowledge of Python, or Core Programming waiver.
B or above in MPCS 55001 Algorithms
*If you are a student who is in MPCS 50103 Discrete Math this Autumn quarter and you would like to take Foundations along with Algorithms in the Winter quarter, we require that you earn at least a B+ in Discrete Math. The course load for both of these classes is extremely high and taking them together will be very challenging.

Other Prerequisites

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

This course requires competency in Unix and Linux. Please plan to attend the MPCS Unix Bootcamp ( or take the online MPCS Unix Bootcamp Course on Canvas.

Overlapping Classes

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

Eligible Programs

Masters Program in Computer Science Bx/MS in Computer Science (Option 3: Profesionally-oriented - Non-CS Majors)